Datasets:

susnato

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like 0

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/feature_extraction.py

from typing import Dict from .base import GenericTensor, Pipeline # Can't use @add_end_docstrings(PIPELINE_INIT_ARGS) here because this one does not accept `binary_output` class FeatureExtractionPipeline(Pipeline): """ Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base transformer, which can be used as features in downstream tasks. Example: ```python >>> from transformers import pipeline >>> extractor = pipeline(model="bert-base-uncased", task="feature-extraction") >>> result = extractor("This is a simple test.", return_tensors=True) >>> result.shape # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input string. torch.Size([1, 8, 768]) ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier: `"feature-extraction"`. All models may be used for this pipeline. See a list of all models, including community-contributed models on [huggingface.co/models](https://huggingface.co/models). Arguments: model ([`PreTrainedModel`] or [`TFPreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow. tokenizer ([`PreTrainedTokenizer`]): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`]. modelcard (`str` or [`ModelCard`], *optional*): Model card attributed to the model for this pipeline. framework (`str`, *optional*): The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be installed. If no framework is specified, will default to the one currently installed. If no framework is specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is provided. return_tensor (`bool`, *optional*): If `True`, returns a tensor according to the specified framework, otherwise returns a list. task (`str`, defaults to `""`): A task-identifier for the pipeline. args_parser ([`~pipelines.ArgumentHandler`], *optional*): Reference to the object in charge of parsing supplied pipeline parameters. device (`int`, *optional*, defaults to -1): Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on the associated CUDA device id. """ def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs): if tokenize_kwargs is None: tokenize_kwargs = {} if truncation is not None: if "truncation" in tokenize_kwargs: raise ValueError( "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)" ) tokenize_kwargs["truncation"] = truncation preprocess_params = tokenize_kwargs postprocess_params = {} if return_tensors is not None: postprocess_params["return_tensors"] = return_tensors return preprocess_params, {}, postprocess_params def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]: return_tensors = self.framework model_inputs = self.tokenizer(inputs, return_tensors=return_tensors, **tokenize_kwargs) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs, return_tensors=False): # [0] is the first available tensor, logits or last_hidden_state. if return_tensors: return model_outputs[0] if self.framework == "pt": return model_outputs[0].tolist() elif self.framework == "tf": return model_outputs[0].numpy().tolist() def __call__(self, *args, **kwargs): """ Extract the features of the input(s). Args: args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of. Return: A nested list of `float`: The features computed by the model. """ return super().__call__(*args, **kwargs)

from typing import Dict from .base import GenericTensor, Pipeline # Can't use @add_end_docstrings(PIPELINE_INIT_ARGS) here because this one does not accept `binary_output` class FeatureExtractionPipeline(Pipeline): """ Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base transformer, which can be used as features in downstream tasks. Example: ```python >>> from transformers import pipeline >>> extractor = pipeline(model="bert-base-uncased", task="feature-extraction") >>> result = extractor("This is a simple test.", return_tensors=True) >>> result.shape # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input string. torch.Size([1, 8, 768]) ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier: `"feature-extraction"`. All models may be used for this pipeline. See a list of all models, including community-contributed models on [huggingface.co/models](https://huggingface.co/models). Arguments: model ([`PreTrainedModel`] or [`TFPreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow. tokenizer ([`PreTrainedTokenizer`]): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`]. modelcard (`str` or [`ModelCard`], *optional*): Model card attributed to the model for this pipeline. framework (`str`, *optional*): The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be installed. If no framework is specified, will default to the one currently installed. If no framework is specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is provided. return_tensor (`bool`, *optional*): If `True`, returns a tensor according to the specified framework, otherwise returns a list. task (`str`, defaults to `""`): A task-identifier for the pipeline. args_parser ([`~pipelines.ArgumentHandler`], *optional*): Reference to the object in charge of parsing supplied pipeline parameters. device (`int`, *optional*, defaults to -1): Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on the associated CUDA device id. """ def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs): if tokenize_kwargs is None: tokenize_kwargs = {} if truncation is not None: if "truncation" in tokenize_kwargs: raise ValueError( "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)" ) tokenize_kwargs["truncation"] = truncation preprocess_params = tokenize_kwargs postprocess_params = {} if return_tensors is not None: postprocess_params["return_tensors"] = return_tensors return preprocess_params, {}, postprocess_params def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]: return_tensors = self.framework model_inputs = self.tokenizer(inputs, return_tensors=return_tensors, **tokenize_kwargs) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs, return_tensors=False): # [0] is the first available tensor, logits or last_hidden_state. if return_tensors: return model_outputs[0] if self.framework == "pt": return model_outputs[0].tolist() elif self.framework == "tf": return model_outputs[0].numpy().tolist() def __call__(self, *args, **kwargs): """ Extract the features of the input(s). Args: args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of. Return: A nested list of `float`: The features computed by the model. """ return super().__call__(*args, **kwargs)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/pipelines/fill_mask.py

from typing import Dict import numpy as np from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException if is_tf_available(): import tensorflow as tf from ..tf_utils import stable_softmax if is_torch_available(): import torch logger = logging.get_logger(__name__) @add_end_docstrings( PIPELINE_INIT_ARGS, r""" top_k (`int`, defaults to 5): The number of predictions to return. targets (`str` or `List[str]`, *optional*): When passed, the model will limit the scores to the passed targets instead of looking up in the whole vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting token will be used (with a warning, and that might be slower). """, ) class FillMaskPipeline(Pipeline): """ Masked language modeling prediction pipeline using any `ModelWithLMHead`. See the [masked language modeling examples](../task_summary#masked-language-modeling) for more information. Example: ```python >>> from transformers import pipeline >>> fill_masker = pipeline(model="bert-base-uncased") >>> potential_words = fill_masker("This is a simple [MASK].") >>> from transformers.testing_utils import nested_simplify >>> nested_simplify(potential_words) # The scores might vary slightly across pytorch/tensorflow versions. [{'score': 0.042, 'token': 3291, 'token_str': 'problem', 'sequence': 'this is a simple problem.'}, {'score': 0.031, 'token': 3160, 'token_str': 'question', 'sequence': 'this is a simple question.'}, {'score': 0.03, 'token': 8522, 'token_str': 'equation', 'sequence': 'this is a simple equation.'}, {'score': 0.027, 'token': 2028, 'token_str': 'one', 'sequence': 'this is a simple one.'}, {'score': 0.024, 'token': 3627, 'token_str': 'rule', 'sequence': 'this is a simple rule.'}] ``` [Using pipelines in a webserver or with a dataset](../pipeline_tutorial) This mask filling pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"fill-mask"`. The models that this pipeline can use are models that have been trained with a masked language modeling objective, which includes the bi-directional models in the library. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=fill-mask). <Tip> This pipeline only works for inputs with exactly one token masked. Experimental: We added support for multiple masks. The returned values are raw model output, and correspond to disjoint probabilities where one might expect joint probabilities (See [discussion](https://github.com/huggingface/transformers/pull/10222)). </Tip>""" def get_masked_index(self, input_ids: GenericTensor) -> np.ndarray: if self.framework == "tf": masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy() elif self.framework == "pt": masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False) else: raise ValueError("Unsupported framework") return masked_index def _ensure_exactly_one_mask_token(self, input_ids: GenericTensor) -> np.ndarray: masked_index = self.get_masked_index(input_ids) numel = np.prod(masked_index.shape) if numel < 1: raise PipelineException( "fill-mask", self.model.base_model_prefix, f"No mask_token ({self.tokenizer.mask_token}) found on the input", ) def ensure_exactly_one_mask_token(self, model_inputs: GenericTensor): if isinstance(model_inputs, list): for model_input in model_inputs: self._ensure_exactly_one_mask_token(model_input["input_ids"][0]) else: for input_ids in model_inputs["input_ids"]: self._ensure_exactly_one_mask_token(input_ids) def preprocess(self, inputs, return_tensors=None, **preprocess_parameters) -> Dict[str, GenericTensor]: if return_tensors is None: return_tensors = self.framework model_inputs = self.tokenizer(inputs, return_tensors=return_tensors) self.ensure_exactly_one_mask_token(model_inputs) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) model_outputs["input_ids"] = model_inputs["input_ids"] return model_outputs def postprocess(self, model_outputs, top_k=5, target_ids=None): # Cap top_k if there are targets if target_ids is not None and target_ids.shape[0] < top_k: top_k = target_ids.shape[0] input_ids = model_outputs["input_ids"][0] outputs = model_outputs["logits"] if self.framework == "tf": masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()[:, 0] outputs = outputs.numpy() logits = outputs[0, masked_index, :] probs = stable_softmax(logits, axis=-1) if target_ids is not None: probs = tf.gather_nd(tf.squeeze(probs, 0), target_ids.reshape(-1, 1)) probs = tf.expand_dims(probs, 0) topk = tf.math.top_k(probs, k=top_k) values, predictions = topk.values.numpy(), topk.indices.numpy() else: masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False).squeeze(-1) # Fill mask pipeline supports only one ${mask_token} per sample logits = outputs[0, masked_index, :] probs = logits.softmax(dim=-1) if target_ids is not None: probs = probs[..., target_ids] values, predictions = probs.topk(top_k) result = [] single_mask = values.shape[0] == 1 for i, (_values, _predictions) in enumerate(zip(values.tolist(), predictions.tolist())): row = [] for v, p in zip(_values, _predictions): # Copy is important since we're going to modify this array in place tokens = input_ids.numpy().copy() if target_ids is not None: p = target_ids[p].tolist() tokens[masked_index[i]] = p # Filter padding out: tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)] # Originally we skip special tokens to give readable output. # For multi masks though, the other [MASK] would be removed otherwise # making the output look odd, so we add them back sequence = self.tokenizer.decode(tokens, skip_special_tokens=single_mask) proposition = {"score": v, "token": p, "token_str": self.tokenizer.decode([p]), "sequence": sequence} row.append(proposition) result.append(row) if single_mask: return result[0] return result def get_target_ids(self, targets, top_k=None): if isinstance(targets, str): targets = [targets] try: vocab = self.tokenizer.get_vocab() except Exception: vocab = {} target_ids = [] for target in targets: id_ = vocab.get(target, None) if id_ is None: input_ids = self.tokenizer( target, add_special_tokens=False, return_attention_mask=False, return_token_type_ids=False, max_length=1, truncation=True, )["input_ids"] if len(input_ids) == 0: logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " "We cannot replace it with anything meaningful, ignoring it" ) continue id_ = input_ids[0] # XXX: If users encounter this pass # it becomes pretty slow, so let's make sure # The warning enables them to fix the input to # get faster performance. logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_)}`." ) target_ids.append(id_) target_ids = list(set(target_ids)) if len(target_ids) == 0: raise ValueError("At least one target must be provided when passed.") target_ids = np.array(target_ids) return target_ids def _sanitize_parameters(self, top_k=None, targets=None): postprocess_params = {} if targets is not None: target_ids = self.get_target_ids(targets, top_k) postprocess_params["target_ids"] = target_ids if top_k is not None: postprocess_params["top_k"] = top_k if self.tokenizer.mask_token_id is None: raise PipelineException( "fill-mask", self.model.base_model_prefix, "The tokenizer does not define a `mask_token`." ) return {}, {}, postprocess_params def __call__(self, inputs, *args, **kwargs): """ Fill the masked token in the text(s) given as inputs. Args: args (`str` or `List[str]`): One or several texts (or one list of prompts) with masked tokens. targets (`str` or `List[str]`, *optional*): When passed, the model will limit the scores to the passed targets instead of looking up in the whole vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting token will be used (with a warning, and that might be slower). top_k (`int`, *optional*): When passed, overrides the number of predictions to return. Return: A list or a list of list of `dict`: Each result comes as list of dictionaries with the following keys: - **sequence** (`str`) -- The corresponding input with the mask token prediction. - **score** (`float`) -- The corresponding probability. - **token** (`int`) -- The predicted token id (to replace the masked one). - **token** (`str`) -- The predicted token (to replace the masked one). """ outputs = super().__call__(inputs, **kwargs) if isinstance(inputs, list) and len(inputs) == 1: return outputs[0] return outputs

from typing import Dict import numpy as np from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException if is_tf_available(): import tensorflow as tf from ..tf_utils import stable_softmax if is_torch_available(): import torch logger = logging.get_logger(__name__) @add_end_docstrings( PIPELINE_INIT_ARGS, r""" top_k (`int`, defaults to 5): The number of predictions to return. targets (`str` or `List[str]`, *optional*): When passed, the model will limit the scores to the passed targets instead of looking up in the whole vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting token will be used (with a warning, and that might be slower). """, ) class FillMaskPipeline(Pipeline): """ Masked language modeling prediction pipeline using any `ModelWithLMHead`. See the [masked language modeling examples](../task_summary#masked-language-modeling) for more information. Example: ```python >>> from transformers import pipeline >>> fill_masker = pipeline(model="bert-base-uncased") >>> fill_masker("This is a simple [MASK].") [{'score': 0.042, 'token': 3291, 'token_str': 'problem', 'sequence': 'this is a simple problem.'}, {'score': 0.031, 'token': 3160, 'token_str': 'question', 'sequence': 'this is a simple question.'}, {'score': 0.03, 'token': 8522, 'token_str': 'equation', 'sequence': 'this is a simple equation.'}, {'score': 0.027, 'token': 2028, 'token_str': 'one', 'sequence': 'this is a simple one.'}, {'score': 0.024, 'token': 3627, 'token_str': 'rule', 'sequence': 'this is a simple rule.'}] ``` [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial) This mask filling pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"fill-mask"`. The models that this pipeline can use are models that have been trained with a masked language modeling objective, which includes the bi-directional models in the library. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=fill-mask). <Tip> This pipeline only works for inputs with exactly one token masked. Experimental: We added support for multiple masks. The returned values are raw model output, and correspond to disjoint probabilities where one might expect joint probabilities (See [discussion](https://github.com/huggingface/transformers/pull/10222)). </Tip>""" def get_masked_index(self, input_ids: GenericTensor) -> np.ndarray: if self.framework == "tf": masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy() elif self.framework == "pt": masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False) else: raise ValueError("Unsupported framework") return masked_index def _ensure_exactly_one_mask_token(self, input_ids: GenericTensor) -> np.ndarray: masked_index = self.get_masked_index(input_ids) numel = np.prod(masked_index.shape) if numel < 1: raise PipelineException( "fill-mask", self.model.base_model_prefix, f"No mask_token ({self.tokenizer.mask_token}) found on the input", ) def ensure_exactly_one_mask_token(self, model_inputs: GenericTensor): if isinstance(model_inputs, list): for model_input in model_inputs: self._ensure_exactly_one_mask_token(model_input["input_ids"][0]) else: for input_ids in model_inputs["input_ids"]: self._ensure_exactly_one_mask_token(input_ids) def preprocess(self, inputs, return_tensors=None, **preprocess_parameters) -> Dict[str, GenericTensor]: if return_tensors is None: return_tensors = self.framework model_inputs = self.tokenizer(inputs, return_tensors=return_tensors) self.ensure_exactly_one_mask_token(model_inputs) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) model_outputs["input_ids"] = model_inputs["input_ids"] return model_outputs def postprocess(self, model_outputs, top_k=5, target_ids=None): # Cap top_k if there are targets if target_ids is not None and target_ids.shape[0] < top_k: top_k = target_ids.shape[0] input_ids = model_outputs["input_ids"][0] outputs = model_outputs["logits"] if self.framework == "tf": masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()[:, 0] outputs = outputs.numpy() logits = outputs[0, masked_index, :] probs = stable_softmax(logits, axis=-1) if target_ids is not None: probs = tf.gather_nd(tf.squeeze(probs, 0), target_ids.reshape(-1, 1)) probs = tf.expand_dims(probs, 0) topk = tf.math.top_k(probs, k=top_k) values, predictions = topk.values.numpy(), topk.indices.numpy() else: masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False).squeeze(-1) # Fill mask pipeline supports only one ${mask_token} per sample logits = outputs[0, masked_index, :] probs = logits.softmax(dim=-1) if target_ids is not None: probs = probs[..., target_ids] values, predictions = probs.topk(top_k) result = [] single_mask = values.shape[0] == 1 for i, (_values, _predictions) in enumerate(zip(values.tolist(), predictions.tolist())): row = [] for v, p in zip(_values, _predictions): # Copy is important since we're going to modify this array in place tokens = input_ids.numpy().copy() if target_ids is not None: p = target_ids[p].tolist() tokens[masked_index[i]] = p # Filter padding out: tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)] # Originally we skip special tokens to give readable output. # For multi masks though, the other [MASK] would be removed otherwise # making the output look odd, so we add them back sequence = self.tokenizer.decode(tokens, skip_special_tokens=single_mask) proposition = {"score": v, "token": p, "token_str": self.tokenizer.decode([p]), "sequence": sequence} row.append(proposition) result.append(row) if single_mask: return result[0] return result def get_target_ids(self, targets, top_k=None): if isinstance(targets, str): targets = [targets] try: vocab = self.tokenizer.get_vocab() except Exception: vocab = {} target_ids = [] for target in targets: id_ = vocab.get(target, None) if id_ is None: input_ids = self.tokenizer( target, add_special_tokens=False, return_attention_mask=False, return_token_type_ids=False, max_length=1, truncation=True, )["input_ids"] if len(input_ids) == 0: logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " "We cannot replace it with anything meaningful, ignoring it" ) continue id_ = input_ids[0] # XXX: If users encounter this pass # it becomes pretty slow, so let's make sure # The warning enables them to fix the input to # get faster performance. logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_)}`." ) target_ids.append(id_) target_ids = list(set(target_ids)) if len(target_ids) == 0: raise ValueError("At least one target must be provided when passed.") target_ids = np.array(target_ids) return target_ids def _sanitize_parameters(self, top_k=None, targets=None): postprocess_params = {} if targets is not None: target_ids = self.get_target_ids(targets, top_k) postprocess_params["target_ids"] = target_ids if top_k is not None: postprocess_params["top_k"] = top_k if self.tokenizer.mask_token_id is None: raise PipelineException( "fill-mask", self.model.base_model_prefix, "The tokenizer does not define a `mask_token`." ) return {}, {}, postprocess_params def __call__(self, inputs, *args, **kwargs): """ Fill the masked token in the text(s) given as inputs. Args: args (`str` or `List[str]`): One or several texts (or one list of prompts) with masked tokens. targets (`str` or `List[str]`, *optional*): When passed, the model will limit the scores to the passed targets instead of looking up in the whole vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting token will be used (with a warning, and that might be slower). top_k (`int`, *optional*): When passed, overrides the number of predictions to return. Return: A list or a list of list of `dict`: Each result comes as list of dictionaries with the following keys: - **sequence** (`str`) -- The corresponding input with the mask token prediction. - **score** (`float`) -- The corresponding probability. - **token** (`int`) -- The predicted token id (to replace the masked one). - **token** (`str`) -- The predicted token (to replace the masked one). """ outputs = super().__call__(inputs, **kwargs) if isinstance(inputs, list) and len(inputs) == 1: return outputs[0] return outputs

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/canine/modeling_canine.py

# coding=utf-8 # Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CANINE model.""" import copy import math import os from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_canine import CanineConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/canine-s" _CONFIG_FOR_DOC = "CanineConfig" _TOKENIZER_FOR_DOC = "CanineTokenizer" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", "google/canine-r" # See all CANINE models at https://huggingface.co/models?filter=canine ] # Support up to 16 hash functions. _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223] @dataclass class CanineModelOutputWithPooling(ModelOutput): """ Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow Transformer encoders. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final shallow Transformer encoder). pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Hidden-state of the first token of the sequence (classification token) at the last layer of the deep Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the input to each encoder + one for the output of each layer of each encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length // config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial input to each Transformer encoder. The hidden states of the shallow encoders have length `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` // `config.downsampling_rate`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of the 3 Transformer encoders of shape `(batch_size, num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def load_tf_weights_in_canine(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model # also discard the cls weights (which were used for the next sentence prediction pre-training task) if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights", ] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue # if first scope name starts with "bert", change it to "encoder" if name[0] == "bert": name[0] = "encoder" # remove "embeddings" middle name of HashBucketCodepointEmbedders elif name[1] == "embeddings": name.remove(name[1]) # rename segment_embeddings to token_type_embeddings elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings" # rename initial convolutional projection layer elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:] # rename final convolutional projection layer elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:] pointer = model for m_name in name: if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class CanineEmbeddings(nn.Module): """Construct the character, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.config = config # character embeddings shard_embedding_size = config.hidden_size // config.num_hash_functions for i in range(config.num_hash_functions): name = f"HashBucketCodepointEmbedder_{i}" setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size)) self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int): """ Converts ids to hash bucket ids via multiple hashing. Args: input_ids: The codepoints or other IDs to be hashed. num_hashes: The number of hash functions to use. num_buckets: The number of hash buckets (i.e. embeddings in each table). Returns: A list of tensors, each of which is the hash bucket IDs from one hash function. """ if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}") primes = _PRIMES[:num_hashes] result_tensors = [] for prime in primes: hashed = ((input_ids + 1) * prime) % num_buckets result_tensors.append(hashed) return result_tensors def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int): """Converts IDs (e.g. codepoints) into embeddings via multiple hashing.""" if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0") hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets) embedding_shards = [] for i, hash_bucket_ids in enumerate(hash_bucket_tensors): name = f"HashBucketCodepointEmbedder_{i}" shard_embeddings = getattr(self, name)(hash_bucket_ids) embedding_shards.append(shard_embeddings) return torch.cat(embedding_shards, dim=-1) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets( input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets ) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.char_position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class CharactersToMolecules(nn.Module): """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions.""" def __init__(self, config): super().__init__() self.conv = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, char_encoding: torch.Tensor) -> torch.Tensor: # `cls_encoding`: [batch, 1, hidden_size] cls_encoding = char_encoding[:, 0:1, :] # char_encoding has shape [batch, char_seq, hidden_size] # We transpose it to be [batch, hidden_size, char_seq] char_encoding = torch.transpose(char_encoding, 1, 2) downsampled = self.conv(char_encoding) downsampled = torch.transpose(downsampled, 1, 2) downsampled = self.activation(downsampled) # Truncate the last molecule in order to reserve a position for [CLS]. # Often, the last position is never used (unless we completely fill the # text buffer). This is important in order to maintain alignment on TPUs # (i.e. a multiple of 128). downsampled_truncated = downsampled[:, 0:-1, :] # We also keep [CLS] as a separate sequence position since we always # want to reserve a position (and the model capacity that goes along # with that) in the deep BERT stack. # `result`: [batch, molecule_seq, molecule_dim] result = torch.cat([cls_encoding, downsampled_truncated], dim=1) result = self.LayerNorm(result) return result class ConvProjection(nn.Module): """ Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size characters. """ def __init__(self, config): super().__init__() self.config = config self.conv = nn.Conv1d( in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None, ) -> torch.Tensor: # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final] # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq] inputs = torch.transpose(inputs, 1, 2) # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation), # so we pad the tensor manually before passing it to the conv layer # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38 pad_total = self.config.upsampling_kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg pad = nn.ConstantPad1d((pad_beg, pad_end), 0) # `result`: shape (batch_size, char_seq_len, hidden_size) result = self.conv(pad(inputs)) result = torch.transpose(result, 1, 2) result = self.activation(result) result = self.LayerNorm(result) result = self.dropout(result) final_char_seq = result if final_seq_char_positions is not None: # Limit transformer query seq and attention mask to these character # positions to greatly reduce the compute cost. Typically, this is just # done for the MLM training task. # TODO add support for MLM raise NotImplementedError("CanineForMaskedLM is currently not supported") else: query_seq = final_char_seq return query_seq class CanineSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: mixed_query_layer = self.query(from_tensor) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. key_layer = self.transpose_for_scores(self.key(to_tensor)) value_layer = self.transpose_for_scores(self.value(to_tensor)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = from_tensor.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: if attention_mask.ndim == 3: # if attention_mask is 3D, do the following: attention_mask = torch.unsqueeze(attention_mask, dim=1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min # Apply the attention mask (precomputed for all layers in CanineModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class CanineSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineAttention(nn.Module): """ Additional arguments related to local attention: - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention. - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to attend to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`, *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `from_tensor`. - **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `to_tensor`. """ def __init__( self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128, attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128, ): super().__init__() self.self = CanineSelfAttention(config) self.output = CanineSelfOutput(config) self.pruned_heads = set() # additional arguments related to local attention self.local = local if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError( "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped." ) if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError( "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped." ) self.always_attend_to_first_position = always_attend_to_first_position self.first_position_attends_to_all = first_position_attends_to_all self.attend_from_chunk_width = attend_from_chunk_width self.attend_from_chunk_stride = attend_from_chunk_stride self.attend_to_chunk_width = attend_to_chunk_width self.attend_to_chunk_stride = attend_to_chunk_stride def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: if not self.local: self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions) attention_output = self_outputs[0] else: from_seq_length = to_seq_length = hidden_states.shape[1] from_tensor = to_tensor = hidden_states # Create chunks (windows) that we will attend *from* and then concatenate them. from_chunks = [] if self.first_position_attends_to_all: from_chunks.append((0, 1)) # We must skip this first position so that our output sequence is the # correct length (this matters in the *from* sequence only). from_start = 1 else: from_start = 0 for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride): chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width) from_chunks.append((chunk_start, chunk_end)) # Determine the chunks (windows) that will will attend *to*. to_chunks = [] if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length)) for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride): chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width) to_chunks.append((chunk_start, chunk_end)) if len(from_chunks) != len(to_chunks): raise ValueError( f"Expected to have same number of `from_chunks` ({from_chunks}) and " f"`to_chunks` ({from_chunks}). Check strides." ) # next, compute attention scores for each pair of windows and concatenate attention_output_chunks = [] attention_probs_chunks = [] for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks): from_tensor_chunk = from_tensor[:, from_start:from_end, :] to_tensor_chunk = to_tensor[:, to_start:to_end, :] # `attention_mask`: <float>[batch_size, from_seq, to_seq] # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk] attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end] if self.always_attend_to_first_position: cls_attention_mask = attention_mask[:, from_start:from_end, 0:1] attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2) cls_position = to_tensor[:, 0:1, :] to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1) attention_outputs_chunk = self.self( from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions ) attention_output_chunks.append(attention_outputs_chunk[0]) if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1]) attention_output = torch.cat(attention_output_chunks, dim=1) attention_output = self.output(attention_output, hidden_states) outputs = (attention_output,) if not self.local: outputs = outputs + self_outputs[1:] # add attentions if we output them else: outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them return outputs class CanineIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class CanineOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineLayer(nn.Module): def __init__( self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CanineAttention( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) self.intermediate = CanineIntermediate(config) self.output = CanineOutput(config) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class CanineEncoder(nn.Module): def __init__( self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128, ): super().__init__() self.config = config self.layer = nn.ModuleList( [ CanineLayer( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) for _ in range(config.num_hidden_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class CaninePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CaninePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class CanineLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = CaninePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class CanineOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = CanineLMPredictionHead(config) def forward( self, sequence_output: Tuple[torch.Tensor], ) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) return prediction_scores class CaninePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CanineConfig load_tf_weights = load_tf_weights_in_canine base_model_prefix = "canine" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CanineEncoder): module.gradient_checkpointing = value CANINE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CanineConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CANINE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CanineTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING, ) class CanineModel(CaninePreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config shallow_config = copy.deepcopy(config) shallow_config.num_hidden_layers = 1 self.char_embeddings = CanineEmbeddings(config) # shallow/low-dim transformer encoder to get a initial character encoding self.initial_char_encoder = CanineEncoder( shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride, attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride, ) self.chars_to_molecules = CharactersToMolecules(config) # deep transformer encoder self.encoder = CanineEncoder(config) self.projection = ConvProjection(config) # shallow/low-dim transformer encoder to get a final character encoding self.final_char_encoder = CanineEncoder(shallow_config) self.pooler = CaninePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1] to_seq_length = to_mask.shape[1] to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float() # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int): """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer.""" # first, make char_attention_mask 3D by adding a channel dim batch_size, char_seq_len = char_attention_mask.shape poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len)) # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len) pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)( poolable_char_mask.float() ) # finally, squeeze to get tensor of shape (batch_size, mol_seq_len) molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1) return molecule_attention_mask def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor: """Repeats molecules to make them the same length as the char sequence.""" rate = self.config.downsampling_rate molecules_without_extra_cls = molecules[:, 1:, :] # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size] repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2) # So far, we've repeated the elements sufficient for any `char_seq_length` # that's a multiple of `downsampling_rate`. Now we account for the last # n elements (n < `downsampling_rate`), i.e. the remainder of floor # division. We do this by repeating the last molecule a few extra times. last_molecule = molecules[:, -1:, :] remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item() remainder_repeated = torch.repeat_interleave( last_molecule, # +1 molecule to compensate for truncation. repeats=remainder_length + rate, dim=-2, ) # `repeated`: [batch_size, char_seq_len, molecule_hidden_size] return torch.cat([repeated, remainder_repeated], dim=-2) @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=CanineModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CanineModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) molecule_attention_mask = self._downsample_attention_mask( attention_mask, downsampling_rate=self.config.downsampling_rate ) extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask( molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]) ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # `input_char_embeddings`: shape (batch_size, char_seq, char_dim) input_char_embeddings = self.char_embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) # Contextualize character embeddings using shallow Transformer. # We use a 3D attention mask for the local attention. # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim) char_attention_mask = self._create_3d_attention_mask_from_input_mask(input_ids, attention_mask) init_chars_encoder_outputs = self.initial_char_encoder( input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) input_char_encoding = init_chars_encoder_outputs.last_hidden_state # Downsample chars to molecules. # The following lines have dimensions: [batch, molecule_seq, molecule_dim]. # In this transformation, we change the dimensionality from `char_dim` to # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on # the resnet connections (a) from the final char transformer stack back into # the original char transformer stack and (b) the resnet connections from # the final char transformer stack back into the deep BERT stack of # molecules. # # Empirically, it is critical to use a powerful enough transformation here: # mean pooling causes training to diverge with huge gradient norms in this # region of the model; using a convolution here resolves this issue. From # this, it seems that molecules and characters require a very different # feature space; intuitively, this makes sense. init_molecule_encoding = self.chars_to_molecules(input_char_encoding) # Deep BERT encoder # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim) encoder_outputs = self.encoder( init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) molecule_sequence_output = encoder_outputs[0] pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None # Upsample molecules back to characters. # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size) repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1]) # Concatenate representations (contextualized char embeddings and repeated molecules): # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final] concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1) # Project representation dimension back to hidden_size # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) sequence_output = self.projection(concat) # Apply final shallow Transformer # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) final_chars_encoder_outputs = self.final_char_encoder( sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = final_chars_encoder_outputs.last_hidden_state if output_hidden_states: deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1] all_hidden_states = ( all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states ) if output_attentions: deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1] all_self_attentions = ( all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions ) if not return_dict: output = (sequence_output, pooled_output) output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None) return output return CanineModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CANINE_START_DOCSTRING, ) class CanineForSequenceClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CANINE_START_DOCSTRING, ) class CanineForMultipleChoice(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CANINE_START_DOCSTRING, ) class CanineForTokenClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CANINE_START_DOCSTRING, ) class CanineForQuestionAnswering(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CANINE model.""" import copy import math import os from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_canine import CanineConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/canine-s" _CONFIG_FOR_DOC = "CanineConfig" _TOKENIZER_FOR_DOC = "CanineTokenizer" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", "google/canine-r" # See all CANINE models at https://huggingface.co/models?filter=canine ] # Support up to 16 hash functions. _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223] @dataclass class CanineModelOutputWithPooling(ModelOutput): """ Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow Transformer encoders. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final shallow Transformer encoder). pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Hidden-state of the first token of the sequence (classification token) at the last layer of the deep Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the input to each encoder + one for the output of each layer of each encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length // config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial input to each Transformer encoder. The hidden states of the shallow encoders have length `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` // `config.downsampling_rate`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of the 3 Transformer encoders of shape `(batch_size, num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def load_tf_weights_in_canine(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model # also discard the cls weights (which were used for the next sentence prediction pre-training task) if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights", ] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue # if first scope name starts with "bert", change it to "encoder" if name[0] == "bert": name[0] = "encoder" # remove "embeddings" middle name of HashBucketCodepointEmbedders elif name[1] == "embeddings": name.remove(name[1]) # rename segment_embeddings to token_type_embeddings elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings" # rename initial convolutional projection layer elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:] # rename final convolutional projection layer elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:] pointer = model for m_name in name: if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class CanineEmbeddings(nn.Module): """Construct the character, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.config = config # character embeddings shard_embedding_size = config.hidden_size // config.num_hash_functions for i in range(config.num_hash_functions): name = f"HashBucketCodepointEmbedder_{i}" setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size)) self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int): """ Converts ids to hash bucket ids via multiple hashing. Args: input_ids: The codepoints or other IDs to be hashed. num_hashes: The number of hash functions to use. num_buckets: The number of hash buckets (i.e. embeddings in each table). Returns: A list of tensors, each of which is the hash bucket IDs from one hash function. """ if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}") primes = _PRIMES[:num_hashes] result_tensors = [] for prime in primes: hashed = ((input_ids + 1) * prime) % num_buckets result_tensors.append(hashed) return result_tensors def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int): """Converts IDs (e.g. codepoints) into embeddings via multiple hashing.""" if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0") hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets) embedding_shards = [] for i, hash_bucket_ids in enumerate(hash_bucket_tensors): name = f"HashBucketCodepointEmbedder_{i}" shard_embeddings = getattr(self, name)(hash_bucket_ids) embedding_shards.append(shard_embeddings) return torch.cat(embedding_shards, dim=-1) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets( input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets ) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.char_position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class CharactersToMolecules(nn.Module): """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions.""" def __init__(self, config): super().__init__() self.conv = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, char_encoding: torch.Tensor) -> torch.Tensor: # `cls_encoding`: [batch, 1, hidden_size] cls_encoding = char_encoding[:, 0:1, :] # char_encoding has shape [batch, char_seq, hidden_size] # We transpose it to be [batch, hidden_size, char_seq] char_encoding = torch.transpose(char_encoding, 1, 2) downsampled = self.conv(char_encoding) downsampled = torch.transpose(downsampled, 1, 2) downsampled = self.activation(downsampled) # Truncate the last molecule in order to reserve a position for [CLS]. # Often, the last position is never used (unless we completely fill the # text buffer). This is important in order to maintain alignment on TPUs # (i.e. a multiple of 128). downsampled_truncated = downsampled[:, 0:-1, :] # We also keep [CLS] as a separate sequence position since we always # want to reserve a position (and the model capacity that goes along # with that) in the deep BERT stack. # `result`: [batch, molecule_seq, molecule_dim] result = torch.cat([cls_encoding, downsampled_truncated], dim=1) result = self.LayerNorm(result) return result class ConvProjection(nn.Module): """ Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size characters. """ def __init__(self, config): super().__init__() self.config = config self.conv = nn.Conv1d( in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None, ) -> torch.Tensor: # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final] # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq] inputs = torch.transpose(inputs, 1, 2) # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation), # so we pad the tensor manually before passing it to the conv layer # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38 pad_total = self.config.upsampling_kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg pad = nn.ConstantPad1d((pad_beg, pad_end), 0) # `result`: shape (batch_size, char_seq_len, hidden_size) result = self.conv(pad(inputs)) result = torch.transpose(result, 1, 2) result = self.activation(result) result = self.LayerNorm(result) result = self.dropout(result) final_char_seq = result if final_seq_char_positions is not None: # Limit transformer query seq and attention mask to these character # positions to greatly reduce the compute cost. Typically, this is just # done for the MLM training task. # TODO add support for MLM raise NotImplementedError("CanineForMaskedLM is currently not supported") else: query_seq = final_char_seq return query_seq class CanineSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: mixed_query_layer = self.query(from_tensor) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. key_layer = self.transpose_for_scores(self.key(to_tensor)) value_layer = self.transpose_for_scores(self.value(to_tensor)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = from_tensor.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: if attention_mask.ndim == 3: # if attention_mask is 3D, do the following: attention_mask = torch.unsqueeze(attention_mask, dim=1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min # Apply the attention mask (precomputed for all layers in CanineModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class CanineSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineAttention(nn.Module): """ Additional arguments related to local attention: - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention. - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to attend to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`, *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `from_tensor`. - **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `to_tensor`. """ def __init__( self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128, attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128, ): super().__init__() self.self = CanineSelfAttention(config) self.output = CanineSelfOutput(config) self.pruned_heads = set() # additional arguments related to local attention self.local = local if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError( "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped." ) if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError( "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped." ) self.always_attend_to_first_position = always_attend_to_first_position self.first_position_attends_to_all = first_position_attends_to_all self.attend_from_chunk_width = attend_from_chunk_width self.attend_from_chunk_stride = attend_from_chunk_stride self.attend_to_chunk_width = attend_to_chunk_width self.attend_to_chunk_stride = attend_to_chunk_stride def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: if not self.local: self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions) attention_output = self_outputs[0] else: from_seq_length = to_seq_length = hidden_states.shape[1] from_tensor = to_tensor = hidden_states # Create chunks (windows) that we will attend *from* and then concatenate them. from_chunks = [] if self.first_position_attends_to_all: from_chunks.append((0, 1)) # We must skip this first position so that our output sequence is the # correct length (this matters in the *from* sequence only). from_start = 1 else: from_start = 0 for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride): chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width) from_chunks.append((chunk_start, chunk_end)) # Determine the chunks (windows) that will will attend *to*. to_chunks = [] if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length)) for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride): chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width) to_chunks.append((chunk_start, chunk_end)) if len(from_chunks) != len(to_chunks): raise ValueError( f"Expected to have same number of `from_chunks` ({from_chunks}) and " f"`to_chunks` ({from_chunks}). Check strides." ) # next, compute attention scores for each pair of windows and concatenate attention_output_chunks = [] attention_probs_chunks = [] for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks): from_tensor_chunk = from_tensor[:, from_start:from_end, :] to_tensor_chunk = to_tensor[:, to_start:to_end, :] # `attention_mask`: <float>[batch_size, from_seq, to_seq] # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk] attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end] if self.always_attend_to_first_position: cls_attention_mask = attention_mask[:, from_start:from_end, 0:1] attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2) cls_position = to_tensor[:, 0:1, :] to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1) attention_outputs_chunk = self.self( from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions ) attention_output_chunks.append(attention_outputs_chunk[0]) if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1]) attention_output = torch.cat(attention_output_chunks, dim=1) attention_output = self.output(attention_output, hidden_states) outputs = (attention_output,) if not self.local: outputs = outputs + self_outputs[1:] # add attentions if we output them else: outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them return outputs class CanineIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class CanineOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineLayer(nn.Module): def __init__( self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CanineAttention( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) self.intermediate = CanineIntermediate(config) self.output = CanineOutput(config) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class CanineEncoder(nn.Module): def __init__( self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128, ): super().__init__() self.config = config self.layer = nn.ModuleList( [ CanineLayer( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) for _ in range(config.num_hidden_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class CaninePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CaninePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class CanineLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = CaninePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class CanineOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = CanineLMPredictionHead(config) def forward( self, sequence_output: Tuple[torch.Tensor], ) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) return prediction_scores class CaninePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CanineConfig load_tf_weights = load_tf_weights_in_canine base_model_prefix = "canine" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CanineEncoder): module.gradient_checkpointing = value CANINE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CanineConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CANINE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CanineTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING, ) class CanineModel(CaninePreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config shallow_config = copy.deepcopy(config) shallow_config.num_hidden_layers = 1 self.char_embeddings = CanineEmbeddings(config) # shallow/low-dim transformer encoder to get a initial character encoding self.initial_char_encoder = CanineEncoder( shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride, attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride, ) self.chars_to_molecules = CharactersToMolecules(config) # deep transformer encoder self.encoder = CanineEncoder(config) self.projection = ConvProjection(config) # shallow/low-dim transformer encoder to get a final character encoding self.final_char_encoder = CanineEncoder(shallow_config) self.pooler = CaninePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1] to_seq_length = to_mask.shape[1] to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float() # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int): """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer.""" # first, make char_attention_mask 3D by adding a channel dim batch_size, char_seq_len = char_attention_mask.shape poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len)) # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len) pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)( poolable_char_mask.float() ) # finally, squeeze to get tensor of shape (batch_size, mol_seq_len) molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1) return molecule_attention_mask def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor: """Repeats molecules to make them the same length as the char sequence.""" rate = self.config.downsampling_rate molecules_without_extra_cls = molecules[:, 1:, :] # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size] repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2) # So far, we've repeated the elements sufficient for any `char_seq_length` # that's a multiple of `downsampling_rate`. Now we account for the last # n elements (n < `downsampling_rate`), i.e. the remainder of floor # division. We do this by repeating the last molecule a few extra times. last_molecule = molecules[:, -1:, :] remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item() remainder_repeated = torch.repeat_interleave( last_molecule, # +1 molecule to compensate for truncation. repeats=remainder_length + rate, dim=-2, ) # `repeated`: [batch_size, char_seq_len, molecule_hidden_size] return torch.cat([repeated, remainder_repeated], dim=-2) @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=CanineModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CanineModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) molecule_attention_mask = self._downsample_attention_mask( attention_mask, downsampling_rate=self.config.downsampling_rate ) extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask( molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]) ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # `input_char_embeddings`: shape (batch_size, char_seq, char_dim) input_char_embeddings = self.char_embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) # Contextualize character embeddings using shallow Transformer. # We use a 3D attention mask for the local attention. # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim) char_attention_mask = self._create_3d_attention_mask_from_input_mask(input_ids, attention_mask) init_chars_encoder_outputs = self.initial_char_encoder( input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) input_char_encoding = init_chars_encoder_outputs.last_hidden_state # Downsample chars to molecules. # The following lines have dimensions: [batch, molecule_seq, molecule_dim]. # In this transformation, we change the dimensionality from `char_dim` to # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on # the resnet connections (a) from the final char transformer stack back into # the original char transformer stack and (b) the resnet connections from # the final char transformer stack back into the deep BERT stack of # molecules. # # Empirically, it is critical to use a powerful enough transformation here: # mean pooling causes training to diverge with huge gradient norms in this # region of the model; using a convolution here resolves this issue. From # this, it seems that molecules and characters require a very different # feature space; intuitively, this makes sense. init_molecule_encoding = self.chars_to_molecules(input_char_encoding) # Deep BERT encoder # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim) encoder_outputs = self.encoder( init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) molecule_sequence_output = encoder_outputs[0] pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None # Upsample molecules back to characters. # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size) repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1]) # Concatenate representations (contextualized char embeddings and repeated molecules): # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final] concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1) # Project representation dimension back to hidden_size # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) sequence_output = self.projection(concat) # Apply final shallow Transformer # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) final_chars_encoder_outputs = self.final_char_encoder( sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = final_chars_encoder_outputs.last_hidden_state if output_hidden_states: deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1] all_hidden_states = ( all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states ) if output_attentions: deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1] all_self_attentions = ( all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions ) if not return_dict: output = (sequence_output, pooled_output) output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None) return output return CanineModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CANINE_START_DOCSTRING, ) class CanineForSequenceClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CANINE_START_DOCSTRING, ) class CanineForMultipleChoice(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CANINE_START_DOCSTRING, ) class CanineForTokenClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CANINE_START_DOCSTRING, ) class CanineForQuestionAnswering(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/codegen/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _import_structure = { "configuration_codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenOnnxConfig"], "tokenization_codegen": ["CodeGenTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_codegen_fast"] = ["CodeGenTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_codegen"] = [ "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", "CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel", ] if TYPE_CHECKING: from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig from .tokenization_codegen import CodeGenTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_codegen_fast import CodeGenTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_codegen import ( CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST, CodeGenForCausalLM, CodeGenModel, CodeGenPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _import_structure = { "configuration_codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenOnnxConfig"], "tokenization_codegen": ["CodeGenTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_codegen_fast"] = ["CodeGenTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_codegen"] = [ "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", "CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel", ] if TYPE_CHECKING: from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig from .tokenization_codegen import CodeGenTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_codegen_fast import CodeGenTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_codegen import ( CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST, CodeGenForCausalLM, CodeGenModel, CodeGenPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/decision_transformer/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _import_structure = { "configuration_decision_transformer": [ "DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "DecisionTransformerConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_decision_transformer"] = [ "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "DecisionTransformerGPT2Model", "DecisionTransformerGPT2PreTrainedModel", "DecisionTransformerModel", "DecisionTransformerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, DecisionTransformerConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, DecisionTransformerGPT2Model, DecisionTransformerGPT2PreTrainedModel, DecisionTransformerModel, DecisionTransformerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _import_structure = { "configuration_decision_transformer": [ "DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "DecisionTransformerConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_decision_transformer"] = [ "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "DecisionTransformerGPT2Model", "DecisionTransformerGPT2PreTrainedModel", "DecisionTransformerModel", "DecisionTransformerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, DecisionTransformerConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, DecisionTransformerGPT2Model, DecisionTransformerGPT2PreTrainedModel, DecisionTransformerModel, DecisionTransformerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import re import sys import types import torch from transformers import AutoTokenizer, GPT2Config from transformers.modeling_utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME, shard_checkpoint def add_checkpointing_args(parser): parser.add_argument("--megatron-path", type=str, default=None, help="Base directory of Megatron repository") parser.add_argument( "--convert_checkpoint_from_megatron_to_transformers", action="store_true", help=( "If True, convert a Megatron checkpoint to a Transformers checkpoint. " "If False, convert a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--load_path", type=str, required=True, help="Path to the checkpoint to convert.", ) parser.add_argument( "--save_path", type=str, required=True, help="Path to the converted checkpoint.", ) parser.add_argument("--print-checkpoint-structure", action="store_true") return parser def add_megatron_checkpoint_args(parser): parser.add_argument( "--target_tensor_model_parallel_size", type=int, default=1, help=( "The tensor model parallel size of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--target_pipeline_model_parallel_size", type=int, default=1, help=( "The pipeline model parallel size of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--target_data_parallel_size", type=int, default=1, help=( "The data parallel size of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--target_params_dtype", type=str, default="fp32", help=( "The dtype of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--make_vocab_size_divisible_by", type=int, default=128, help=( "Pad the vocab size to be divisible by this value. " "This is added for computational efficieny reasons. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--use_distributed_optimizer", action="store_true", help=( "If True, use the distributed optimizer. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) return parser def add_transformers_checkpoint_args(parser): parser.add_argument( "--tokenizer_name", type=str, default=None, help=( "The name of the pre-trained tokenizer to save. " "If not None, the tokenizer will be saved. " "Only used when converting a Megatron checkpoint to a Transformers checkpoint." ), ) parser.add_argument( "--max_shard_size", type=str, default="10GB", help=( "The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size " "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`). " "Only used when converting a Megatron checkpoint to a Transformers checkpoint." ), ) return parser # The simple map of names for "automated" rules. megatron_to_transformers = { "attention.dense": ".attn.c_proj.", "self_attention.dense": ".attn.c_proj.", "mlp.dense_h_to_4h": ".mlp.c_fc.", "mlp.dense_4h_to_h": ".mlp.c_proj.", } transformers_to_megatron = {v[1:-1]: k for k, v in megatron_to_transformers.items()} tensor_parallel_params = [ # megatron-lm layers to merge across tp ranks "self_attention.query_key_value.weight", "self_attention.query_key_value.bias", "self_attention.dense.weight", "mlp.dense_h_to_4h.weight", "mlp.dense_h_to_4h.bias", "mlp.dense_4h_to_h.weight", # deprecated "attention.query_key_value.weight", "attention.query_key_value.bias", "attention.dense.weight", # transformers layers to split across tp ranks "attn.c_attn.weight", "attn.c_attn.bias", "attn.c_proj.weight", "mlp.c_fc.weight", "mlp.c_fc.bias", "mlp.c_proj.weight", ] def recursive_print(name, val, spaces=0): """ Recursively print the structure of a checkpoint. This function is taken from `convert_megatron_gpt2_checkpoint.py` Args: name (str): the name of the current tensor parameter val (Tuple(int)): the shape of the current tensor parameter spaces (int): the number of spaces to print before the output for a nested structure """ # Format the message. if name is None: msg = None else: fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}" msg = fmt.format(name) # Print and recurse (if needed). if isinstance(val, dict): if msg is not None: print(msg) for k in val.keys(): recursive_print(k, val[k], spaces + 2) elif isinstance(val, torch.Tensor): print(msg, ":", val.size()) else: print(msg, ":", val) def megatron_to_transformers_fix_query_key_value_ordering( param, checkpoint_version, num_splits, num_heads, hidden_size ): """ Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] for compatibility with later versions of NVIDIA Megatron-LM. The inverse operation is performed inside Megatron-LM to read checkpoints: https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 If param is the weight tensor of the self-attention block, the returned tensor will have to be transposed one more time to be read by HuggingFace GPT2. This function is taken from `convert_megatron_gpt2_checkpoint.py` Args: param (torch.Tensor): the tensor to permute checkpoint_version (int): the version of the checkpoint. num_splits (int): the number of projections, usually 3 for (Query, Key, Value) num_heads (int): the number of attention heads hidden_size (int): the hidden size per head """ input_shape = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:] param = param.view(*saved_shape) param = param.transpose(0, 2) param = param.transpose(1, 2).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:] param = param.view(*saved_shape) param = param.transpose(0, 1).contiguous() param = param.view(*input_shape) return param def transformers_to_megatron_fix_query_key_value_ordering( param, checkpoint_version, num_splits, num_heads, hidden_size ): """ Permutes layout of param tensor to the one compatible with respective NVIDIA Megatron-LM chekpoint versions. Input is [num_splits * num_heads * hidden_size, :] and output is [num_heads * hidden_size * num_splits, :] for version 1.0 and [num_heads * num_splits * hidden_size, :] for version 2.0 and later. If param is the weight tensor of the self-attention block, the param needs to be already transposed before calling this function. Args: param (torch.Tensor): the tensor to permute checkpoint_version (int): the version of the checkpoint. num_splits (int): the number of projections, usually 3 for (Query, Key, Value) num_heads (int): the number of attention heads hidden_size (int): the hidden size per head """ # Input is [num_splits * num_heads * hidden_size, :] input_shape = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:] param = param.view(*current_shape) param = param.transpose(0, 2) param = param.transpose(1, 2).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:] param = param.view(*current_shape) param = param.transpose(0, 1).contiguous() param = param.view(*input_shape) return param def merge_transformers_sharded_states(path, num_checkpoints): """ Merge sharded checkpoints from transformers into a single checkpoint. Args: path (str): the path to the sharded checkpoints num_checkpoints (int): the number of checkpoints to merge """ state_dict = {} for i in range(1, num_checkpoints + 1): checkpoint_path = os.path.join(path, f"pytorch_model-{i:05d}-of-{num_checkpoints:05d}.bin") current_chunk = torch.load(checkpoint_path, map_location="cpu") state_dict.update(current_chunk) return state_dict def get_megatron_sharded_states(args, tp_size, pp_size, pp_rank): """ Get sharded checkpoints from NVIDIA Megatron-LM checkpoint based on the provided tensor parallel size, pipeline parallel size and pipeline parallel rank. Args: args (argparse.Namespace): the arguments to the script tp_size (int): the tensor parallel size pp_size (int): the pipeline parallel size pp_rank (int): the pipeline parallel rank """ tp_state_dicts = [] for i in range(tp_size): sub_dir_name = f"mp_rank_{i:02d}" if pp_size == 1 else f"mp_rank_{i:02d}_{pp_rank:03d}" checkpoint_name = os.listdir(os.path.join(args.load_path, sub_dir_name))[0] checkpoint_path = os.path.join(args.load_path, sub_dir_name, checkpoint_name) state_dict = torch.load(checkpoint_path, map_location="cpu") tp_state_dicts.append(state_dict) return tp_state_dicts def get_element_from_dict_by_path(d, path): """ Get element from dictionary by path. If element is not present, recursively add empty dictionaries. Args: d (dict): the dictionary to get the element from path (list): the path to the element which is delimited by "." """ path = path.split(".") for k in path: if k not in d: d[k] = {} d = d[k] return d def convert_checkpoint_from_megatron_to_transformers(args): """ Convert NVIDIA Megatron-LM checkpoint to HuggingFace Transformers checkpoint. This handles Megatron checkpoints with different tensor parallelism and pipeline parallelism sizes. It saves the converted checkpoint into shards using HuggingFace Transformers checkpoint sharding functionality. This greatly extends the functionality of `convert_megatron_gpt2_checkpoint.py` Args: args (argparse.Namespace): the arguments to the script """ # Load Megatron-LM checkpoint arguments from the state dict sub_dirs = os.listdir(args.load_path) possible_sub_dirs = ["mp_rank_00", "mp_rank_00_000"] for sub_dir in possible_sub_dirs: if sub_dir in sub_dirs: rank0_checkpoint_name = os.listdir(os.path.join(args.load_path, sub_dir))[0] rank0_checkpoint_path = os.path.join(args.load_path, sub_dir, rank0_checkpoint_name) break print(f"Loading Megatron-LM checkpoint arguments from: {rank0_checkpoint_path}") state_dict = torch.load(rank0_checkpoint_path, map_location="cpu") megatron_args = state_dict.get("args", None) if megatron_args is None: raise ValueError( "Megatron-LM checkpoint does not contain arguments. This utility only supports Megatron-LM checkpoints" " containing all the megatron arguments. This is because it loads all config related to model" " architecture, the tensor and pipeline model parallel size from the checkpoint insead of user having to" " manually specify all the details. Please save Megatron-LM checkpoint along with all the megatron" " arguments to use this utility." ) # Create Transformers GPT2 config from Megatron-LM arguments if megatron_args is not None: if megatron_args.bias_gelu_fusion: activation_function = "gelu_fast" elif megatron_args.openai_gelu: activation_function = "gelu_new" else: activation_function = "gelu" else: # in the very early days this used to be "gelu_new" activation_function = "gelu_new" vocab_size = ( megatron_args.padded_vocab_size if getattr(megatron_args, "orig_vocab_size", None) is None else megatron_args.orig_vocab_size ) print(vocab_size) config = GPT2Config( vocab_size=vocab_size, n_positions=megatron_args.max_position_embeddings, n_embd=megatron_args.hidden_size, n_layer=megatron_args.num_layers, n_head=megatron_args.num_attention_heads, n_inner=megatron_args.ffn_hidden_size, activation_function=activation_function, resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, scale_attn_weights=True, use_cache=True, bos_token_id=vocab_size - 1, eos_token_id=vocab_size - 1, architectures=["GPT2LMHeadModel"], ) output_state_dict = {} checkpoint_version = state_dict.get("checkpoint_version", 0.0) tp_size = megatron_args.tensor_model_parallel_size pp_size = megatron_args.pipeline_model_parallel_size dtype = torch.float32 # The regex to extract layer names. layer_re = re.compile("layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)") # Convert. print("Converting") # Embeddings print("Converting embeddings") tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, 0) # Convert and store the position embeddings. position_embeddings = get_element_from_dict_by_path( tp_state_dicts[0], "model.language_model.embedding.position_embeddings.weight" ) output_state_dict["transformer.wpe.weight"] = position_embeddings.to(dtype) # Convert and store the word embeddings. word_embeddings = torch.cat( [ get_element_from_dict_by_path( tp_state_dicts[tp_rank], "model.language_model.embedding.word_embeddings.weight" ) for tp_rank in range(tp_size) ], dim=0, ) word_embeddings = word_embeddings[:vocab_size].to(dtype) output_state_dict["transformer.wte.weight"] = word_embeddings # Transformer Layers print("Converting transformer layers") # The number of heads. heads = config.n_head # The hidden_size per head. hidden_size_per_head = config.n_embd // config.n_head n_positions = config.n_positions num_layers = config.num_hidden_layers // pp_size for pp_rank in range(pp_size): if pp_size > 0: print(f"Converting pipeline parallel rank {pp_rank}") tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, pp_rank) # The transformer. path = ( "model.language_model.transformer" if "transformer" in get_element_from_dict_by_path(tp_state_dicts[0], "model.language_model").keys() else "model.language_model.encoder" ) # Extract the layers. for key, val in get_element_from_dict_by_path(tp_state_dicts[0], path).items(): # Match the name. m = layer_re.match(key) # Stop if that's not a layer if m is None: break # The index of the layer. layer_idx = int(m.group(1)) + pp_rank * num_layers # The name of the operation. op_name = m.group(2) # Is it a weight or a bias? weight_or_bias = m.group(3) # The name of the layer. layer_name = f"transformer.h.{layer_idx}" if op_name + "." + weight_or_bias not in tensor_parallel_params: params = val.to(dtype) else: dim = 1 if op_name in ["self_attention.dense", "mlp.dense_4h_to_h", "attention.dense"] else 0 params = torch.cat( [val] + [ get_element_from_dict_by_path(tp_state_dicts[tp_rank], f"{path}")[key] for tp_rank in range(1, tp_size) ], dim=dim, ).to(dtype) # For layernorm(s), simply store the layer norm. if op_name.endswith("layernorm"): ln_name = "ln_1" if op_name.startswith("input") else "ln_2" output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = params # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=dtype)).view( 1, 1, n_positions, n_positions ) output_state_dict[layer_name + ".attn.bias"] = causal_mask # Insert a "dummy" tensor for masked_bias. masked_bias = torch.tensor(-1e4, dtype=dtype) output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias out_val = megatron_to_transformers_fix_query_key_value_ordering( params, checkpoint_version, 3, heads, hidden_size_per_head, ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. out_val = out_val.transpose(0, 1).contiguous() # Store. output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": out_val = megatron_to_transformers_fix_query_key_value_ordering( params, checkpoint_version, 3, heads, hidden_size_per_head ) # Store. No change of shape. output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val # Transpose the weights. elif weight_or_bias == "weight": out_name = megatron_to_transformers[op_name] output_state_dict[layer_name + out_name + "weight"] = params.transpose(0, 1) # Copy the bias. elif weight_or_bias == "bias": out_name = megatron_to_transformers[op_name] output_state_dict[layer_name + out_name + "bias"] = params if config.n_layer != (layer_idx + 1): raise ValueError(f"Expected {config.n_layer} layers but found {layer_idx + 1}") # The final layernorm. print("Converting final layernorm") params = get_element_from_dict_by_path(tp_state_dicts[0], str(path)) output_state_dict["transformer.ln_f.weight"] = params["final_layernorm.weight"].to(dtype) output_state_dict["transformer.ln_f.bias"] = params["final_layernorm.bias"].to(dtype) # For LM head, transformers' wants the matrix to weight embeddings. print("Converting LM head") output_state_dict["lm_head.weight"] = word_embeddings.to(dtype) # It should be done! print("Conversion from Megatron-LM to Transformers is done!") # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(None, output_state_dict) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if args.tokenizer_name is None: tokenizer_name = "gpt2" else: tokenizer_name = args.tokenizer_name tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) tokenizer_class = type(tokenizer).__name__ config.tokenizer_class = tokenizer_class # Store the config to file. print("Saving config") config.save_pretrained(args.save_path) # Save tokenizer based on args if args.tokenizer_name is not None: print(f"Adding {tokenizer_class} tokenizer files") tokenizer.save_pretrained(args.save_path) # Store the state_dict to file. max_shard_size = int(args.max_shard_size) if args.max_shard_size.isdigit() else args.max_shard_size shards, index = shard_checkpoint(output_state_dict, max_shard_size=max_shard_size) # Save the model for shard_file, shard in shards.items(): torch.save(shard, os.path.join(args.save_path, shard_file)) if index is None: print(f"Model weights saved in {os.path.join(args.save_path, WEIGHTS_NAME)}") else: save_index_file = os.path.join(args.save_path, WEIGHTS_INDEX_NAME) # Save the index as well with open(save_index_file, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) print( f"The model is bigger than the maximum size per checkpoint ({args.max_shard_size}) and is going to be " f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the " f"index located at {save_index_file}." ) def convert_checkpoint_from_transformers_to_megatron(args): """ Convert a checkpoint from HuggingFace Transformers to Megatron-LM. This allows converted checkpoints with variable tensor parallelism and pipeline parallelism sizes. It takes as input a checkpoint from HuggingFace Transformers which can have multiple shards. Args: args (argparse.Namespace): the arguments to the script """ os.makedirs(args.save_path, exist_ok=True) # Search in directory above this sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) if args.megatron_path is not None: sys.path.insert(0, args.megatron_path) try: from megatron.tokenizer.tokenizer import _vocab_size_with_padding except ModuleNotFoundError: print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") exit(1) # load the transformers model state dict and config sub_dirs = [x for x in os.listdir(args.load_path) if x.startswith("pytorch_model")] if len(sub_dirs) == 1: checkpoint_name = "pytorch_model.bin" state_dict = torch.load(os.path.join(args.load_path, checkpoint_name), map_location="cpu") else: num_checkpoints = len(sub_dirs) - 1 state_dict = merge_transformers_sharded_states(args.load_path, num_checkpoints) config = GPT2Config.from_pretrained(args.load_path) # Saving the tracker file tracker_filepath = os.path.join(args.save_path, "latest_checkpointed_iteration.txt") with open(tracker_filepath, "w") as f: f.write("release") # create `release` dir in args.load_path release_dir = os.path.join(args.save_path, "release") os.makedirs(release_dir, exist_ok=True) # megatron args megatron_args = { "orig_vocab_size": config.vocab_size, "max_position_embeddings": config.n_positions, "hidden_size": config.n_embd, "num_layers": config.n_layer, "num_attention_heads": config.n_head, "ffn_hidden_size": config.n_inner, "tensor_model_parallel_size": args.target_tensor_model_parallel_size, "pipeline_model_parallel_size": args.target_pipeline_model_parallel_size, "data_parallel_size": args.target_data_parallel_size, "make_vocab_size_divisible_by": args.make_vocab_size_divisible_by, "rank": 0, "tokenizer_type": "GPT2BPETokenizer", } if config.activation_function == "gelu": megatron_args["bias_gelu_fusion"] = False megatron_args["openai_gelu"] = False elif config.activation_function == "gelu_fast": megatron_args["bias_gelu_fusion"] = True megatron_args["openai_gelu"] = False elif config.activation_function == "gelu_new": megatron_args["bias_gelu_fusion"] = False megatron_args["openai_gelu"] = True margs = types.SimpleNamespace() for k, v in megatron_args.items(): setattr(margs, k, v) # params dtype if args.target_params_dtype == "fp16": dtype = torch.float16 elif args.target_params_dtype == "bf16": dtype = torch.bfloat16 else: dtype = torch.float32 setattr(margs, "params_dtype", dtype) # save dummy optim state dict dummy_optim_state_dict = {} dummy_optim_state_dict["optimizer"] = { "step": 0, "param_groups": [ { "lr": 0.0, "beta1": 0.0, "beta2": 0.0, "eps": 0.0, "weight_decay": 0.0, "correct_bias": False, "params": [], } ], } if args.use_distributed_optimizer: for i in range(args.target_pipeline_model_parallel_size): for j in range(args.target_tensor_model_parallel_size): for k in range(args.target_data_parallel_size): if args.target_pipeline_model_parallel_size == 1: checkpoint_dir = f"mp_rank_{i:02d}_{k:03d}" else: checkpoint_dir = f"mp_rank_{i:02d}_{j:03d}_{k:03d}" checkpoint_dir = os.path.join(release_dir, checkpoint_dir) os.makedirs(checkpoint_dir, exist_ok=True) torch.save( dummy_optim_state_dict, os.path.join(checkpoint_dir, "optim.pt"), ) # Convert. print("Converting") output_state_dict = [] for i in range(args.target_tensor_model_parallel_size): output_state_dict.append({}) # Embedding layer print("converting embedding layer") pos_embedding = state_dict["transformer.wpe.weight"].to(dtype) word_embedding = state_dict["transformer.wte.weight"].to(dtype) orig_vocab_size = config.vocab_size padded_vocab_size = _vocab_size_with_padding(orig_vocab_size, margs) setattr(margs, "padded_vocab_size", padded_vocab_size) # Cut out extra padding we don't need if orig_vocab_size > padded_vocab_size: full_word_embed = word_embedding[0:padded_vocab_size, :] # Expanding embedding to larger size by replicating final entry elif orig_vocab_size < padded_vocab_size: padding_size = padded_vocab_size - orig_vocab_size full_word_embed = torch.cat((word_embedding, word_embedding[-1].unsqueeze(0).expand(padding_size, -1))) # Same size! else: full_word_embed = word_embedding # Split into new tensor model parallel sizes out_word_embed = torch.chunk(full_word_embed, args.target_tensor_model_parallel_size, dim=0) for i in range(args.target_tensor_model_parallel_size): pos_emb_dict = get_element_from_dict_by_path( output_state_dict[i], "model.language_model.embedding.position_embeddings" ) pos_emb_dict["weight"] = pos_embedding word_emb_dict = get_element_from_dict_by_path( output_state_dict[i], "model.language_model.embedding.word_embeddings" ) word_emb_dict["weight"] = out_word_embed[i] # Transformer layers print("converting transformer layers") if config.num_hidden_layers % args.target_tensor_model_parallel_size != 0: raise ValueError( f"Number of layers ({config.num_hidden_layers}) must be divisible by number of tensor parallelism" f" ({args.target_tensor_model_parallel_size})" ) num_layers = config.num_hidden_layers // args.target_pipeline_model_parallel_size layer_re = re.compile("transformer.h\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)") # The number of heads. heads = config.n_head # The hidden_size per head. hidden_size_per_head = config.n_embd // config.n_head for pp_rank in range(args.target_pipeline_model_parallel_size): layer_offset = pp_rank * num_layers if pp_rank > 0: output_state_dict = [] for i in range(args.target_tensor_model_parallel_size): output_state_dict.append({}) for layer in range(num_layers): pp_layer_id = layer + layer_offset layers_to_copy = [ layer_name for layer_name in state_dict.keys() if layer_name.startswith(f"transformer.h.{pp_layer_id}.") ] for layer_name in layers_to_copy: m = layer_re.match(layer_name) # Stop if that's not a layer if m is None: break # The index of the layer. _ = int(m.group(1)) # The name of the operation. op_name = m.group(2) # Is it a weight or a bias? weight_or_bias = m.group(3) params = state_dict[layer_name].to(dtype) # handle layernorm if op_name.startswith("ln"): out_name = "input_layernorm" if op_name.endswith("1") else "post_attention_layernorm" layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}" # handle attention K, V, Q weights elif op_name.startswith("attn.c_attn") and weight_or_bias == "weight": # transformers stores D X (3*D) but Megatron-LM expects (3*D) X D. params = params.transpose(0, 1).contiguous() params = transformers_to_megatron_fix_query_key_value_ordering( params, 3.0, 3, heads, hidden_size_per_head, ) layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}" # handle attention K, V, Q bias elif op_name.startswith("attn.c_attn") and weight_or_bias == "bias": params = transformers_to_megatron_fix_query_key_value_ordering( params, 3.0, 3, heads, hidden_size_per_head, ) layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}" # handle attention and mlp weights elif weight_or_bias == "weight": out_name = transformers_to_megatron.get(op_name, None) if out_name is None: continue params = params.transpose(0, 1) layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}" # handle attention and mlp bias elif weight_or_bias == "bias": out_name = transformers_to_megatron.get(op_name, None) if out_name is None: continue layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}" # skip else: continue if op_name + "." + weight_or_bias in tensor_parallel_params: dim = 1 if op_name in ["attn.c_proj", "mlp.c_proj"] else 0 params = torch.chunk(params, args.target_tensor_model_parallel_size, dim=dim) for i in range(args.target_tensor_model_parallel_size): params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder") params_dict[layer_name] = ( params[i] if (op_name + "." + weight_or_bias in tensor_parallel_params) else params ) if pp_rank == args.target_pipeline_model_parallel_size - 1: # handle final layernorm for weight_or_bias in ["weight", "bias"]: params = state_dict[f"transformer.ln_f.{weight_or_bias}"].to(dtype) layer_name = f"final_layernorm.{weight_or_bias}" for i in range(args.target_tensor_model_parallel_size): params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder") params_dict[layer_name] = params # add the LM head for i in range(args.target_tensor_model_parallel_size): params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.word_embeddings_for_head") params_dict["weight"] = out_word_embed[i] # saving the state dict as per the tp_rank and pp_rank for tp_rank in range(args.target_tensor_model_parallel_size): output_state_dict[tp_rank]["checkpoint_version"] = 3.0 output_state_dict[tp_rank]["args"] = margs checkpoint_dir = ( f"mp_rank_{tp_rank:02d}" if args.target_pipeline_model_parallel_size == 1 else f"mp_rank_{tp_rank:02d}_{pp_rank:03d}" ) if args.use_distributed_optimizer: checkpoint_name = "model_rng.pt" else: checkpoint_name = "model_optim_rng.pt" output_state_dict[tp_rank]["optimizer"] = dummy_optim_state_dict["optimizer"] checkpoint_dir = os.path.join(release_dir, checkpoint_dir) os.makedirs(checkpoint_dir, exist_ok=True) checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name) if args.print_checkpoint_structure: print( f"Checkpoint structure of model state dict shard belonging to TP rank {tp_rank} and PP rank" f" {pp_rank}:" ) recursive_print(None, output_state_dict[tp_rank]) torch.save(output_state_dict[tp_rank], checkpoint_path) def main(): parser = argparse.ArgumentParser() parser = add_checkpointing_args(parser) parser = add_megatron_checkpoint_args(parser) parser = add_transformers_checkpoint_args(parser) args = parser.parse_args() if args.convert_checkpoint_from_megatron_to_transformers: convert_checkpoint_from_megatron_to_transformers(args) else: convert_checkpoint_from_transformers_to_megatron(args) if __name__ == "__main__": main()

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import re import sys import types import torch from transformers import AutoTokenizer, GPT2Config from transformers.modeling_utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME, shard_checkpoint def add_checkpointing_args(parser): parser.add_argument("--megatron-path", type=str, default=None, help="Base directory of Megatron repository") parser.add_argument( "--convert_checkpoint_from_megatron_to_transformers", action="store_true", help=( "If True, convert a Megatron checkpoint to a Transformers checkpoint. " "If False, convert a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--load_path", type=str, required=True, help="Path to the checkpoint to convert.", ) parser.add_argument( "--save_path", type=str, required=True, help="Path to the converted checkpoint.", ) parser.add_argument("--print-checkpoint-structure", action="store_true") return parser def add_megatron_checkpoint_args(parser): parser.add_argument( "--target_tensor_model_parallel_size", type=int, default=1, help=( "The tensor model parallel size of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--target_pipeline_model_parallel_size", type=int, default=1, help=( "The pipeline model parallel size of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--target_data_parallel_size", type=int, default=1, help=( "The data parallel size of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--target_params_dtype", type=str, default="fp32", help=( "The dtype of the converted checkpoint. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--make_vocab_size_divisible_by", type=int, default=128, help=( "Pad the vocab size to be divisible by this value. " "This is added for computational efficieny reasons. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) parser.add_argument( "--use_distributed_optimizer", action="store_true", help=( "If True, use the distributed optimizer. " "Only used when converting a Transformers checkpoint to a Megatron checkpoint." ), ) return parser def add_transformers_checkpoint_args(parser): parser.add_argument( "--tokenizer_name", type=str, default=None, help=( "The name of the pre-trained tokenizer to save. " "If not None, the tokenizer will be saved. " "Only used when converting a Megatron checkpoint to a Transformers checkpoint." ), ) parser.add_argument( "--max_shard_size", type=str, default="10GB", help=( "The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size " "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`). " "Only used when converting a Megatron checkpoint to a Transformers checkpoint." ), ) return parser # The simple map of names for "automated" rules. megatron_to_transformers = { "attention.dense": ".attn.c_proj.", "self_attention.dense": ".attn.c_proj.", "mlp.dense_h_to_4h": ".mlp.c_fc.", "mlp.dense_4h_to_h": ".mlp.c_proj.", } transformers_to_megatron = {v[1:-1]: k for k, v in megatron_to_transformers.items()} tensor_parallel_params = [ # megatron-lm layers to merge across tp ranks "self_attention.query_key_value.weight", "self_attention.query_key_value.bias", "self_attention.dense.weight", "mlp.dense_h_to_4h.weight", "mlp.dense_h_to_4h.bias", "mlp.dense_4h_to_h.weight", # deprecated "attention.query_key_value.weight", "attention.query_key_value.bias", "attention.dense.weight", # transformers layers to split across tp ranks "attn.c_attn.weight", "attn.c_attn.bias", "attn.c_proj.weight", "mlp.c_fc.weight", "mlp.c_fc.bias", "mlp.c_proj.weight", ] def recursive_print(name, val, spaces=0): """ Recursively print the structure of a checkpoint. This function is taken from `convert_megatron_gpt2_checkpoint.py` Args: name (str): the name of the current tensor parameter val (Tuple(int)): the shape of the current tensor parameter spaces (int): the number of spaces to print before the output for a nested structure """ # Format the message. if name is None: msg = None else: fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}" msg = fmt.format(name) # Print and recurse (if needed). if isinstance(val, dict): if msg is not None: print(msg) for k in val.keys(): recursive_print(k, val[k], spaces + 2) elif isinstance(val, torch.Tensor): print(msg, ":", val.size()) else: print(msg, ":", val) def megatron_to_transformers_fix_query_key_value_ordering( param, checkpoint_version, num_splits, num_heads, hidden_size ): """ Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] for compatibility with later versions of NVIDIA Megatron-LM. The inverse operation is performed inside Megatron-LM to read checkpoints: https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 If param is the weight tensor of the self-attention block, the returned tensor will have to be transposed one more time to be read by HuggingFace GPT2. This function is taken from `convert_megatron_gpt2_checkpoint.py` Args: param (torch.Tensor): the tensor to permute checkpoint_version (int): the version of the checkpoint. num_splits (int): the number of projections, usually 3 for (Query, Key, Value) num_heads (int): the number of attention heads hidden_size (int): the hidden size per head """ input_shape = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:] param = param.view(*saved_shape) param = param.transpose(0, 2) param = param.transpose(1, 2).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:] param = param.view(*saved_shape) param = param.transpose(0, 1).contiguous() param = param.view(*input_shape) return param def transformers_to_megatron_fix_query_key_value_ordering( param, checkpoint_version, num_splits, num_heads, hidden_size ): """ Permutes layout of param tensor to the one compatible with respective NVIDIA Megatron-LM chekpoint versions. Input is [num_splits * num_heads * hidden_size, :] and output is [num_heads * hidden_size * num_splits, :] for version 1.0 and [num_heads * num_splits * hidden_size, :] for version 2.0 and later. If param is the weight tensor of the self-attention block, the param needs to be already transposed before calling this function. Args: param (torch.Tensor): the tensor to permute checkpoint_version (int): the version of the checkpoint. num_splits (int): the number of projections, usually 3 for (Query, Key, Value) num_heads (int): the number of attention heads hidden_size (int): the hidden size per head """ # Input is [num_splits * num_heads * hidden_size, :] input_shape = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:] param = param.view(*current_shape) param = param.transpose(0, 2) param = param.transpose(1, 2).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:] param = param.view(*current_shape) param = param.transpose(0, 1).contiguous() param = param.view(*input_shape) return param def merge_transformers_sharded_states(path, num_checkpoints): """ Merge sharded checkpoints from transformers into a single checkpoint. Args: path (str): the path to the sharded checkpoints num_checkpoints (int): the number of checkpoints to merge """ state_dict = {} for i in range(1, num_checkpoints + 1): checkpoint_path = os.path.join(path, f"pytorch_model-{i:05d}-of-{num_checkpoints:05d}.bin") current_chunk = torch.load(checkpoint_path, map_location="cpu") state_dict.update(current_chunk) return state_dict def get_megatron_sharded_states(args, tp_size, pp_size, pp_rank): """ Get sharded checkpoints from NVIDIA Megatron-LM checkpoint based on the provided tensor parallel size, pipeline parallel size and pipeline parallel rank. Args: args (argparse.Namespace): the arguments to the script tp_size (int): the tensor parallel size pp_size (int): the pipeline parallel size pp_rank (int): the pipeline parallel rank """ tp_state_dicts = [] for i in range(tp_size): sub_dir_name = f"mp_rank_{i:02d}" if pp_size == 1 else f"mp_rank_{i:02d}_{pp_rank:03d}" checkpoint_name = os.listdir(os.path.join(args.load_path, sub_dir_name))[0] checkpoint_path = os.path.join(args.load_path, sub_dir_name, checkpoint_name) state_dict = torch.load(checkpoint_path, map_location="cpu") tp_state_dicts.append(state_dict) return tp_state_dicts def get_element_from_dict_by_path(d, path): """ Get element from dictionary by path. If element is not present, recursively add empty dictionaries. Args: d (dict): the dictionary to get the element from path (list): the path to the element which is delimited by "." """ path = path.split(".") for k in path: if k not in d: d[k] = {} d = d[k] return d def convert_checkpoint_from_megatron_to_transformers(args): """ Convert NVIDIA Megatron-LM checkpoint to HuggingFace Transformers checkpoint. This handles Megatron checkpoints with different tensor parallelism and pipeline parallelism sizes. It saves the converted checkpoint into shards using HuggingFace Transformers checkpoint sharding functionality. This greatly extends the functionality of `convert_megatron_gpt2_checkpoint.py` Args: args (argparse.Namespace): the arguments to the script """ # Load Megatron-LM checkpoint arguments from the state dict sub_dirs = os.listdir(args.load_path) possible_sub_dirs = ["mp_rank_00", "mp_rank_00_000"] for sub_dir in possible_sub_dirs: if sub_dir in sub_dirs: rank0_checkpoint_name = os.listdir(os.path.join(args.load_path, sub_dir))[0] rank0_checkpoint_path = os.path.join(args.load_path, sub_dir, rank0_checkpoint_name) break print(f"Loading Megatron-LM checkpoint arguments from: {rank0_checkpoint_path}") state_dict = torch.load(rank0_checkpoint_path, map_location="cpu") megatron_args = state_dict.get("args", None) if megatron_args is None: raise ValueError( "Megatron-LM checkpoint does not contain arguments. This utility only supports Megatron-LM checkpoints" " containing all the megatron arguments. This is because it loads all config related to model" " architecture, the tensor and pipeline model parallel size from the checkpoint insead of user having to" " manually specify all the details. Please save Megatron-LM checkpoint along with all the megatron" " arguments to use this utility." ) # Create Transformers GPT2 config from Megatron-LM arguments if megatron_args is not None: if megatron_args.bias_gelu_fusion: activation_function = "gelu_fast" elif megatron_args.openai_gelu: activation_function = "gelu_new" else: activation_function = "gelu" else: # in the very early days this used to be "gelu_new" activation_function = "gelu_new" vocab_size = ( megatron_args.padded_vocab_size if getattr(megatron_args, "orig_vocab_size", None) is None else megatron_args.orig_vocab_size ) print(vocab_size) config = GPT2Config( vocab_size=vocab_size, n_positions=megatron_args.max_position_embeddings, n_embd=megatron_args.hidden_size, n_layer=megatron_args.num_layers, n_head=megatron_args.num_attention_heads, n_inner=megatron_args.ffn_hidden_size, activation_function=activation_function, resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, scale_attn_weights=True, use_cache=True, bos_token_id=vocab_size - 1, eos_token_id=vocab_size - 1, architectures=["GPT2LMHeadModel"], ) output_state_dict = {} checkpoint_version = state_dict.get("checkpoint_version", 0.0) tp_size = megatron_args.tensor_model_parallel_size pp_size = megatron_args.pipeline_model_parallel_size dtype = torch.float32 # The regex to extract layer names. layer_re = re.compile("layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)") # Convert. print("Converting") # Embeddings print("Converting embeddings") tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, 0) # Convert and store the position embeddings. position_embeddings = get_element_from_dict_by_path( tp_state_dicts[0], "model.language_model.embedding.position_embeddings.weight" ) output_state_dict["transformer.wpe.weight"] = position_embeddings.to(dtype) # Convert and store the word embeddings. word_embeddings = torch.cat( [ get_element_from_dict_by_path( tp_state_dicts[tp_rank], "model.language_model.embedding.word_embeddings.weight" ) for tp_rank in range(tp_size) ], dim=0, ) word_embeddings = word_embeddings[:vocab_size].to(dtype) output_state_dict["transformer.wte.weight"] = word_embeddings # Transformer Layers print("Converting transformer layers") # The number of heads. heads = config.n_head # The hidden_size per head. hidden_size_per_head = config.n_embd // config.n_head n_positions = config.n_positions num_layers = config.num_hidden_layers // pp_size for pp_rank in range(pp_size): if pp_size > 0: print(f"Converting pipeline parallel rank {pp_rank}") tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, pp_rank) # The transformer. path = ( "model.language_model.transformer" if "transformer" in get_element_from_dict_by_path(tp_state_dicts[0], "model.language_model").keys() else "model.language_model.encoder" ) # Extract the layers. for key, val in get_element_from_dict_by_path(tp_state_dicts[0], path).items(): # Match the name. m = layer_re.match(key) # Stop if that's not a layer if m is None: break # The index of the layer. layer_idx = int(m.group(1)) + pp_rank * num_layers # The name of the operation. op_name = m.group(2) # Is it a weight or a bias? weight_or_bias = m.group(3) # The name of the layer. layer_name = f"transformer.h.{layer_idx}" if op_name + "." + weight_or_bias not in tensor_parallel_params: params = val.to(dtype) else: dim = 1 if op_name in ["self_attention.dense", "mlp.dense_4h_to_h", "attention.dense"] else 0 params = torch.cat( [val] + [ get_element_from_dict_by_path(tp_state_dicts[tp_rank], f"{path}")[key] for tp_rank in range(1, tp_size) ], dim=dim, ).to(dtype) # For layernorm(s), simply store the layer norm. if op_name.endswith("layernorm"): ln_name = "ln_1" if op_name.startswith("input") else "ln_2" output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = params # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=dtype)).view( 1, 1, n_positions, n_positions ) output_state_dict[layer_name + ".attn.bias"] = causal_mask # Insert a "dummy" tensor for masked_bias. masked_bias = torch.tensor(-1e4, dtype=dtype) output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias out_val = megatron_to_transformers_fix_query_key_value_ordering( params, checkpoint_version, 3, heads, hidden_size_per_head, ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. out_val = out_val.transpose(0, 1).contiguous() # Store. output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": out_val = megatron_to_transformers_fix_query_key_value_ordering( params, checkpoint_version, 3, heads, hidden_size_per_head ) # Store. No change of shape. output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val # Transpose the weights. elif weight_or_bias == "weight": out_name = megatron_to_transformers[op_name] output_state_dict[layer_name + out_name + "weight"] = params.transpose(0, 1) # Copy the bias. elif weight_or_bias == "bias": out_name = megatron_to_transformers[op_name] output_state_dict[layer_name + out_name + "bias"] = params if config.n_layer != (layer_idx + 1): raise ValueError(f"Expected {config.n_layer} layers but found {layer_idx + 1}") # The final layernorm. print("Converting final layernorm") params = get_element_from_dict_by_path(tp_state_dicts[0], str(path)) output_state_dict["transformer.ln_f.weight"] = params["final_layernorm.weight"].to(dtype) output_state_dict["transformer.ln_f.bias"] = params["final_layernorm.bias"].to(dtype) # For LM head, transformers' wants the matrix to weight embeddings. print("Converting LM head") output_state_dict["lm_head.weight"] = word_embeddings.to(dtype) # It should be done! print("Conversion from Megatron-LM to Transformers is done!") # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(None, output_state_dict) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if args.tokenizer_name is None: tokenizer_name = "gpt2" else: tokenizer_name = args.tokenizer_name tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) tokenizer_class = type(tokenizer).__name__ config.tokenizer_class = tokenizer_class # Store the config to file. print("Saving config") config.save_pretrained(args.save_path) # Save tokenizer based on args if args.tokenizer_name is not None: print(f"Adding {tokenizer_class} tokenizer files") tokenizer.save_pretrained(args.save_path) # Store the state_dict to file. max_shard_size = int(args.max_shard_size) if args.max_shard_size.isdigit() else args.max_shard_size shards, index = shard_checkpoint(output_state_dict, max_shard_size=max_shard_size) # Save the model for shard_file, shard in shards.items(): torch.save(shard, os.path.join(args.save_path, shard_file)) if index is None: print(f"Model weights saved in {os.path.join(args.save_path, WEIGHTS_NAME)}") else: save_index_file = os.path.join(args.save_path, WEIGHTS_INDEX_NAME) # Save the index as well with open(save_index_file, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) print( f"The model is bigger than the maximum size per checkpoint ({args.max_shard_size}) and is going to be " f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the " f"index located at {save_index_file}." ) def convert_checkpoint_from_transformers_to_megatron(args): """ Convert a checkpoint from HuggingFace Transformers to Megatron-LM. This allows converted checkpoints with variable tensor parallelism and pipeline parallelism sizes. It takes as input a checkpoint from HuggingFace Transformers which can have multiple shards. Args: args (argparse.Namespace): the arguments to the script """ os.makedirs(args.save_path, exist_ok=True) # Search in directory above this sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) if args.megatron_path is not None: sys.path.insert(0, args.megatron_path) try: from megatron.tokenizer.tokenizer import _vocab_size_with_padding except ModuleNotFoundError: print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") exit(1) # load the transformers model state dict and config sub_dirs = [x for x in os.listdir(args.load_path) if x.startswith("pytorch_model")] if len(sub_dirs) == 1: checkpoint_name = "pytorch_model.bin" state_dict = torch.load(os.path.join(args.load_path, checkpoint_name), map_location="cpu") else: num_checkpoints = len(sub_dirs) - 1 state_dict = merge_transformers_sharded_states(args.load_path, num_checkpoints) config = GPT2Config.from_pretrained(args.load_path) # Saving the tracker file tracker_filepath = os.path.join(args.save_path, "latest_checkpointed_iteration.txt") with open(tracker_filepath, "w") as f: f.write("release") # create `release` dir in args.load_path release_dir = os.path.join(args.save_path, "release") os.makedirs(release_dir, exist_ok=True) # megatron args megatron_args = { "orig_vocab_size": config.vocab_size, "max_position_embeddings": config.n_positions, "hidden_size": config.n_embd, "num_layers": config.n_layer, "num_attention_heads": config.n_head, "ffn_hidden_size": config.n_inner, "tensor_model_parallel_size": args.target_tensor_model_parallel_size, "pipeline_model_parallel_size": args.target_pipeline_model_parallel_size, "data_parallel_size": args.target_data_parallel_size, "make_vocab_size_divisible_by": args.make_vocab_size_divisible_by, "rank": 0, "tokenizer_type": "GPT2BPETokenizer", } if config.activation_function == "gelu": megatron_args["bias_gelu_fusion"] = False megatron_args["openai_gelu"] = False elif config.activation_function == "gelu_fast": megatron_args["bias_gelu_fusion"] = True megatron_args["openai_gelu"] = False elif config.activation_function == "gelu_new": megatron_args["bias_gelu_fusion"] = False megatron_args["openai_gelu"] = True margs = types.SimpleNamespace() for k, v in megatron_args.items(): setattr(margs, k, v) # params dtype if args.target_params_dtype == "fp16": dtype = torch.float16 elif args.target_params_dtype == "bf16": dtype = torch.bfloat16 else: dtype = torch.float32 setattr(margs, "params_dtype", dtype) # save dummy optim state dict dummy_optim_state_dict = {} dummy_optim_state_dict["optimizer"] = { "step": 0, "param_groups": [ { "lr": 0.0, "beta1": 0.0, "beta2": 0.0, "eps": 0.0, "weight_decay": 0.0, "correct_bias": False, "params": [], } ], } if args.use_distributed_optimizer: for i in range(args.target_pipeline_model_parallel_size): for j in range(args.target_tensor_model_parallel_size): for k in range(args.target_data_parallel_size): if args.target_pipeline_model_parallel_size == 1: checkpoint_dir = f"mp_rank_{i:02d}_{k:03d}" else: checkpoint_dir = f"mp_rank_{i:02d}_{j:03d}_{k:03d}" checkpoint_dir = os.path.join(release_dir, checkpoint_dir) os.makedirs(checkpoint_dir, exist_ok=True) torch.save( dummy_optim_state_dict, os.path.join(checkpoint_dir, "optim.pt"), ) # Convert. print("Converting") output_state_dict = [] for i in range(args.target_tensor_model_parallel_size): output_state_dict.append({}) # Embedding layer print("converting embedding layer") pos_embedding = state_dict["transformer.wpe.weight"].to(dtype) word_embedding = state_dict["transformer.wte.weight"].to(dtype) orig_vocab_size = config.vocab_size padded_vocab_size = _vocab_size_with_padding(orig_vocab_size, margs) setattr(margs, "padded_vocab_size", padded_vocab_size) # Cut out extra padding we don't need if orig_vocab_size > padded_vocab_size: full_word_embed = word_embedding[0:padded_vocab_size, :] # Expanding embedding to larger size by replicating final entry elif orig_vocab_size < padded_vocab_size: padding_size = padded_vocab_size - orig_vocab_size full_word_embed = torch.cat((word_embedding, word_embedding[-1].unsqueeze(0).expand(padding_size, -1))) # Same size! else: full_word_embed = word_embedding # Split into new tensor model parallel sizes out_word_embed = torch.chunk(full_word_embed, args.target_tensor_model_parallel_size, dim=0) for i in range(args.target_tensor_model_parallel_size): pos_emb_dict = get_element_from_dict_by_path( output_state_dict[i], "model.language_model.embedding.position_embeddings" ) pos_emb_dict["weight"] = pos_embedding word_emb_dict = get_element_from_dict_by_path( output_state_dict[i], "model.language_model.embedding.word_embeddings" ) word_emb_dict["weight"] = out_word_embed[i] # Transformer layers print("converting transformer layers") if config.num_hidden_layers % args.target_tensor_model_parallel_size != 0: raise ValueError( f"Number of layers ({config.num_hidden_layers}) must be divisible by number of tensor parallelism" f" ({args.target_tensor_model_parallel_size})" ) num_layers = config.num_hidden_layers // args.target_pipeline_model_parallel_size layer_re = re.compile("transformer.h\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)") # The number of heads. heads = config.n_head # The hidden_size per head. hidden_size_per_head = config.n_embd // config.n_head for pp_rank in range(args.target_pipeline_model_parallel_size): layer_offset = pp_rank * num_layers if pp_rank > 0: output_state_dict = [] for i in range(args.target_tensor_model_parallel_size): output_state_dict.append({}) for layer in range(num_layers): pp_layer_id = layer + layer_offset layers_to_copy = [ layer_name for layer_name in state_dict.keys() if layer_name.startswith(f"transformer.h.{pp_layer_id}.") ] for layer_name in layers_to_copy: m = layer_re.match(layer_name) # Stop if that's not a layer if m is None: break # The index of the layer. _ = int(m.group(1)) # The name of the operation. op_name = m.group(2) # Is it a weight or a bias? weight_or_bias = m.group(3) params = state_dict[layer_name].to(dtype) # handle layernorm if op_name.startswith("ln"): out_name = "input_layernorm" if op_name.endswith("1") else "post_attention_layernorm" layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}" # handle attention K, V, Q weights elif op_name.startswith("attn.c_attn") and weight_or_bias == "weight": # transformers stores D X (3*D) but Megatron-LM expects (3*D) X D. params = params.transpose(0, 1).contiguous() params = transformers_to_megatron_fix_query_key_value_ordering( params, 3.0, 3, heads, hidden_size_per_head, ) layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}" # handle attention K, V, Q bias elif op_name.startswith("attn.c_attn") and weight_or_bias == "bias": params = transformers_to_megatron_fix_query_key_value_ordering( params, 3.0, 3, heads, hidden_size_per_head, ) layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}" # handle attention and mlp weights elif weight_or_bias == "weight": out_name = transformers_to_megatron.get(op_name, None) if out_name is None: continue params = params.transpose(0, 1) layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}" # handle attention and mlp bias elif weight_or_bias == "bias": out_name = transformers_to_megatron.get(op_name, None) if out_name is None: continue layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}" # skip else: continue if op_name + "." + weight_or_bias in tensor_parallel_params: dim = 1 if op_name in ["attn.c_proj", "mlp.c_proj"] else 0 params = torch.chunk(params, args.target_tensor_model_parallel_size, dim=dim) for i in range(args.target_tensor_model_parallel_size): params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder") params_dict[layer_name] = ( params[i] if (op_name + "." + weight_or_bias in tensor_parallel_params) else params ) if pp_rank == args.target_pipeline_model_parallel_size - 1: # handle final layernorm for weight_or_bias in ["weight", "bias"]: params = state_dict[f"transformer.ln_f.{weight_or_bias}"].to(dtype) layer_name = f"final_layernorm.{weight_or_bias}" for i in range(args.target_tensor_model_parallel_size): params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder") params_dict[layer_name] = params # add the LM head for i in range(args.target_tensor_model_parallel_size): params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.word_embeddings_for_head") params_dict["weight"] = out_word_embed[i] # saving the state dict as per the tp_rank and pp_rank for tp_rank in range(args.target_tensor_model_parallel_size): output_state_dict[tp_rank]["checkpoint_version"] = 3.0 output_state_dict[tp_rank]["args"] = margs checkpoint_dir = ( f"mp_rank_{tp_rank:02d}" if args.target_pipeline_model_parallel_size == 1 else f"mp_rank_{tp_rank:02d}_{pp_rank:03d}" ) if args.use_distributed_optimizer: checkpoint_name = "model_rng.pt" else: checkpoint_name = "model_optim_rng.pt" output_state_dict[tp_rank]["optimizer"] = dummy_optim_state_dict["optimizer"] checkpoint_dir = os.path.join(release_dir, checkpoint_dir) os.makedirs(checkpoint_dir, exist_ok=True) checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name) if args.print_checkpoint_structure: print( f"Checkpoint structure of model state dict shard belonging to TP rank {tp_rank} and PP rank" f" {pp_rank}:" ) recursive_print(None, output_state_dict[tp_rank]) torch.save(output_state_dict[tp_rank], checkpoint_path) def main(): parser = argparse.ArgumentParser() parser = add_checkpointing_args(parser) parser = add_megatron_checkpoint_args(parser) parser = add_transformers_checkpoint_args(parser) args = parser.parse_args() if args.convert_checkpoint_from_megatron_to_transformers: convert_checkpoint_from_megatron_to_transformers(args) else: convert_checkpoint_from_transformers_to_megatron(args) if __name__ == "__main__": main()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/mpnet/__init__.py

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/x_clip/test_modeling_x_clip.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch XCLIP model. """ import inspect import os import tempfile import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import XCLIPConfig, XCLIPTextConfig, XCLIPVisionConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from torch import nn from transformers import XCLIPModel, XCLIPTextModel, XCLIPVisionModel from transformers.models.x_clip.modeling_x_clip import XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from transformers import XCLIPProcessor class XCLIPVisionModelTester: def __init__( self, parent, batch_size=8, image_size=30, patch_size=2, num_channels=3, num_frames=8, # important; the batch size * time must be divisible by the number of frames is_training=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, mit_hidden_size=64, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_frames = num_frames self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.mit_hidden_size = mit_hidden_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor( [self.batch_size * self.num_frames, self.num_channels, self.image_size, self.image_size] ) config = self.get_config() return config, pixel_values def get_config(self): return XCLIPVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_frames=self.num_frames, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, mit_hidden_size=self.mit_hidden_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = XCLIPVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size * self.num_frames, num_patches + 1, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_frames, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class XCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as X-CLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (XCLIPVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = XCLIPVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=XCLIPVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="X-CLIP does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="XCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="XCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XCLIPVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue print("Model class:", model_class) config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # we add 1 here due to the special message token in X-CLIP's vision encoder seq_len = getattr(self.model_tester, "seq_length", None) + 1 encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(outputs.attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length], ) @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # some params shouldn't be scattered by nn.DataParallel # so just remove them if they are present. blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"] for k in blacklist_non_batched_params: inputs_dict.pop(k, None) # move input tensors to cuda:O for k, v in inputs_dict.items(): if torch.is_tensor(v): inputs_dict[k] = v.to(0) for model_class in self.all_model_classes: model = model_class(config=config) model.to(0) model.eval() # Wrap model in nn.DataParallel model = nn.DataParallel(model) with torch.no_grad(): test = self._prepare_for_class(inputs_dict, model_class) for k, v in test.items(): if isinstance(v, torch.Tensor): print(k, v.shape) else: print(k, v) _ = model(**self._prepare_for_class(inputs_dict, model_class)) class XCLIPTextModelTester: def __init__( self, parent, batch_size=8, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return XCLIPTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = XCLIPTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class XCLIPTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (XCLIPTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = XCLIPTextModelTester(self) self.config_tester = ConfigTester(self, config_class=XCLIPTextConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="X-CLIP does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="XCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="XCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XCLIPTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class XCLIPModelTester: def __init__( self, parent, text_kwargs=None, vision_kwargs=None, projection_dim=64, mit_hidden_size=64, is_training=True, ): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.projection_dim = projection_dim self.mit_hidden_size = mit_hidden_size self.text_model_tester = XCLIPTextModelTester(parent, **text_kwargs) self.vision_model_tester = XCLIPVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, _ = self.vision_model_tester.prepare_config_and_inputs() pixel_values = floats_tensor( [ self.vision_model_tester.batch_size, self.vision_model_tester.num_frames, self.vision_model_tester.num_channels, self.vision_model_tester.image_size, self.vision_model_tester.image_size, ] ) config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return XCLIPConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=self.projection_dim, ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = XCLIPModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_video.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size), ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size), ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_torch class XCLIPModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (XCLIPModel,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_torchscript = False maxdiff = None def setUp(self): self.model_tester = XCLIPModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="XCLIPModel does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="XCLIPModel does not support feedforward chunking") def test_feed_forward_chunking(self): pass # override as the `logit_scale`, `prompts_generator.alpha` parameters require special treatment def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif name == "prompts_generator.alpha": self.assertAlmostEqual(param.data.mean().item(), model.config.prompt_alpha) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # X-CLIP needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save XCLIPConfig and check if we can load XCLIPVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = XCLIPVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save XCLIPConfig and check if we can load XCLIPTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = XCLIPTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XCLIPModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on a spaghetti video def prepare_video(): file = hf_hub_download( repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_8_frames.npy", repo_type="dataset" ) video = np.load(file) return list(video) @require_vision @require_torch class XCLIPModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "microsoft/xclip-base-patch32" model = XCLIPModel.from_pretrained(model_name).to(torch_device) processor = XCLIPProcessor.from_pretrained(model_name) video = prepare_video() inputs = processor( text=["playing sports", "eating spaghetti", "go shopping"], videos=video, return_tensors="pt", padding=True ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_video.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[14.0181, 20.2771, 14.4776]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_video, expected_logits, atol=1e-3))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch XCLIP model. """ import inspect import os import tempfile import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import XCLIPConfig, XCLIPTextConfig, XCLIPVisionConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from torch import nn from transformers import XCLIPModel, XCLIPTextModel, XCLIPVisionModel from transformers.models.x_clip.modeling_x_clip import XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from transformers import XCLIPProcessor class XCLIPVisionModelTester: def __init__( self, parent, batch_size=8, image_size=30, patch_size=2, num_channels=3, num_frames=8, # important; the batch size * time must be divisible by the number of frames is_training=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, mit_hidden_size=64, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_frames = num_frames self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.mit_hidden_size = mit_hidden_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor( [self.batch_size * self.num_frames, self.num_channels, self.image_size, self.image_size] ) config = self.get_config() return config, pixel_values def get_config(self): return XCLIPVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_frames=self.num_frames, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, mit_hidden_size=self.mit_hidden_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = XCLIPVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size * self.num_frames, num_patches + 1, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_frames, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class XCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as X-CLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (XCLIPVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = XCLIPVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=XCLIPVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="X-CLIP does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="XCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="XCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XCLIPVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue print("Model class:", model_class) config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # we add 1 here due to the special message token in X-CLIP's vision encoder seq_len = getattr(self.model_tester, "seq_length", None) + 1 encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(outputs.attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length], ) @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # some params shouldn't be scattered by nn.DataParallel # so just remove them if they are present. blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"] for k in blacklist_non_batched_params: inputs_dict.pop(k, None) # move input tensors to cuda:O for k, v in inputs_dict.items(): if torch.is_tensor(v): inputs_dict[k] = v.to(0) for model_class in self.all_model_classes: model = model_class(config=config) model.to(0) model.eval() # Wrap model in nn.DataParallel model = nn.DataParallel(model) with torch.no_grad(): test = self._prepare_for_class(inputs_dict, model_class) for k, v in test.items(): if isinstance(v, torch.Tensor): print(k, v.shape) else: print(k, v) _ = model(**self._prepare_for_class(inputs_dict, model_class)) class XCLIPTextModelTester: def __init__( self, parent, batch_size=8, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return XCLIPTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = XCLIPTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class XCLIPTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (XCLIPTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = XCLIPTextModelTester(self) self.config_tester = ConfigTester(self, config_class=XCLIPTextConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="X-CLIP does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="XCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="XCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XCLIPTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class XCLIPModelTester: def __init__( self, parent, text_kwargs=None, vision_kwargs=None, projection_dim=64, mit_hidden_size=64, is_training=True, ): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.projection_dim = projection_dim self.mit_hidden_size = mit_hidden_size self.text_model_tester = XCLIPTextModelTester(parent, **text_kwargs) self.vision_model_tester = XCLIPVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, _ = self.vision_model_tester.prepare_config_and_inputs() pixel_values = floats_tensor( [ self.vision_model_tester.batch_size, self.vision_model_tester.num_frames, self.vision_model_tester.num_channels, self.vision_model_tester.image_size, self.vision_model_tester.image_size, ] ) config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return XCLIPConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=self.projection_dim, ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = XCLIPModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_video.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size), ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size), ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_torch class XCLIPModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (XCLIPModel,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_torchscript = False maxdiff = None def setUp(self): self.model_tester = XCLIPModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="XCLIPModel does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="XCLIPModel does not support feedforward chunking") def test_feed_forward_chunking(self): pass # override as the `logit_scale`, `prompts_generator.alpha` parameters require special treatment def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif name == "prompts_generator.alpha": self.assertAlmostEqual(param.data.mean().item(), model.config.prompt_alpha) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # X-CLIP needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save XCLIPConfig and check if we can load XCLIPVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = XCLIPVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save XCLIPConfig and check if we can load XCLIPTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = XCLIPTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XCLIPModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on a spaghetti video def prepare_video(): file = hf_hub_download( repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_8_frames.npy", repo_type="dataset" ) video = np.load(file) return list(video) @require_vision @require_torch class XCLIPModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "microsoft/xclip-base-patch32" model = XCLIPModel.from_pretrained(model_name).to(torch_device) processor = XCLIPProcessor.from_pretrained(model_name) video = prepare_video() inputs = processor( text=["playing sports", "eating spaghetti", "go shopping"], videos=video, return_tensors="pt", padding=True ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_video.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[14.0181, 20.2771, 14.4776]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_video, expected_logits, atol=1e-3))

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/utils/test_model_output.py

# coding=utf-8 # Copyright 2020 The Hugging Face Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from dataclasses import dataclass from typing import Optional from transformers.utils import ModelOutput @dataclass class ModelOutputTest(ModelOutput): a: float b: Optional[float] = None c: Optional[float] = None class ModelOutputTester(unittest.TestCase): def test_get_attributes(self): x = ModelOutputTest(a=30) self.assertEqual(x.a, 30) self.assertIsNone(x.b) self.assertIsNone(x.c) with self.assertRaises(AttributeError): _ = x.d def test_index_with_ints_and_slices(self): x = ModelOutputTest(a=30, b=10) self.assertEqual(x[0], 30) self.assertEqual(x[1], 10) self.assertEqual(x[:2], (30, 10)) self.assertEqual(x[:], (30, 10)) x = ModelOutputTest(a=30, c=10) self.assertEqual(x[0], 30) self.assertEqual(x[1], 10) self.assertEqual(x[:2], (30, 10)) self.assertEqual(x[:], (30, 10)) def test_index_with_strings(self): x = ModelOutputTest(a=30, b=10) self.assertEqual(x["a"], 30) self.assertEqual(x["b"], 10) with self.assertRaises(KeyError): _ = x["c"] x = ModelOutputTest(a=30, c=10) self.assertEqual(x["a"], 30) self.assertEqual(x["c"], 10) with self.assertRaises(KeyError): _ = x["b"] def test_dict_like_properties(self): x = ModelOutputTest(a=30) self.assertEqual(list(x.keys()), ["a"]) self.assertEqual(list(x.values()), [30]) self.assertEqual(list(x.items()), [("a", 30)]) self.assertEqual(list(x), ["a"]) x = ModelOutputTest(a=30, b=10) self.assertEqual(list(x.keys()), ["a", "b"]) self.assertEqual(list(x.values()), [30, 10]) self.assertEqual(list(x.items()), [("a", 30), ("b", 10)]) self.assertEqual(list(x), ["a", "b"]) x = ModelOutputTest(a=30, c=10) self.assertEqual(list(x.keys()), ["a", "c"]) self.assertEqual(list(x.values()), [30, 10]) self.assertEqual(list(x.items()), [("a", 30), ("c", 10)]) self.assertEqual(list(x), ["a", "c"]) with self.assertRaises(Exception): x = x.update({"d": 20}) with self.assertRaises(Exception): del x["a"] with self.assertRaises(Exception): _ = x.pop("a") with self.assertRaises(Exception): _ = x.setdefault("d", 32) def test_set_attributes(self): x = ModelOutputTest(a=30) x.a = 10 self.assertEqual(x.a, 10) self.assertEqual(x["a"], 10) def test_set_keys(self): x = ModelOutputTest(a=30) x["a"] = 10 self.assertEqual(x.a, 10) self.assertEqual(x["a"], 10) def test_instantiate_from_dict(self): x = ModelOutputTest({"a": 30, "b": 10}) self.assertEqual(list(x.keys()), ["a", "b"]) self.assertEqual(x.a, 30) self.assertEqual(x.b, 10)

# coding=utf-8 # Copyright 2020 The Hugging Face Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from dataclasses import dataclass from typing import Optional from transformers.utils import ModelOutput @dataclass class ModelOutputTest(ModelOutput): a: float b: Optional[float] = None c: Optional[float] = None class ModelOutputTester(unittest.TestCase): def test_get_attributes(self): x = ModelOutputTest(a=30) self.assertEqual(x.a, 30) self.assertIsNone(x.b) self.assertIsNone(x.c) with self.assertRaises(AttributeError): _ = x.d def test_index_with_ints_and_slices(self): x = ModelOutputTest(a=30, b=10) self.assertEqual(x[0], 30) self.assertEqual(x[1], 10) self.assertEqual(x[:2], (30, 10)) self.assertEqual(x[:], (30, 10)) x = ModelOutputTest(a=30, c=10) self.assertEqual(x[0], 30) self.assertEqual(x[1], 10) self.assertEqual(x[:2], (30, 10)) self.assertEqual(x[:], (30, 10)) def test_index_with_strings(self): x = ModelOutputTest(a=30, b=10) self.assertEqual(x["a"], 30) self.assertEqual(x["b"], 10) with self.assertRaises(KeyError): _ = x["c"] x = ModelOutputTest(a=30, c=10) self.assertEqual(x["a"], 30) self.assertEqual(x["c"], 10) with self.assertRaises(KeyError): _ = x["b"] def test_dict_like_properties(self): x = ModelOutputTest(a=30) self.assertEqual(list(x.keys()), ["a"]) self.assertEqual(list(x.values()), [30]) self.assertEqual(list(x.items()), [("a", 30)]) self.assertEqual(list(x), ["a"]) x = ModelOutputTest(a=30, b=10) self.assertEqual(list(x.keys()), ["a", "b"]) self.assertEqual(list(x.values()), [30, 10]) self.assertEqual(list(x.items()), [("a", 30), ("b", 10)]) self.assertEqual(list(x), ["a", "b"]) x = ModelOutputTest(a=30, c=10) self.assertEqual(list(x.keys()), ["a", "c"]) self.assertEqual(list(x.values()), [30, 10]) self.assertEqual(list(x.items()), [("a", 30), ("c", 10)]) self.assertEqual(list(x), ["a", "c"]) with self.assertRaises(Exception): x = x.update({"d": 20}) with self.assertRaises(Exception): del x["a"] with self.assertRaises(Exception): _ = x.pop("a") with self.assertRaises(Exception): _ = x.setdefault("d", 32) def test_set_attributes(self): x = ModelOutputTest(a=30) x.a = 10 self.assertEqual(x.a, 10) self.assertEqual(x["a"], 10) def test_set_keys(self): x = ModelOutputTest(a=30) x["a"] = 10 self.assertEqual(x.a, 10) self.assertEqual(x["a"], 10) def test_instantiate_from_dict(self): x = ModelOutputTest({"a": 30, "b": 10}) self.assertEqual(list(x.keys()), ["a", "b"]) self.assertEqual(x.a, 30) self.assertEqual(x.b, 10)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/unispeech/__init__.py

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/big_bird/convert_bigbird_original_tf_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert BigBird checkpoint.""" import argparse from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird from transformers.utils import logging logging.set_verbosity_info() def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, pytorch_dump_path, is_trivia_qa): # Initialise PyTorch model config = BigBirdConfig.from_json_file(big_bird_config_file) print(f"Building PyTorch model from configuration: {config}") if is_trivia_qa: model = BigBirdForQuestionAnswering(config) else: model = BigBirdForPreTraining(config) # Load weights from tf checkpoint load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=is_trivia_qa) # Save pytorch-model print(f"Save PyTorch model to {pytorch_dump_path}") model.save_pretrained(pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--big_bird_config_file", default=None, type=str, required=True, help=( "The config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture." ), ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head." ) args = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert BigBird checkpoint.""" import argparse from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird from transformers.utils import logging logging.set_verbosity_info() def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, pytorch_dump_path, is_trivia_qa): # Initialise PyTorch model config = BigBirdConfig.from_json_file(big_bird_config_file) print(f"Building PyTorch model from configuration: {config}") if is_trivia_qa: model = BigBirdForQuestionAnswering(config) else: model = BigBirdForPreTraining(config) # Load weights from tf checkpoint load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=is_trivia_qa) # Save pytorch-model print(f"Save PyTorch model to {pytorch_dump_path}") model.save_pretrained(pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--big_bird_config_file", default=None, type=str, required=True, help=( "The config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture." ), ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head." ) args = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/optimization/test_optimization_tf.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf if is_tf_available(): import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from transformers import GradientAccumulator, create_optimizer @require_tf class OptimizationFTest(unittest.TestCase): def assertListAlmostEqual(self, list1, list2, tol): self.assertEqual(len(list1), len(list2)) for a, b in zip(list1, list2): self.assertAlmostEqual(a, b, delta=tol) def testGradientAccumulator(self): accumulator = GradientAccumulator() accumulator([tf.constant([1.0, 2.0])]) accumulator([tf.constant([-2.0, 1.0])]) accumulator([tf.constant([-1.0, 2.0])]) with self.assertRaises(ValueError): accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])]) self.assertEqual(accumulator.step, 3) self.assertEqual(len(accumulator.gradients), 1) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [-2.0, 5.0], tol=1e-2) accumulator.reset() self.assertEqual(accumulator.step, 0) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [0.0, 0.0], tol=1e-2) def testGradientAccumulatorDistributionStrategy(self): context._context = None ops.enable_eager_execution_internal() physical_devices = tf.config.list_physical_devices("CPU") if len(physical_devices) == 1: tf.config.set_logical_device_configuration( physical_devices[0], [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] ) devices = tf.config.list_logical_devices(device_type="CPU") strategy = tf.distribute.MirroredStrategy(devices=devices[:2]) with strategy.scope(): accumulator = GradientAccumulator() variable = tf.Variable([4.0, 3.0]) optimizer, _ = create_optimizer(5e-5, 10, 5) gradient_placeholder = tf.Variable([0.0, 0.0], trainable=False) def accumulate_on_replica(gradient): accumulator([gradient]) def apply_on_replica(): optimizer.apply_gradients(list(zip(accumulator.gradients, [variable]))) @tf.function def accumulate(grad1, grad2): with strategy.scope(): local_variables = strategy.experimental_local_results(gradient_placeholder) local_variables[0].assign(grad1) local_variables[1].assign(grad2) strategy.run(accumulate_on_replica, args=(gradient_placeholder,)) @tf.function def apply_grad(): with strategy.scope(): strategy.run(apply_on_replica) def _check_local_values(grad1, grad2): values = strategy.experimental_local_results(accumulator._gradients[0]) self.assertListAlmostEqual(values[0].value(), grad1, tol=1e-2) self.assertListAlmostEqual(values[1].value(), grad2, tol=1e-2) accumulate([1.0, 2.0], [-1.0, 1.0]) accumulate([3.0, -1.0], [-1.0, -1.0]) accumulate([-2.0, 2.0], [3.0, -2.0]) self.assertEqual(accumulator.step, 3) _check_local_values([2.0, 3.0], [1.0, -2.0]) apply_grad() self.assertListAlmostEqual(variable.value(), [4.0, 3.0], tol=1e-2) accumulator.reset() self.assertEqual(accumulator.step, 0) _check_local_values([0.0, 0.0], [0.0, 0.0])

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf if is_tf_available(): import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from transformers import GradientAccumulator, create_optimizer @require_tf class OptimizationFTest(unittest.TestCase): def assertListAlmostEqual(self, list1, list2, tol): self.assertEqual(len(list1), len(list2)) for a, b in zip(list1, list2): self.assertAlmostEqual(a, b, delta=tol) def testGradientAccumulator(self): accumulator = GradientAccumulator() accumulator([tf.constant([1.0, 2.0])]) accumulator([tf.constant([-2.0, 1.0])]) accumulator([tf.constant([-1.0, 2.0])]) with self.assertRaises(ValueError): accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])]) self.assertEqual(accumulator.step, 3) self.assertEqual(len(accumulator.gradients), 1) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [-2.0, 5.0], tol=1e-2) accumulator.reset() self.assertEqual(accumulator.step, 0) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [0.0, 0.0], tol=1e-2) def testGradientAccumulatorDistributionStrategy(self): context._context = None ops.enable_eager_execution_internal() physical_devices = tf.config.list_physical_devices("CPU") if len(physical_devices) == 1: tf.config.set_logical_device_configuration( physical_devices[0], [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] ) devices = tf.config.list_logical_devices(device_type="CPU") strategy = tf.distribute.MirroredStrategy(devices=devices[:2]) with strategy.scope(): accumulator = GradientAccumulator() variable = tf.Variable([4.0, 3.0]) optimizer, _ = create_optimizer(5e-5, 10, 5) gradient_placeholder = tf.Variable([0.0, 0.0], trainable=False) def accumulate_on_replica(gradient): accumulator([gradient]) def apply_on_replica(): optimizer.apply_gradients(list(zip(accumulator.gradients, [variable]))) @tf.function def accumulate(grad1, grad2): with strategy.scope(): local_variables = strategy.experimental_local_results(gradient_placeholder) local_variables[0].assign(grad1) local_variables[1].assign(grad2) strategy.run(accumulate_on_replica, args=(gradient_placeholder,)) @tf.function def apply_grad(): with strategy.scope(): strategy.run(apply_on_replica) def _check_local_values(grad1, grad2): values = strategy.experimental_local_results(accumulator._gradients[0]) self.assertListAlmostEqual(values[0].value(), grad1, tol=1e-2) self.assertListAlmostEqual(values[1].value(), grad2, tol=1e-2) accumulate([1.0, 2.0], [-1.0, 1.0]) accumulate([3.0, -1.0], [-1.0, -1.0]) accumulate([-2.0, 2.0], [3.0, -2.0]) self.assertEqual(accumulator.step, 3) _check_local_values([2.0, 3.0], [1.0, -2.0]) apply_grad() self.assertListAlmostEqual(variable.value(), [4.0, 3.0], tol=1e-2) accumulator.reset() self.assertEqual(accumulator.step, 0) _check_local_values([0.0, 0.0], [0.0, 0.0])

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/jax-projects/big_bird/evaluate.py

from datasets import load_from_disk import jax import jax.numpy as jnp from bigbird_flax import FlaxBigBirdForNaturalQuestions from transformers import BigBirdTokenizerFast CATEGORY_MAPPING = {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"} PUNCTUATION_SET_TO_EXCLUDE = set("".join(["‘", "’", "´", "`", ".", ",", "-", '"'])) def get_sub_answers(answers, begin=0, end=None): return [" ".join(x.split(" ")[begin:end]) for x in answers if len(x.split(" ")) > 1] def expand_to_aliases(given_answers, make_sub_answers=False): if make_sub_answers: # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word # *e.g.* if the correct answer contains a prefix such as "the", or "a" given_answers = ( given_answers + get_sub_answers(given_answers, begin=1) + get_sub_answers(given_answers, end=-1) ) answers = [] for answer in given_answers: alias = answer.replace("_", " ").lower() alias = "".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias) answers.append(" ".join(alias.split()).strip()) return set(answers) def get_best_valid_start_end_idx(start_scores, end_scores, top_k=1, max_size=100): best_start_scores, best_start_idx = jax.lax.top_k(start_scores, top_k) best_end_scores, best_end_idx = jax.lax.top_k(end_scores, top_k) widths = best_end_idx[:, None] - best_start_idx[None, :] mask = jnp.logical_or(widths < 0, widths > max_size) scores = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask) best_score = jnp.argmax(scores).item() return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k] def format_dataset(sample): question = sample["question"]["text"] context = sample["document"]["tokens"]["token"] is_html = sample["document"]["tokens"]["is_html"] long_answers = sample["annotations"]["long_answer"] short_answers = sample["annotations"]["short_answers"] context_string = " ".join([context[i] for i in range(len(context)) if not is_html[i]]) # 0 - No ; 1 - Yes for answer in sample["annotations"]["yes_no_answer"]: if answer == 0 or answer == 1: return { "question": question, "context": context_string, "short": [], "long": [], "category": "no" if answer == 0 else "yes", } short_targets = [] for s in short_answers: short_targets.extend(s["text"]) short_targets = list(set(short_targets)) long_targets = [] for s in long_answers: if s["start_token"] == -1: continue answer = context[s["start_token"] : s["end_token"]] html = is_html[s["start_token"] : s["end_token"]] new_answer = " ".join([answer[i] for i in range(len(answer)) if not html[i]]) if new_answer not in long_targets: long_targets.append(new_answer) category = "long_short" if len(short_targets + long_targets) > 0 else "null" return { "question": question, "context": context_string, "short": short_targets, "long": long_targets, "category": category, } def main(): dataset = load_from_disk("natural-questions-validation") dataset = dataset.map(format_dataset).remove_columns(["annotations", "document", "id"]) print(dataset) short_validation_dataset = dataset.filter(lambda x: (len(x["question"]) + len(x["context"])) < 4 * 4096) short_validation_dataset = short_validation_dataset.filter(lambda x: x["category"] != "null") short_validation_dataset model_id = "vasudevgupta/flax-bigbird-natural-questions" model = FlaxBigBirdForNaturalQuestions.from_pretrained(model_id) tokenizer = BigBirdTokenizerFast.from_pretrained(model_id) @jax.jit def forward(*args, **kwargs): start_logits, end_logits, pooled_logits = model(*args, **kwargs) return start_logits, end_logits, jnp.argmax(pooled_logits, axis=-1) def evaluate(example): # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length inputs = tokenizer( example["question"], example["context"], return_tensors="np", max_length=4096, padding="max_length", truncation=True, ) start_scores, end_scores, category = forward(**inputs) predicted_category = CATEGORY_MAPPING[category.item()] example["targets"] = example["long"] + example["short"] if example["category"] in ["yes", "no", "null"]: example["targets"] = [example["category"]] example["has_tgt"] = example["category"] != "null" # Now target can be: "yes", "no", "null", "list of long & short answers" if predicted_category in ["yes", "no", "null"]: example["output"] = [predicted_category] example["match"] = example["output"] == example["targets"] example["has_pred"] = predicted_category != "null" return example max_size = 38 if predicted_category == "short" else 1024 start_score, end_score = get_best_valid_start_end_idx( start_scores[0], end_scores[0], top_k=8, max_size=max_size ) input_ids = inputs["input_ids"][0].tolist() example["output"] = [tokenizer.decode(input_ids[start_score : end_score + 1])] answers = expand_to_aliases(example["targets"], make_sub_answers=True) predictions = expand_to_aliases(example["output"]) # some preprocessing to both prediction and answer answers = set(["".join(a.split()) for a in answers]) predictions = set(["".join(p.split()) for p in predictions]) predictions = set([s for s in predictions if s not in ["``", "''", "`", "'"]]) # if there is a common element, it's a exact match example["match"] = len(list(answers & predictions)) > 0 example["has_pred"] = predicted_category != "null" and len(predictions) > 0 return example short_validation_dataset = short_validation_dataset.map(evaluate) total = len(short_validation_dataset) matched = len(short_validation_dataset.filter(lambda x: x["match"] == 1)) print("EM score:", (matched / total) * 100, "%") if __name__ == "__main__": main()

from datasets import load_from_disk import jax import jax.numpy as jnp from bigbird_flax import FlaxBigBirdForNaturalQuestions from transformers import BigBirdTokenizerFast CATEGORY_MAPPING = {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"} PUNCTUATION_SET_TO_EXCLUDE = set("".join(["‘", "’", "´", "`", ".", ",", "-", '"'])) def get_sub_answers(answers, begin=0, end=None): return [" ".join(x.split(" ")[begin:end]) for x in answers if len(x.split(" ")) > 1] def expand_to_aliases(given_answers, make_sub_answers=False): if make_sub_answers: # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word # *e.g.* if the correct answer contains a prefix such as "the", or "a" given_answers = ( given_answers + get_sub_answers(given_answers, begin=1) + get_sub_answers(given_answers, end=-1) ) answers = [] for answer in given_answers: alias = answer.replace("_", " ").lower() alias = "".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias) answers.append(" ".join(alias.split()).strip()) return set(answers) def get_best_valid_start_end_idx(start_scores, end_scores, top_k=1, max_size=100): best_start_scores, best_start_idx = jax.lax.top_k(start_scores, top_k) best_end_scores, best_end_idx = jax.lax.top_k(end_scores, top_k) widths = best_end_idx[:, None] - best_start_idx[None, :] mask = jnp.logical_or(widths < 0, widths > max_size) scores = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask) best_score = jnp.argmax(scores).item() return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k] def format_dataset(sample): question = sample["question"]["text"] context = sample["document"]["tokens"]["token"] is_html = sample["document"]["tokens"]["is_html"] long_answers = sample["annotations"]["long_answer"] short_answers = sample["annotations"]["short_answers"] context_string = " ".join([context[i] for i in range(len(context)) if not is_html[i]]) # 0 - No ; 1 - Yes for answer in sample["annotations"]["yes_no_answer"]: if answer == 0 or answer == 1: return { "question": question, "context": context_string, "short": [], "long": [], "category": "no" if answer == 0 else "yes", } short_targets = [] for s in short_answers: short_targets.extend(s["text"]) short_targets = list(set(short_targets)) long_targets = [] for s in long_answers: if s["start_token"] == -1: continue answer = context[s["start_token"] : s["end_token"]] html = is_html[s["start_token"] : s["end_token"]] new_answer = " ".join([answer[i] for i in range(len(answer)) if not html[i]]) if new_answer not in long_targets: long_targets.append(new_answer) category = "long_short" if len(short_targets + long_targets) > 0 else "null" return { "question": question, "context": context_string, "short": short_targets, "long": long_targets, "category": category, } def main(): dataset = load_from_disk("natural-questions-validation") dataset = dataset.map(format_dataset).remove_columns(["annotations", "document", "id"]) print(dataset) short_validation_dataset = dataset.filter(lambda x: (len(x["question"]) + len(x["context"])) < 4 * 4096) short_validation_dataset = short_validation_dataset.filter(lambda x: x["category"] != "null") short_validation_dataset model_id = "vasudevgupta/flax-bigbird-natural-questions" model = FlaxBigBirdForNaturalQuestions.from_pretrained(model_id) tokenizer = BigBirdTokenizerFast.from_pretrained(model_id) @jax.jit def forward(*args, **kwargs): start_logits, end_logits, pooled_logits = model(*args, **kwargs) return start_logits, end_logits, jnp.argmax(pooled_logits, axis=-1) def evaluate(example): # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length inputs = tokenizer( example["question"], example["context"], return_tensors="np", max_length=4096, padding="max_length", truncation=True, ) start_scores, end_scores, category = forward(**inputs) predicted_category = CATEGORY_MAPPING[category.item()] example["targets"] = example["long"] + example["short"] if example["category"] in ["yes", "no", "null"]: example["targets"] = [example["category"]] example["has_tgt"] = example["category"] != "null" # Now target can be: "yes", "no", "null", "list of long & short answers" if predicted_category in ["yes", "no", "null"]: example["output"] = [predicted_category] example["match"] = example["output"] == example["targets"] example["has_pred"] = predicted_category != "null" return example max_size = 38 if predicted_category == "short" else 1024 start_score, end_score = get_best_valid_start_end_idx( start_scores[0], end_scores[0], top_k=8, max_size=max_size ) input_ids = inputs["input_ids"][0].tolist() example["output"] = [tokenizer.decode(input_ids[start_score : end_score + 1])] answers = expand_to_aliases(example["targets"], make_sub_answers=True) predictions = expand_to_aliases(example["output"]) # some preprocessing to both prediction and answer answers = set(["".join(a.split()) for a in answers]) predictions = set(["".join(p.split()) for p in predictions]) predictions = set([s for s in predictions if s not in ["``", "''", "`", "'"]]) # if there is a common element, it's a exact match example["match"] = len(list(answers & predictions)) > 0 example["has_pred"] = predicted_category != "null" and len(predictions) > 0 return example short_validation_dataset = short_validation_dataset.map(evaluate) total = len(short_validation_dataset) matched = len(short_validation_dataset.filter(lambda x: x["match"] == 1)) print("EM score:", (matched / total) * 100, "%") if __name__ == "__main__": main()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/camembert/modeling_camembert.py

# coding=utf-8 # Copyright 2019 Inria, Facebook AI Research and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch CamemBERT model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN, gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_camembert import CamembertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "camembert-base" _CONFIG_FOR_DOC = "CamembertConfig" _TOKENIZER_FOR_DOC = "CamembertTokenizer" CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "camembert-base", "Musixmatch/umberto-commoncrawl-cased-v1", "Musixmatch/umberto-wikipedia-uncased-v1", # See all CamemBERT models at https://huggingface.co/models?filter=camembert ] CAMEMBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CamembertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Camembert class CamembertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Camembert class CamembertSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in CamembertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput with Roberta->Camembert class CamembertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->Camembert class CamembertAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = CamembertSelfAttention(config, position_embedding_type=position_embedding_type) self.output = CamembertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Roberta->Camembert class CamembertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Roberta->Camembert class CamembertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->Camembert class CamembertLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CamembertAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = CamembertAttention(config, position_embedding_type="absolute") self.intermediate = CamembertIntermediate(config) self.output = CamembertOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->Camembert class CamembertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler class CamembertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CamembertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CamembertConfig base_model_prefix = "roberta" supports_gradient_checkpointing = True # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CamembertEncoder): module.gradient_checkpointing = value def update_keys_to_ignore(self, config, del_keys_to_ignore): """Remove some keys from ignore list""" if not config.tie_word_embeddings: # must make a new list, or the class variable gets modified! self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] self._keys_to_ignore_on_load_missing = [ k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore ] CAMEMBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CamembertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Camembert class CamembertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x # Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Camembert class CamembertLMHead(nn.Module): """Camembert Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) # For accelerate compatibility and to not break backward compatibility if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias else: self.bias = self.decoder.bias @add_start_docstrings( "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.", CAMEMBERT_START_DOCSTRING, ) class CamembertModel(CamembertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ _keys_to_ignore_on_load_missing = [r"position_ids"] _no_split_modules = [] # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Camembert def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = CamembertEmbeddings(config) self.encoder = CamembertEncoder(config) self.pooler = CamembertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top.""", CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMaskedLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForSequenceClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.roberta = CamembertModel(config, add_pooling_layer=False) self.classifier = CamembertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMultipleChoice(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.roberta = CamembertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForTokenClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="Jean-Baptiste/roberta-large-ner-english", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits` """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForQuestionAnswering(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="deepset/roberta-base-squad2", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, roberta-base->camembert-base class CamembertForCausalLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `CamembertLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import CamembertTokenizer, CamembertForCausalLM, CamembertConfig >>> import torch >>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base") >>> config = CamembertConfig.from_pretrained("camembert-base") >>> config.is_decoder = True >>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

# coding=utf-8 # Copyright 2019 Inria, Facebook AI Research and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch CamemBERT model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN, gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_camembert import CamembertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "camembert-base" _CONFIG_FOR_DOC = "CamembertConfig" _TOKENIZER_FOR_DOC = "CamembertTokenizer" CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "camembert-base", "Musixmatch/umberto-commoncrawl-cased-v1", "Musixmatch/umberto-wikipedia-uncased-v1", # See all CamemBERT models at https://huggingface.co/models?filter=camembert ] CAMEMBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CamembertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Camembert class CamembertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Camembert class CamembertSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in CamembertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput with Roberta->Camembert class CamembertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->Camembert class CamembertAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = CamembertSelfAttention(config, position_embedding_type=position_embedding_type) self.output = CamembertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Roberta->Camembert class CamembertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Roberta->Camembert class CamembertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->Camembert class CamembertLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CamembertAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = CamembertAttention(config, position_embedding_type="absolute") self.intermediate = CamembertIntermediate(config) self.output = CamembertOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->Camembert class CamembertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_bert.BertPooler class CamembertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CamembertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CamembertConfig base_model_prefix = "roberta" supports_gradient_checkpointing = True # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CamembertEncoder): module.gradient_checkpointing = value def update_keys_to_ignore(self, config, del_keys_to_ignore): """Remove some keys from ignore list""" if not config.tie_word_embeddings: # must make a new list, or the class variable gets modified! self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] self._keys_to_ignore_on_load_missing = [ k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore ] CAMEMBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CamembertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Camembert class CamembertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x # Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Camembert class CamembertLMHead(nn.Module): """Camembert Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) # For accelerate compatibility and to not break backward compatibility if self.decoder.bias.device.type == "meta": self.decoder.bias = self.bias else: self.bias = self.decoder.bias @add_start_docstrings( "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.", CAMEMBERT_START_DOCSTRING, ) class CamembertModel(CamembertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ _keys_to_ignore_on_load_missing = [r"position_ids"] _no_split_modules = [] # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Camembert def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = CamembertEmbeddings(config) self.encoder = CamembertEncoder(config) self.pooler = CamembertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top.""", CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMaskedLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForSequenceClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.roberta = CamembertModel(config, add_pooling_layer=False) self.classifier = CamembertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForMultipleChoice(CamembertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.roberta = CamembertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForTokenClassification(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="Jean-Baptiste/roberta-large-ner-english", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits` """, CAMEMBERT_START_DOCSTRING, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering with Roberta->Camembert, ROBERTA->CAMEMBERT class CamembertForQuestionAnswering(CamembertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = CamembertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint="deepset/roberta-base-squad2", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING ) # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, roberta-base->camembert-base class CamembertForCausalLM(CamembertPreTrainedModel): _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `CamembertLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = CamembertModel(config, add_pooling_layer=False) self.lm_head = CamembertLMHead(config) # The LM head weights require special treatment only when they are tied with the word embeddings self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import CamembertTokenizer, CamembertForCausalLM, CamembertConfig >>> import torch >>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base") >>> config = CamembertConfig.from_pretrained("camembert-base") >>> config.is_decoder = True >>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/auto/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = { "auto_factory": ["get_values"], "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"], "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"], "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"], "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"], "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_auto"] = [ "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", "MODEL_FOR_AUDIO_XVECTOR_MAPPING", "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "MODEL_FOR_MASKED_LM_MAPPING", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "MODEL_FOR_OBJECT_DETECTION_MAPPING", "MODEL_FOR_PRETRAINING_MAPPING", "MODEL_FOR_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "AutoModel", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", "AutoModelForDepthEstimation", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", "AutoModelForMaskedImageModeling", "AutoModelForMaskedLM", "AutoModelForMultipleChoice", "AutoModelForNextSentencePrediction", "AutoModelForObjectDetection", "AutoModelForPreTraining", "AutoModelForQuestionAnswering", "AutoModelForSemanticSegmentation", "AutoModelForSeq2SeqLM", "AutoModelForSequenceClassification", "AutoModelForSpeechSeq2Seq", "AutoModelForTableQuestionAnswering", "AutoModelForTokenClassification", "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", "AutoModelForDocumentQuestionAnswering", "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_auto"] = [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "TF_MODEL_FOR_MASKED_LM_MAPPING", "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "TF_MODEL_FOR_PRETRAINING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", "TF_MODEL_MAPPING", "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", "TFAutoModelForSequenceClassification", "TFAutoModelForSpeechSeq2Seq", "TFAutoModelForTableQuestionAnswering", "TFAutoModelForTokenClassification", "TFAutoModelForVision2Seq", "TFAutoModelWithLMHead", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_auto"] = [ "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_MASKED_LM_MAPPING", "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "FLAX_MODEL_FOR_PRETRAINING_MAPPING", "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", "FLAX_MODEL_MAPPING", "FlaxAutoModel", "FlaxAutoModelForCausalLM", "FlaxAutoModelForImageClassification", "FlaxAutoModelForMaskedLM", "FlaxAutoModelForMultipleChoice", "FlaxAutoModelForNextSentencePrediction", "FlaxAutoModelForPreTraining", "FlaxAutoModelForQuestionAnswering", "FlaxAutoModelForSeq2SeqLM", "FlaxAutoModelForSequenceClassification", "FlaxAutoModelForTokenClassification", "FlaxAutoModelForVision2Seq", ] if TYPE_CHECKING: from .auto_factory import get_values from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor from .processing_auto import PROCESSOR_MAPPING, AutoProcessor from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_CTC_MAPPING, MODEL_FOR_DEPTH_ESTIMATION_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoModel, AutoModelForAudioClassification, AutoModelForAudioFrameClassification, AutoModelForAudioXVector, AutoModelForCausalLM, AutoModelForCTC, AutoModelForDepthEstimation, AutoModelForDocumentQuestionAnswering, AutoModelForImageClassification, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMultipleChoice, AutoModelForNextSentencePrediction, AutoModelForObjectDetection, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSemanticSegmentation, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForSpeechSeq2Seq, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelForVideoClassification, AutoModelForVision2Seq, AutoModelForVisualQuestionAnswering, AutoModelForZeroShotObjectDetection, AutoModelWithLMHead, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForDocumentQuestionAnswering, TFAutoModelForImageClassification, TFAutoModelForMaskedLM, TFAutoModelForMultipleChoice, TFAutoModelForNextSentencePrediction, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSemanticSegmentation, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForSpeechSeq2Seq, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelForVision2Seq, TFAutoModelWithLMHead, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_auto import ( FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, FLAX_MODEL_FOR_PRETRAINING_MAPPING, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_MAPPING, FlaxAutoModel, FlaxAutoModelForCausalLM, FlaxAutoModelForImageClassification, FlaxAutoModelForMaskedLM, FlaxAutoModelForMultipleChoice, FlaxAutoModelForNextSentencePrediction, FlaxAutoModelForPreTraining, FlaxAutoModelForQuestionAnswering, FlaxAutoModelForSeq2SeqLM, FlaxAutoModelForSequenceClassification, FlaxAutoModelForTokenClassification, FlaxAutoModelForVision2Seq, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = { "auto_factory": ["get_values"], "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"], "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"], "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"], "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"], "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_auto"] = [ "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", "MODEL_FOR_AUDIO_XVECTOR_MAPPING", "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "MODEL_FOR_MASKED_LM_MAPPING", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "MODEL_FOR_OBJECT_DETECTION_MAPPING", "MODEL_FOR_PRETRAINING_MAPPING", "MODEL_FOR_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "AutoModel", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", "AutoModelForDepthEstimation", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", "AutoModelForMaskedImageModeling", "AutoModelForMaskedLM", "AutoModelForMultipleChoice", "AutoModelForNextSentencePrediction", "AutoModelForObjectDetection", "AutoModelForPreTraining", "AutoModelForQuestionAnswering", "AutoModelForSemanticSegmentation", "AutoModelForSeq2SeqLM", "AutoModelForSequenceClassification", "AutoModelForSpeechSeq2Seq", "AutoModelForTableQuestionAnswering", "AutoModelForTokenClassification", "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", "AutoModelForDocumentQuestionAnswering", "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_auto"] = [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "TF_MODEL_FOR_MASKED_LM_MAPPING", "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "TF_MODEL_FOR_PRETRAINING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", "TF_MODEL_MAPPING", "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", "TFAutoModelForSequenceClassification", "TFAutoModelForSpeechSeq2Seq", "TFAutoModelForTableQuestionAnswering", "TFAutoModelForTokenClassification", "TFAutoModelForVision2Seq", "TFAutoModelWithLMHead", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_auto"] = [ "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_MASKED_LM_MAPPING", "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "FLAX_MODEL_FOR_PRETRAINING_MAPPING", "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", "FLAX_MODEL_MAPPING", "FlaxAutoModel", "FlaxAutoModelForCausalLM", "FlaxAutoModelForImageClassification", "FlaxAutoModelForMaskedLM", "FlaxAutoModelForMultipleChoice", "FlaxAutoModelForNextSentencePrediction", "FlaxAutoModelForPreTraining", "FlaxAutoModelForQuestionAnswering", "FlaxAutoModelForSeq2SeqLM", "FlaxAutoModelForSequenceClassification", "FlaxAutoModelForTokenClassification", "FlaxAutoModelForVision2Seq", ] if TYPE_CHECKING: from .auto_factory import get_values from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor from .processing_auto import PROCESSOR_MAPPING, AutoProcessor from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_CTC_MAPPING, MODEL_FOR_DEPTH_ESTIMATION_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoModel, AutoModelForAudioClassification, AutoModelForAudioFrameClassification, AutoModelForAudioXVector, AutoModelForCausalLM, AutoModelForCTC, AutoModelForDepthEstimation, AutoModelForDocumentQuestionAnswering, AutoModelForImageClassification, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMultipleChoice, AutoModelForNextSentencePrediction, AutoModelForObjectDetection, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSemanticSegmentation, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForSpeechSeq2Seq, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelForVideoClassification, AutoModelForVision2Seq, AutoModelForVisualQuestionAnswering, AutoModelForZeroShotObjectDetection, AutoModelWithLMHead, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForDocumentQuestionAnswering, TFAutoModelForImageClassification, TFAutoModelForMaskedLM, TFAutoModelForMultipleChoice, TFAutoModelForNextSentencePrediction, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSemanticSegmentation, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForSpeechSeq2Seq, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelForVision2Seq, TFAutoModelWithLMHead, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_auto import ( FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, FLAX_MODEL_FOR_PRETRAINING_MAPPING, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_MAPPING, FlaxAutoModel, FlaxAutoModelForCausalLM, FlaxAutoModelForImageClassification, FlaxAutoModelForMaskedLM, FlaxAutoModelForMultipleChoice, FlaxAutoModelForNextSentencePrediction, FlaxAutoModelForPreTraining, FlaxAutoModelForQuestionAnswering, FlaxAutoModelForSeq2SeqLM, FlaxAutoModelForSequenceClassification, FlaxAutoModelForTokenClassification, FlaxAutoModelForVision2Seq, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax VisionTextDualEncoder model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring from ...utils import add_start_docstrings, logging from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded via the [`~FlaxAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and should be fine-tuned on a downstream task, like contrastive image-text modeling. In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on new zero-shot vision tasks such as image classification or retrieval. After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`VisionTextDualEncoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using a feature extractor (e.g. if you use ViT as the encoder, you should use [`ViTFeatureExtractor`]). See [`ViTFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxVisionTextDualEncoderModule(nn.Module): config: VisionTextDualEncoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): vision_config = self.config.vision_config text_config = self.config.text_config self.vision_embed_dim = vision_config.hidden_size self.text_embed_dim = text_config.hidden_size self.projection_dim = self.config.projection_dim vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class self.vision_model = vision_module(vision_config, dtype=self.dtype) self.text_model = text_module(text_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, token_type_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel): config_class = VisionTextDualEncoderConfig module_class = FlaxVisionTextDualEncoderModule def __init__( self, config: VisionTextDualEncoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if not _do_init: raise ValueError( "`FlaxVisionTextDualEncoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) token_type_ids = jnp.ones_like(input_ids) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)[ "params" ] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of text model. """ if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`ImageFeatureExtractionMixin`]. See [`ImageFeatureExtractionMixin.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of vision model. """ # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) @classmethod def from_vision_text_pretrained( cls, vision_model_name_or_path: str = None, text_model_name_or_path: str = None, *model_args, **kwargs, ) -> FlaxPreTrainedModel: """ Params: vision_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the vision model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. text_model_name_or_path (`str`, *optional*): Information necessary to initiate the text model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the text configuration, use the prefix *text_* for each configuration parameter. - To update the vision configuration, use the prefix *vision_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionTextDualEncoderModel >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("./vit-bert") ```""" kwargs_vision = { argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") } kwargs_text = { argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") } # remove text, vision kwargs from kwargs for key in kwargs_vision.keys(): del kwargs["vision_" + key] for key in kwargs_text.keys(): del kwargs["text_" + key] # Load and initialize the text and vision model vision_model = kwargs_vision.pop("model", None) if vision_model is None: if vision_model_name_or_path is None: raise ValueError( "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" ) if "config" not in kwargs_vision: vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) if vision_config.model_type == "clip": kwargs_vision["config"] = vision_config.vision_config vision_model = FlaxCLIPVisionModel.from_pretrained( vision_model_name_or_path, *model_args, **kwargs_vision ) else: kwargs_vision["config"] = vision_config vision_model = FlaxAutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) text_model = kwargs_text.pop("model", None) if text_model is None: if text_model_name_or_path is None: raise ValueError( "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" ) if "config" not in kwargs_text: text_config = AutoConfig.from_pretrained(text_model_name_or_path) kwargs_text["config"] = text_config text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) # init model model = cls(config, *model_args, dtype=dtype, **kwargs) model.params["vision_model"] = vision_model.params model.params["text_model"] = text_model.params # the projection layers are always newly initialized when loading the model # using pre-trained vision and text model. logger.warning( "The projection layer and logit scale weights `[('visual_projection', 'kernel'), ('text_projection'," " 'kernel'), ('logit_scale',)]` are newly initialized. You should probably TRAIN this model on a" " down-stream task to be able to use it for predictions and inference." ) return model VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> import jax >>> from transformers import ( ... FlaxVisionTextDualEncoderModel, ... VisionTextDualEncoderProcessor, ... ViTFeatureExtractor, ... BertTokenizer, ... ) >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") >>> processor = VisionTextDualEncoderProcessor(feature_extractor, tokenizer) >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # contrastive training >>> urls = [ ... "http://images.cocodataset.org/val2017/000000039769.jpg", ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", ... ] >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True ... ) >>> outputs = model( ... input_ids=inputs.input_ids, ... attention_mask=inputs.attention_mask, ... pixel_values=inputs.pixel_values, ... ) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> # save and load from pretrained >>> model.save_pretrained("vit-bert") >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert") >>> # inference >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring( FlaxVisionTextDualEncoderModel, VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax VisionTextDualEncoder model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring from ...utils import add_start_docstrings, logging from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded via the [`~FlaxAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and should be fine-tuned on a downstream task, like contrastive image-text modeling. In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on new zero-shot vision tasks such as image classification or retrieval. After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`VisionTextDualEncoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using a feature extractor (e.g. if you use ViT as the encoder, you should use [`ViTFeatureExtractor`]). See [`ViTFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxVisionTextDualEncoderModule(nn.Module): config: VisionTextDualEncoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): vision_config = self.config.vision_config text_config = self.config.text_config self.vision_embed_dim = vision_config.hidden_size self.text_embed_dim = text_config.hidden_size self.projection_dim = self.config.projection_dim vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class self.vision_model = vision_module(vision_config, dtype=self.dtype) self.text_model = text_module(text_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, token_type_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel): config_class = VisionTextDualEncoderConfig module_class = FlaxVisionTextDualEncoderModule def __init__( self, config: VisionTextDualEncoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if not _do_init: raise ValueError( "`FlaxVisionTextDualEncoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) token_type_ids = jnp.ones_like(input_ids) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)[ "params" ] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of text model. """ if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`ImageFeatureExtractionMixin`]. See [`ImageFeatureExtractionMixin.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of vision model. """ # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) @classmethod def from_vision_text_pretrained( cls, vision_model_name_or_path: str = None, text_model_name_or_path: str = None, *model_args, **kwargs, ) -> FlaxPreTrainedModel: """ Params: vision_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the vision model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. text_model_name_or_path (`str`, *optional*): Information necessary to initiate the text model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the text configuration, use the prefix *text_* for each configuration parameter. - To update the vision configuration, use the prefix *vision_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionTextDualEncoderModel >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("./vit-bert") ```""" kwargs_vision = { argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") } kwargs_text = { argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") } # remove text, vision kwargs from kwargs for key in kwargs_vision.keys(): del kwargs["vision_" + key] for key in kwargs_text.keys(): del kwargs["text_" + key] # Load and initialize the text and vision model vision_model = kwargs_vision.pop("model", None) if vision_model is None: if vision_model_name_or_path is None: raise ValueError( "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" ) if "config" not in kwargs_vision: vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) if vision_config.model_type == "clip": kwargs_vision["config"] = vision_config.vision_config vision_model = FlaxCLIPVisionModel.from_pretrained( vision_model_name_or_path, *model_args, **kwargs_vision ) else: kwargs_vision["config"] = vision_config vision_model = FlaxAutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) text_model = kwargs_text.pop("model", None) if text_model is None: if text_model_name_or_path is None: raise ValueError( "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" ) if "config" not in kwargs_text: text_config = AutoConfig.from_pretrained(text_model_name_or_path) kwargs_text["config"] = text_config text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) # init model model = cls(config, *model_args, dtype=dtype, **kwargs) model.params["vision_model"] = vision_model.params model.params["text_model"] = text_model.params # the projection layers are always newly initialized when loading the model # using pre-trained vision and text model. logger.warning( "The projection layer and logit scale weights `[('visual_projection', 'kernel'), ('text_projection'," " 'kernel'), ('logit_scale',)]` are newly initialized. You should probably TRAIN this model on a" " down-stream task to be able to use it for predictions and inference." ) return model VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> import jax >>> from transformers import ( ... FlaxVisionTextDualEncoderModel, ... VisionTextDualEncoderProcessor, ... ViTFeatureExtractor, ... BertTokenizer, ... ) >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") >>> processor = VisionTextDualEncoderProcessor(feature_extractor, tokenizer) >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # contrastive training >>> urls = [ ... "http://images.cocodataset.org/val2017/000000039769.jpg", ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", ... ] >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True ... ) >>> outputs = model( ... input_ids=inputs.input_ids, ... attention_mask=inputs.attention_mask, ... pixel_values=inputs.pixel_values, ... ) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> # save and load from pretrained >>> model.save_pretrained("vit-bert") >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert") >>> # inference >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring( FlaxVisionTextDualEncoderModel, VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/fnet/__init__.py

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/commands/convert.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser, Namespace from ..utils import logging from . import BaseTransformersCLICommand def convert_command_factory(args: Namespace): """ Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint. Returns: ServeCommand """ return ConvertCommand( args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name ) IMPORT_ERROR_MESSAGE = """ transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions. """ class ConvertCommand(BaseTransformersCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): """ Register this command to argparse so it's available for the transformer-cli Args: parser: Root parser to register command-specific arguments """ train_parser = parser.add_parser( "convert", help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.", ) train_parser.add_argument("--model_type", type=str, required=True, help="Model's type.") train_parser.add_argument( "--tf_checkpoint", type=str, required=True, help="TensorFlow checkpoint path or folder." ) train_parser.add_argument( "--pytorch_dump_output", type=str, required=True, help="Path to the PyTorch saved model output." ) train_parser.add_argument("--config", type=str, default="", help="Configuration file path or folder.") train_parser.add_argument( "--finetuning_task_name", type=str, default=None, help="Optional fine-tuning task name if the TF model was a finetuned model.", ) train_parser.set_defaults(func=convert_command_factory) def __init__( self, model_type: str, tf_checkpoint: str, pytorch_dump_output: str, config: str, finetuning_task_name: str, *args ): self._logger = logging.get_logger("transformers-cli/converting") self._logger.info(f"Loading model {model_type}") self._model_type = model_type self._tf_checkpoint = tf_checkpoint self._pytorch_dump_output = pytorch_dump_output self._config = config self._finetuning_task_name = finetuning_task_name def run(self): if self._model_type == "albert": try: from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import ( convert_tf_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "bert": try: from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import ( convert_tf_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "funnel": try: from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import ( convert_tf_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "t5": try: from ..models.t5.convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "gpt": from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import ( convert_openai_checkpoint_to_pytorch, ) convert_openai_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "transfo_xl": try: from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import ( convert_transfo_xl_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) if "ckpt" in self._tf_checkpoint.lower(): TF_CHECKPOINT = self._tf_checkpoint TF_DATASET_FILE = "" else: TF_DATASET_FILE = self._tf_checkpoint TF_CHECKPOINT = "" convert_transfo_xl_checkpoint_to_pytorch( TF_CHECKPOINT, self._config, self._pytorch_dump_output, TF_DATASET_FILE ) elif self._model_type == "gpt2": try: from ..models.gpt2.convert_gpt2_original_tf_checkpoint_to_pytorch import ( convert_gpt2_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_gpt2_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "xlnet": try: from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import ( convert_xlnet_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_xlnet_checkpoint_to_pytorch( self._tf_checkpoint, self._config, self._pytorch_dump_output, self._finetuning_task_name ) elif self._model_type == "xlm": from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import ( convert_xlm_checkpoint_to_pytorch, ) convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output) elif self._model_type == "lxmert": from ..models.lxmert.convert_lxmert_original_pytorch_checkpoint_to_pytorch import ( convert_lxmert_checkpoint_to_pytorch, ) convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output) elif self._model_type == "rembert": from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import ( convert_rembert_tf_checkpoint_to_pytorch, ) convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) else: raise ValueError( "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser, Namespace from ..utils import logging from . import BaseTransformersCLICommand def convert_command_factory(args: Namespace): """ Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint. Returns: ServeCommand """ return ConvertCommand( args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name ) IMPORT_ERROR_MESSAGE = """ transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions. """ class ConvertCommand(BaseTransformersCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): """ Register this command to argparse so it's available for the transformer-cli Args: parser: Root parser to register command-specific arguments """ train_parser = parser.add_parser( "convert", help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.", ) train_parser.add_argument("--model_type", type=str, required=True, help="Model's type.") train_parser.add_argument( "--tf_checkpoint", type=str, required=True, help="TensorFlow checkpoint path or folder." ) train_parser.add_argument( "--pytorch_dump_output", type=str, required=True, help="Path to the PyTorch saved model output." ) train_parser.add_argument("--config", type=str, default="", help="Configuration file path or folder.") train_parser.add_argument( "--finetuning_task_name", type=str, default=None, help="Optional fine-tuning task name if the TF model was a finetuned model.", ) train_parser.set_defaults(func=convert_command_factory) def __init__( self, model_type: str, tf_checkpoint: str, pytorch_dump_output: str, config: str, finetuning_task_name: str, *args ): self._logger = logging.get_logger("transformers-cli/converting") self._logger.info(f"Loading model {model_type}") self._model_type = model_type self._tf_checkpoint = tf_checkpoint self._pytorch_dump_output = pytorch_dump_output self._config = config self._finetuning_task_name = finetuning_task_name def run(self): if self._model_type == "albert": try: from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import ( convert_tf_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "bert": try: from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import ( convert_tf_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "funnel": try: from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import ( convert_tf_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "t5": try: from ..models.t5.convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "gpt": from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import ( convert_openai_checkpoint_to_pytorch, ) convert_openai_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "transfo_xl": try: from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import ( convert_transfo_xl_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) if "ckpt" in self._tf_checkpoint.lower(): TF_CHECKPOINT = self._tf_checkpoint TF_DATASET_FILE = "" else: TF_DATASET_FILE = self._tf_checkpoint TF_CHECKPOINT = "" convert_transfo_xl_checkpoint_to_pytorch( TF_CHECKPOINT, self._config, self._pytorch_dump_output, TF_DATASET_FILE ) elif self._model_type == "gpt2": try: from ..models.gpt2.convert_gpt2_original_tf_checkpoint_to_pytorch import ( convert_gpt2_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_gpt2_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) elif self._model_type == "xlnet": try: from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import ( convert_xlnet_checkpoint_to_pytorch, ) except ImportError: raise ImportError(IMPORT_ERROR_MESSAGE) convert_xlnet_checkpoint_to_pytorch( self._tf_checkpoint, self._config, self._pytorch_dump_output, self._finetuning_task_name ) elif self._model_type == "xlm": from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import ( convert_xlm_checkpoint_to_pytorch, ) convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output) elif self._model_type == "lxmert": from ..models.lxmert.convert_lxmert_original_pytorch_checkpoint_to_pytorch import ( convert_lxmert_checkpoint_to_pytorch, ) convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output) elif self._model_type == "rembert": from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import ( convert_rembert_tf_checkpoint_to_pytorch, ) convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output) else: raise ValueError( "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/prophetnet/test_tokenization_prophetnet.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from transformers import BatchEncoding from transformers.models.bert.tokenization_bert import ( BasicTokenizer, WordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer from transformers.testing_utils import require_torch, slow from ...test_tokenization_common import TokenizerTesterMixin class ProphetNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = ProphetNetTokenizer test_rust_tokenizer = False def setUp(self): super().setUp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11]) def test_chinese(self): tokenizer = BasicTokenizer() self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"), ["ah", "\u535A", "\u63A8", "zz"]) def test_basic_tokenizer_lower(self): tokenizer = BasicTokenizer(do_lower_case=True) self.assertListEqual( tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["hello", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"]) def test_basic_tokenizer_lower_strip_accents_false(self): tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hällo", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"]) def test_basic_tokenizer_lower_strip_accents_true(self): tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=True) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"]) def test_basic_tokenizer_lower_strip_accents_default(self): tokenizer = BasicTokenizer(do_lower_case=True) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"]) def test_basic_tokenizer_no_lower(self): tokenizer = BasicTokenizer(do_lower_case=False) self.assertListEqual( tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["HeLLo", "!", "how", "Are", "yoU", "?"] ) def test_basic_tokenizer_no_lower_strip_accents_false(self): tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HäLLo", "!", "how", "Are", "yoU", "?"] ) def test_basic_tokenizer_no_lower_strip_accents_true(self): tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HaLLo", "!", "how", "Are", "yoU", "?"] ) def test_basic_tokenizer_respects_never_split_tokens(self): tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"]) self.assertListEqual( tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] ) def test_wordpiece_tokenizer(self): vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"] vocab = {} for i, token in enumerate(vocab_tokens): vocab[token] = i tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]") self.assertListEqual(tokenizer.tokenize(""), []) self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"]) self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"]) @require_torch def test_prepare_batch(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased") src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] expected_src_tokens = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102] batch = tokenizer(src_text, padding=True, return_tensors="pt") self.assertIsInstance(batch, BatchEncoding) result = list(batch.input_ids.numpy()[0]) self.assertListEqual(expected_src_tokens, result) self.assertEqual((2, 9), batch.input_ids.shape) self.assertEqual((2, 9), batch.attention_mask.shape) def test_is_whitespace(self): self.assertTrue(_is_whitespace(" ")) self.assertTrue(_is_whitespace("\t")) self.assertTrue(_is_whitespace("\r")) self.assertTrue(_is_whitespace("\n")) self.assertTrue(_is_whitespace("\u00A0")) self.assertFalse(_is_whitespace("A")) self.assertFalse(_is_whitespace("-")) def test_is_control(self): self.assertTrue(_is_control("\u0005")) self.assertFalse(_is_control("A")) self.assertFalse(_is_control(" ")) self.assertFalse(_is_control("\t")) self.assertFalse(_is_control("\r")) def test_is_punctuation(self): self.assertTrue(_is_punctuation("-")) self.assertTrue(_is_punctuation("$")) self.assertTrue(_is_punctuation("`")) self.assertTrue(_is_punctuation(".")) self.assertFalse(_is_punctuation("A")) self.assertFalse(_is_punctuation(" ")) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == text + [102] assert encoded_pair == text + [102] + text_2 + [102]

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from transformers import BatchEncoding from transformers.models.bert.tokenization_bert import ( BasicTokenizer, WordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer from transformers.testing_utils import require_torch, slow from ...test_tokenization_common import TokenizerTesterMixin class ProphetNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = ProphetNetTokenizer test_rust_tokenizer = False def setUp(self): super().setUp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11]) def test_chinese(self): tokenizer = BasicTokenizer() self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"), ["ah", "\u535A", "\u63A8", "zz"]) def test_basic_tokenizer_lower(self): tokenizer = BasicTokenizer(do_lower_case=True) self.assertListEqual( tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["hello", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"]) def test_basic_tokenizer_lower_strip_accents_false(self): tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hällo", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"]) def test_basic_tokenizer_lower_strip_accents_true(self): tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=True) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"]) def test_basic_tokenizer_lower_strip_accents_default(self): tokenizer = BasicTokenizer(do_lower_case=True) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"] ) self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"]) def test_basic_tokenizer_no_lower(self): tokenizer = BasicTokenizer(do_lower_case=False) self.assertListEqual( tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["HeLLo", "!", "how", "Are", "yoU", "?"] ) def test_basic_tokenizer_no_lower_strip_accents_false(self): tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HäLLo", "!", "how", "Are", "yoU", "?"] ) def test_basic_tokenizer_no_lower_strip_accents_true(self): tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True) self.assertListEqual( tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HaLLo", "!", "how", "Are", "yoU", "?"] ) def test_basic_tokenizer_respects_never_split_tokens(self): tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"]) self.assertListEqual( tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] ) def test_wordpiece_tokenizer(self): vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"] vocab = {} for i, token in enumerate(vocab_tokens): vocab[token] = i tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]") self.assertListEqual(tokenizer.tokenize(""), []) self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"]) self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"]) @require_torch def test_prepare_batch(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased") src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] expected_src_tokens = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102] batch = tokenizer(src_text, padding=True, return_tensors="pt") self.assertIsInstance(batch, BatchEncoding) result = list(batch.input_ids.numpy()[0]) self.assertListEqual(expected_src_tokens, result) self.assertEqual((2, 9), batch.input_ids.shape) self.assertEqual((2, 9), batch.attention_mask.shape) def test_is_whitespace(self): self.assertTrue(_is_whitespace(" ")) self.assertTrue(_is_whitespace("\t")) self.assertTrue(_is_whitespace("\r")) self.assertTrue(_is_whitespace("\n")) self.assertTrue(_is_whitespace("\u00A0")) self.assertFalse(_is_whitespace("A")) self.assertFalse(_is_whitespace("-")) def test_is_control(self): self.assertTrue(_is_control("\u0005")) self.assertFalse(_is_control("A")) self.assertFalse(_is_control(" ")) self.assertFalse(_is_control("\t")) self.assertFalse(_is_control("\r")) def test_is_punctuation(self): self.assertTrue(_is_punctuation("-")) self.assertTrue(_is_punctuation("$")) self.assertTrue(_is_punctuation("`")) self.assertTrue(_is_punctuation(".")) self.assertFalse(_is_punctuation("A")) self.assertFalse(_is_punctuation(" ")) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == text + [102] assert encoded_pair == text + [102] + text_2 + [102]

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/commands/lfs.py

""" Implementation of a custom transfer agent for the transfer type "multipart" for git-lfs. Inspired by: github.com/cbartz/git-lfs-swift-transfer-agent/blob/master/git_lfs_swift_transfer.py Spec is: github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md To launch debugger while developing: ``` [lfs "customtransfer.multipart"] path = /path/to/transformers/.env/bin/python args = -m debugpy --listen 5678 --wait-for-client /path/to/transformers/src/transformers/commands/transformers_cli.py lfs-multipart-upload ```""" import json import os import subprocess import sys import warnings from argparse import ArgumentParser from contextlib import AbstractContextManager from typing import Dict, List, Optional import requests from ..utils import logging from . import BaseTransformersCLICommand logger = logging.get_logger(__name__) # pylint: disable=invalid-name LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload" class LfsCommands(BaseTransformersCLICommand): """ Implementation of a custom transfer agent for the transfer type "multipart" for git-lfs. This lets users upload large files >5GB 🔥. Spec for LFS custom transfer agent is: https://github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md This introduces two commands to the CLI: 1. $ transformers-cli lfs-enable-largefiles This should be executed once for each model repo that contains a model file >5GB. It's documented in the error message you get if you just try to git push a 5GB file without having enabled it before. 2. $ transformers-cli lfs-multipart-upload This command is called by lfs directly and is not meant to be called by the user. """ @staticmethod def register_subcommand(parser: ArgumentParser): enable_parser = parser.add_parser( "lfs-enable-largefiles", help=( "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB." ), ) enable_parser.add_argument("path", type=str, help="Local path to repository you want to configure.") enable_parser.set_defaults(func=lambda args: LfsEnableCommand(args)) upload_parser = parser.add_parser( LFS_MULTIPART_UPLOAD_COMMAND, help=( "Deprecated: use `huggingface-cli` instead. " "Command will get called by git-lfs, do not call it directly." ), ) upload_parser.set_defaults(func=lambda args: LfsUploadCommand(args)) class LfsEnableCommand: def __init__(self, args): self.args = args def run(self): warnings.warn( "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead." ) local_path = os.path.abspath(self.args.path) if not os.path.isdir(local_path): print("This does not look like a valid git repo.") exit(1) subprocess.run( "git config lfs.customtransfer.multipart.path transformers-cli".split(), check=True, cwd=local_path ) subprocess.run( f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split(), check=True, cwd=local_path, ) print("Local repo set up for largefiles") def write_msg(msg: Dict): """Write out the message in Line delimited JSON.""" msg = json.dumps(msg) + "\n" sys.stdout.write(msg) sys.stdout.flush() def read_msg() -> Optional[Dict]: """Read Line delimited JSON from stdin.""" msg = json.loads(sys.stdin.readline().strip()) if "terminate" in (msg.get("type"), msg.get("event")): # terminate message received return None if msg.get("event") not in ("download", "upload"): logger.critical("Received unexpected message") sys.exit(1) return msg class FileSlice(AbstractContextManager): """ File-like object that only reads a slice of a file Inspired by stackoverflow.com/a/29838711/593036 """ def __init__(self, filepath: str, seek_from: int, read_limit: int): self.filepath = filepath self.seek_from = seek_from self.read_limit = read_limit self.n_seen = 0 def __enter__(self): self.f = open(self.filepath, "rb") self.f.seek(self.seek_from) return self def __len__(self): total_length = os.fstat(self.f.fileno()).st_size return min(self.read_limit, total_length - self.seek_from) def read(self, n=-1): if self.n_seen >= self.read_limit: return b"" remaining_amount = self.read_limit - self.n_seen data = self.f.read(remaining_amount if n < 0 else min(n, remaining_amount)) self.n_seen += len(data) return data def __iter__(self): yield self.read(n=4 * 1024 * 1024) def __exit__(self, *args): self.f.close() class LfsUploadCommand: def __init__(self, args): self.args = args def run(self): # Immediately after invoking a custom transfer process, git-lfs # sends initiation data to the process over stdin. # This tells the process useful information about the configuration. init_msg = json.loads(sys.stdin.readline().strip()) if not (init_msg.get("event") == "init" and init_msg.get("operation") == "upload"): write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}) sys.exit(1) # The transfer process should use the information it needs from the # initiation structure, and also perform any one-off setup tasks it # needs to do. It should then respond on stdout with a simple empty # confirmation structure, as follows: write_msg({}) # After the initiation exchange, git-lfs will send any number of # transfer requests to the stdin of the transfer process, in a serial sequence. while True: msg = read_msg() if msg is None: # When all transfers have been processed, git-lfs will send # a terminate event to the stdin of the transfer process. # On receiving this message the transfer process should # clean up and terminate. No response is expected. sys.exit(0) oid = msg["oid"] filepath = msg["path"] completion_url = msg["action"]["href"] header = msg["action"]["header"] chunk_size = int(header.pop("chunk_size")) presigned_urls: List[str] = list(header.values()) parts = [] for i, presigned_url in enumerate(presigned_urls): with FileSlice(filepath, seek_from=i * chunk_size, read_limit=chunk_size) as data: r = requests.put(presigned_url, data=data) r.raise_for_status() parts.append( { "etag": r.headers.get("etag"), "partNumber": i + 1, } ) # In order to support progress reporting while data is uploading / downloading, # the transfer process should post messages to stdout write_msg( { "event": "progress", "oid": oid, "bytesSoFar": (i + 1) * chunk_size, "bytesSinceLast": chunk_size, } ) # Not precise but that's ok. r = requests.post( completion_url, json={ "oid": oid, "parts": parts, }, ) r.raise_for_status() write_msg({"event": "complete", "oid": oid})

""" Implementation of a custom transfer agent for the transfer type "multipart" for git-lfs. Inspired by: github.com/cbartz/git-lfs-swift-transfer-agent/blob/master/git_lfs_swift_transfer.py Spec is: github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md To launch debugger while developing: ``` [lfs "customtransfer.multipart"] path = /path/to/transformers/.env/bin/python args = -m debugpy --listen 5678 --wait-for-client /path/to/transformers/src/transformers/commands/transformers_cli.py lfs-multipart-upload ```""" import json import os import subprocess import sys import warnings from argparse import ArgumentParser from contextlib import AbstractContextManager from typing import Dict, List, Optional import requests from ..utils import logging from . import BaseTransformersCLICommand logger = logging.get_logger(__name__) # pylint: disable=invalid-name LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload" class LfsCommands(BaseTransformersCLICommand): """ Implementation of a custom transfer agent for the transfer type "multipart" for git-lfs. This lets users upload large files >5GB 🔥. Spec for LFS custom transfer agent is: https://github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md This introduces two commands to the CLI: 1. $ transformers-cli lfs-enable-largefiles This should be executed once for each model repo that contains a model file >5GB. It's documented in the error message you get if you just try to git push a 5GB file without having enabled it before. 2. $ transformers-cli lfs-multipart-upload This command is called by lfs directly and is not meant to be called by the user. """ @staticmethod def register_subcommand(parser: ArgumentParser): enable_parser = parser.add_parser( "lfs-enable-largefiles", help=( "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB." ), ) enable_parser.add_argument("path", type=str, help="Local path to repository you want to configure.") enable_parser.set_defaults(func=lambda args: LfsEnableCommand(args)) upload_parser = parser.add_parser( LFS_MULTIPART_UPLOAD_COMMAND, help=( "Deprecated: use `huggingface-cli` instead. " "Command will get called by git-lfs, do not call it directly." ), ) upload_parser.set_defaults(func=lambda args: LfsUploadCommand(args)) class LfsEnableCommand: def __init__(self, args): self.args = args def run(self): warnings.warn( "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead." ) local_path = os.path.abspath(self.args.path) if not os.path.isdir(local_path): print("This does not look like a valid git repo.") exit(1) subprocess.run( "git config lfs.customtransfer.multipart.path transformers-cli".split(), check=True, cwd=local_path ) subprocess.run( f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split(), check=True, cwd=local_path, ) print("Local repo set up for largefiles") def write_msg(msg: Dict): """Write out the message in Line delimited JSON.""" msg = json.dumps(msg) + "\n" sys.stdout.write(msg) sys.stdout.flush() def read_msg() -> Optional[Dict]: """Read Line delimited JSON from stdin.""" msg = json.loads(sys.stdin.readline().strip()) if "terminate" in (msg.get("type"), msg.get("event")): # terminate message received return None if msg.get("event") not in ("download", "upload"): logger.critical("Received unexpected message") sys.exit(1) return msg class FileSlice(AbstractContextManager): """ File-like object that only reads a slice of a file Inspired by stackoverflow.com/a/29838711/593036 """ def __init__(self, filepath: str, seek_from: int, read_limit: int): self.filepath = filepath self.seek_from = seek_from self.read_limit = read_limit self.n_seen = 0 def __enter__(self): self.f = open(self.filepath, "rb") self.f.seek(self.seek_from) return self def __len__(self): total_length = os.fstat(self.f.fileno()).st_size return min(self.read_limit, total_length - self.seek_from) def read(self, n=-1): if self.n_seen >= self.read_limit: return b"" remaining_amount = self.read_limit - self.n_seen data = self.f.read(remaining_amount if n < 0 else min(n, remaining_amount)) self.n_seen += len(data) return data def __iter__(self): yield self.read(n=4 * 1024 * 1024) def __exit__(self, *args): self.f.close() class LfsUploadCommand: def __init__(self, args): self.args = args def run(self): # Immediately after invoking a custom transfer process, git-lfs # sends initiation data to the process over stdin. # This tells the process useful information about the configuration. init_msg = json.loads(sys.stdin.readline().strip()) if not (init_msg.get("event") == "init" and init_msg.get("operation") == "upload"): write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}) sys.exit(1) # The transfer process should use the information it needs from the # initiation structure, and also perform any one-off setup tasks it # needs to do. It should then respond on stdout with a simple empty # confirmation structure, as follows: write_msg({}) # After the initiation exchange, git-lfs will send any number of # transfer requests to the stdin of the transfer process, in a serial sequence. while True: msg = read_msg() if msg is None: # When all transfers have been processed, git-lfs will send # a terminate event to the stdin of the transfer process. # On receiving this message the transfer process should # clean up and terminate. No response is expected. sys.exit(0) oid = msg["oid"] filepath = msg["path"] completion_url = msg["action"]["href"] header = msg["action"]["header"] chunk_size = int(header.pop("chunk_size")) presigned_urls: List[str] = list(header.values()) parts = [] for i, presigned_url in enumerate(presigned_urls): with FileSlice(filepath, seek_from=i * chunk_size, read_limit=chunk_size) as data: r = requests.put(presigned_url, data=data) r.raise_for_status() parts.append( { "etag": r.headers.get("etag"), "partNumber": i + 1, } ) # In order to support progress reporting while data is uploading / downloading, # the transfer process should post messages to stdout write_msg( { "event": "progress", "oid": oid, "bytesSoFar": (i + 1) * chunk_size, "bytesSinceLast": chunk_size, } ) # Not precise but that's ok. r = requests.post( completion_url, json={ "oid": oid, "parts": parts, }, ) r.raise_for_status() write_msg({"event": "complete", "oid": oid})

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/audio-classification/run_audio_classification.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import sys import warnings from dataclasses import dataclass, field from random import randint from typing import Optional import datasets import numpy as np from datasets import DatasetDict, load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt") def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000): """Randomly sample chunks of `max_length` seconds from the input audio""" sample_length = int(round(sample_rate * max_length)) if len(wav) <= sample_length: return wav random_offset = randint(0, len(wav) - sample_length - 1) return wav[random_offset : random_offset + sample_length] @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: Optional[str] = field(default=None, metadata={"help": "Name of a dataset from the datasets package"}) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field( default=None, metadata={"help": "A file containing the training audio paths and labels."} ) eval_file: Optional[str] = field( default=None, metadata={"help": "A file containing the validation audio paths and labels."} ) train_split_name: str = field( default="train", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) eval_split_name: str = field( default="validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to 'validation'" ) }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) label_column_name: str = field( default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_length_seconds: float = field( default=20, metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}, ) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="facebook/wav2vec2-base", metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) feature_extractor_name: Optional[str] = field( default=None, metadata={"help": "Name or path of preprocessor config."} ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_mask: bool = field( default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."} ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) freeze_feature_extractor: Optional[bool] = field( default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."} ) ignore_mismatched_sizes: bool = field( default=False, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, ) def __post_init__(self): if not self.freeze_feature_extractor and self.freeze_feature_encoder: warnings.warn( "The argument `--freeze_feature_extractor` is deprecated and " "will be removed in a future version. Use `--freeze_feature_encoder`" "instead. Setting `freeze_feature_encoder==True`.", FutureWarning, ) if self.freeze_feature_extractor and not self.freeze_feature_encoder: raise ValueError( "The argument `--freeze_feature_extractor` is deprecated and " "should not be used in combination with `--freeze_feature_encoder`." "Only make use of `--freeze_feature_encoder`." ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_audio_classification", model_args, data_args) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} " + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to train from scratch." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Initialize our dataset and prepare it for the audio classification task. raw_datasets = DatasetDict() raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=True if model_args.use_auth_token else None, ) raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=True if model_args.use_auth_token else None, ) if data_args.audio_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--audio_column_name` to the correct audio column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) if data_args.label_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--label_column_name` to the correct text column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over # transformer outputs in the classifier, but it doesn't always lead to better accuracy feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name or model_args.model_name_or_path, return_attention_mask=model_args.attention_mask, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) # `datasets` takes care of automatically loading and resampling the audio, # so we just need to set the correct target sampling rate. raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) def train_transforms(batch): """Apply train_transforms across a batch.""" output_batch = {"input_values": []} for audio in batch[data_args.audio_column_name]: wav = random_subsample( audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate ) output_batch["input_values"].append(wav) output_batch["labels"] = [label for label in batch[data_args.label_column_name]] return output_batch def val_transforms(batch): """Apply val_transforms across a batch.""" output_batch = {"input_values": []} for audio in batch[data_args.audio_column_name]: wav = audio["array"] output_batch["input_values"].append(wav) output_batch["labels"] = [label for label in batch[data_args.label_column_name]] return output_batch # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = raw_datasets["train"].features[data_args.label_column_name].names label2id, id2label = dict(), dict() for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label # Load the accuracy metric from the datasets package metric = evaluate.load("accuracy") # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with # `predictions` and `label_ids` fields) and has to return a dictionary string to float. def compute_metrics(eval_pred): """Computes accuracy on a batch of predictions""" predictions = np.argmax(eval_pred.predictions, axis=1) return metric.compute(predictions=predictions, references=eval_pred.label_ids) config = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path, num_labels=len(labels), label2id=label2id, id2label=id2label, finetuning_task="audio-classification", cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) model = AutoModelForAudioClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, ) # freeze the convolutional waveform encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() if training_args.do_train: if data_args.max_train_samples is not None: raw_datasets["train"] = ( raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples)) ) # Set the training transforms raw_datasets["train"].set_transform(train_transforms, output_all_columns=False) if training_args.do_eval: if data_args.max_eval_samples is not None: raw_datasets["eval"] = ( raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples)) ) # Set the validation transforms raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False) # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=raw_datasets["train"] if training_args.do_train else None, eval_dataset=raw_datasets["eval"] if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=feature_extractor, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "audio-classification", "dataset": data_args.dataset_name, "tags": ["audio-classification"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import sys import warnings from dataclasses import dataclass, field from random import randint from typing import Optional import datasets import numpy as np from datasets import DatasetDict, load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt") def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000): """Randomly sample chunks of `max_length` seconds from the input audio""" sample_length = int(round(sample_rate * max_length)) if len(wav) <= sample_length: return wav random_offset = randint(0, len(wav) - sample_length - 1) return wav[random_offset : random_offset + sample_length] @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: Optional[str] = field(default=None, metadata={"help": "Name of a dataset from the datasets package"}) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field( default=None, metadata={"help": "A file containing the training audio paths and labels."} ) eval_file: Optional[str] = field( default=None, metadata={"help": "A file containing the validation audio paths and labels."} ) train_split_name: str = field( default="train", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) eval_split_name: str = field( default="validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to 'validation'" ) }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) label_column_name: str = field( default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_length_seconds: float = field( default=20, metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}, ) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="facebook/wav2vec2-base", metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) feature_extractor_name: Optional[str] = field( default=None, metadata={"help": "Name or path of preprocessor config."} ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_mask: bool = field( default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."} ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) freeze_feature_extractor: Optional[bool] = field( default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."} ) ignore_mismatched_sizes: bool = field( default=False, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, ) def __post_init__(self): if not self.freeze_feature_extractor and self.freeze_feature_encoder: warnings.warn( "The argument `--freeze_feature_extractor` is deprecated and " "will be removed in a future version. Use `--freeze_feature_encoder`" "instead. Setting `freeze_feature_encoder==True`.", FutureWarning, ) if self.freeze_feature_extractor and not self.freeze_feature_encoder: raise ValueError( "The argument `--freeze_feature_extractor` is deprecated and " "should not be used in combination with `--freeze_feature_encoder`." "Only make use of `--freeze_feature_encoder`." ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_audio_classification", model_args, data_args) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} " + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to train from scratch." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Initialize our dataset and prepare it for the audio classification task. raw_datasets = DatasetDict() raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=True if model_args.use_auth_token else None, ) raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=True if model_args.use_auth_token else None, ) if data_args.audio_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--audio_column_name` to the correct audio column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) if data_args.label_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--label_column_name` to the correct text column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over # transformer outputs in the classifier, but it doesn't always lead to better accuracy feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name or model_args.model_name_or_path, return_attention_mask=model_args.attention_mask, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) # `datasets` takes care of automatically loading and resampling the audio, # so we just need to set the correct target sampling rate. raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) def train_transforms(batch): """Apply train_transforms across a batch.""" output_batch = {"input_values": []} for audio in batch[data_args.audio_column_name]: wav = random_subsample( audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate ) output_batch["input_values"].append(wav) output_batch["labels"] = [label for label in batch[data_args.label_column_name]] return output_batch def val_transforms(batch): """Apply val_transforms across a batch.""" output_batch = {"input_values": []} for audio in batch[data_args.audio_column_name]: wav = audio["array"] output_batch["input_values"].append(wav) output_batch["labels"] = [label for label in batch[data_args.label_column_name]] return output_batch # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = raw_datasets["train"].features[data_args.label_column_name].names label2id, id2label = dict(), dict() for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label # Load the accuracy metric from the datasets package metric = evaluate.load("accuracy") # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with # `predictions` and `label_ids` fields) and has to return a dictionary string to float. def compute_metrics(eval_pred): """Computes accuracy on a batch of predictions""" predictions = np.argmax(eval_pred.predictions, axis=1) return metric.compute(predictions=predictions, references=eval_pred.label_ids) config = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path, num_labels=len(labels), label2id=label2id, id2label=id2label, finetuning_task="audio-classification", cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) model = AutoModelForAudioClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, ) # freeze the convolutional waveform encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() if training_args.do_train: if data_args.max_train_samples is not None: raw_datasets["train"] = ( raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples)) ) # Set the training transforms raw_datasets["train"].set_transform(train_transforms, output_all_columns=False) if training_args.do_eval: if data_args.max_eval_samples is not None: raw_datasets["eval"] = ( raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples)) ) # Set the validation transforms raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False) # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=raw_datasets["train"] if training_args.do_train else None, eval_dataset=raw_datasets["eval"] if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=feature_extractor, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "audio-classification", "dataset": data_args.dataset_name, "tags": ["audio-classification"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/sagemaker/test_multi_node_data_parallel.py

import json import os import subprocess import unittest from ast import literal_eval import pytest from parameterized import parameterized, parameterized_class from . import is_sagemaker_available if is_sagemaker_available(): from sagemaker import Session, TrainingJobAnalytics from sagemaker.huggingface import HuggingFace @pytest.mark.skipif( literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True, reason="Skipping test because should only be run when releasing minor transformers version", ) @pytest.mark.usefixtures("sm_env") @parameterized_class( [ { "framework": "pytorch", "script": "run_glue.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.p3.16xlarge", "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6}, }, { "framework": "pytorch", "script": "run_ddp.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.p3.16xlarge", "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6}, }, { "framework": "tensorflow", "script": "run_tf_dist.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.p3.16xlarge", "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7}, }, ] ) class MultiNodeTest(unittest.TestCase): def setUp(self): if self.framework == "pytorch": subprocess.run( f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(), encoding="utf-8", check=True, ) assert hasattr(self, "env") def create_estimator(self, instance_count): job_name = f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}" # distributed data settings distribution = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None # creates estimator return HuggingFace( entry_point=self.script, source_dir=self.env.test_path, role=self.env.role, image_uri=self.env.image_uri, base_job_name=job_name, instance_count=instance_count, instance_type=self.instance_type, debugger_hook_config=False, hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}, metric_definitions=self.env.metric_definitions, distribution=distribution, py_version="py36", ) def save_results_as_csv(self, job_name): TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv") # @parameterized.expand([(2,), (4,),]) @parameterized.expand([(2,)]) def test_script(self, instance_count): # create estimator estimator = self.create_estimator(instance_count) # run training estimator.fit() # result dataframe result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe() # extract kpis eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]) eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]) # get train time from SageMaker job, this includes starting, preprocessing, stopping train_runtime = ( Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999) ) # assert kpis assert train_runtime <= self.results["train_runtime"] assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy) assert all(t <= self.results["eval_loss"] for t in eval_loss) # dump tests result into json file to share in PR with open(f"{estimator.latest_training_job.name}.json", "w") as outfile: json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)

import json import os import subprocess import unittest from ast import literal_eval import pytest from parameterized import parameterized, parameterized_class from . import is_sagemaker_available if is_sagemaker_available(): from sagemaker import Session, TrainingJobAnalytics from sagemaker.huggingface import HuggingFace @pytest.mark.skipif( literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True, reason="Skipping test because should only be run when releasing minor transformers version", ) @pytest.mark.usefixtures("sm_env") @parameterized_class( [ { "framework": "pytorch", "script": "run_glue.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.p3.16xlarge", "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6}, }, { "framework": "pytorch", "script": "run_ddp.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.p3.16xlarge", "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6}, }, { "framework": "tensorflow", "script": "run_tf_dist.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.p3.16xlarge", "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7}, }, ] ) class MultiNodeTest(unittest.TestCase): def setUp(self): if self.framework == "pytorch": subprocess.run( f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(), encoding="utf-8", check=True, ) assert hasattr(self, "env") def create_estimator(self, instance_count): job_name = f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}" # distributed data settings distribution = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None # creates estimator return HuggingFace( entry_point=self.script, source_dir=self.env.test_path, role=self.env.role, image_uri=self.env.image_uri, base_job_name=job_name, instance_count=instance_count, instance_type=self.instance_type, debugger_hook_config=False, hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}, metric_definitions=self.env.metric_definitions, distribution=distribution, py_version="py36", ) def save_results_as_csv(self, job_name): TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv") # @parameterized.expand([(2,), (4,),]) @parameterized.expand([(2,)]) def test_script(self, instance_count): # create estimator estimator = self.create_estimator(instance_count) # run training estimator.fit() # result dataframe result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe() # extract kpis eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]) eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]) # get train time from SageMaker job, this includes starting, preprocessing, stopping train_runtime = ( Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999) ) # assert kpis assert train_runtime <= self.results["train_runtime"] assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy) assert all(t <= self.results["eval_loss"] for t in eval_loss) # dump tests result into json file to share in PR with open(f"{estimator.latest_training_job.name}.json", "w") as outfile: json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/longformer/tokenization_longformer_fast.py

# coding=utf-8 # Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization classes for Longformer.""" import json from typing import List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_longformer import LongformerTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json", "allenai/longformer-large-4096": ( "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json" ), "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json" ), }, "merges_file": { "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt", "allenai/longformer-large-4096": ( "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt" ), "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt" ), }, "tokenizer_file": { "allenai/longformer-base-4096": ( "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json" ), "allenai/longformer-large-4096": ( "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json" ), "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json" ), }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "allenai/longformer-base-4096": 4096, "allenai/longformer-large-4096": 4096, "allenai/longformer-large-4096-finetuned-triviaqa": 4096, "allenai/longformer-base-4096-extra.pos.embd.only": 4096, "allenai/longformer-large-4096-extra.pos.embd.only": 4096, } # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast with roberta-base->allenai/longformer-base-4096, RoBERTa->Longformer all-casing, Roberta->Longformer class LongformerTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" Longformer tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import LongformerTokenizerFast >>> tokenizer = LongformerTokenizerFast.from_pretrained("allenai/longformer-base-4096") >>> tokenizer("Hello world")['input_ids'] [0, 31414, 232, 328, 2] >>> tokenizer(" Hello world")['input_ids'] [0, 20920, 232, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (Longformer tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = LongformerTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. Longformer tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on Longformer. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return output return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

# coding=utf-8 # Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization classes for Longformer.""" import json from typing import List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_longformer import LongformerTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json", "allenai/longformer-large-4096": ( "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json" ), "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json" ), }, "merges_file": { "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt", "allenai/longformer-large-4096": ( "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt" ), "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt" ), }, "tokenizer_file": { "allenai/longformer-base-4096": ( "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json" ), "allenai/longformer-large-4096": ( "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json" ), "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json" ), }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "allenai/longformer-base-4096": 4096, "allenai/longformer-large-4096": 4096, "allenai/longformer-large-4096-finetuned-triviaqa": 4096, "allenai/longformer-base-4096-extra.pos.embd.only": 4096, "allenai/longformer-large-4096-extra.pos.embd.only": 4096, } # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast with roberta-base->allenai/longformer-base-4096, RoBERTa->Longformer all-casing, Roberta->Longformer class LongformerTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" Longformer tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import LongformerTokenizerFast >>> tokenizer = LongformerTokenizerFast.from_pretrained("allenai/longformer-base-4096") >>> tokenizer("Hello world")['input_ids'] [0, 31414, 232, 328, 2] >>> tokenizer(" Hello world")['input_ids'] [0, 20920, 232, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (Longformer tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = LongformerTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. Longformer tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on Longformer. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return output return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/utils/generic.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Generic utilities """ import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp class cached_property(property): """ Descriptor that mimics @property but caches output in member variable. From tensorflow_datasets Built-in in functools from Python 3.8. """ def __get__(self, obj, objtype=None): # See docs.python.org/3/howto/descriptor.html#properties if obj is None: return self if self.fget is None: raise AttributeError("unreadable attribute") attr = "__cached_" + self.fget.__name__ cached = getattr(obj, attr, None) if cached is None: cached = self.fget(obj) setattr(obj, attr, cached) return cached def is_tensor(x): """ Tests if `x` is a `torch.Tensor`, `tf.Tensor`, `jaxlib.xla_extension.DeviceArray` or `np.ndarray`. """ if is_torch_fx_proxy(x): return True if is_torch_available(): import torch if isinstance(x, torch.Tensor): return True if is_tf_available(): import tensorflow as tf if isinstance(x, tf.Tensor): return True if is_flax_available(): import jax.numpy as jnp from jax.core import Tracer if isinstance(x, (jnp.ndarray, Tracer)): return True return isinstance(x, np.ndarray) def _is_numpy(x): return isinstance(x, np.ndarray) def is_numpy_array(x): """ Tests if `x` is a numpy array or not. """ return _is_numpy(x) def _is_torch(x): import torch return isinstance(x, torch.Tensor) def is_torch_tensor(x): """ Tests if `x` is a torch tensor or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch(x) def _is_torch_device(x): import torch return isinstance(x, torch.device) def is_torch_device(x): """ Tests if `x` is a torch device or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch_device(x) def _is_tensorflow(x): import tensorflow as tf return isinstance(x, tf.Tensor) def is_tf_tensor(x): """ Tests if `x` is a tensorflow tensor or not. Safe to call even if tensorflow is not installed. """ return False if not is_tf_available() else _is_tensorflow(x) def _is_jax(x): import jax.numpy as jnp # noqa: F811 return isinstance(x, jnp.ndarray) def is_jax_tensor(x): """ Tests if `x` is a Jax tensor or not. Safe to call even if jax is not installed. """ return False if not is_flax_available() else _is_jax(x) def to_py_obj(obj): """ Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list. """ if isinstance(obj, (dict, UserDict)): return {k: to_py_obj(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return [to_py_obj(o) for o in obj] elif is_tf_tensor(obj): return obj.numpy().tolist() elif is_torch_tensor(obj): return obj.detach().cpu().tolist() elif is_jax_tensor(obj): return np.asarray(obj).tolist() elif isinstance(obj, (np.ndarray, np.number)): # tolist also works on 0d np arrays return obj.tolist() else: return obj def to_numpy(obj): """ Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a Numpy array. """ if isinstance(obj, (dict, UserDict)): return {k: to_numpy(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return np.array(obj) elif is_tf_tensor(obj): return obj.numpy() elif is_torch_tensor(obj): return obj.detach().cpu().numpy() elif is_jax_tensor(obj): return np.asarray(obj) else: return obj class ModelOutput(OrderedDict): """ Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular python dictionary. <Tip warning={true}> You can't unpack a `ModelOutput` directly. Use the [`~utils.ModelOutput.to_tuple`] method to convert it to a tuple before. </Tip> """ def __post_init__(self): class_fields = fields(self) # Safety and consistency checks if not len(class_fields): raise ValueError(f"{self.__class__.__name__} has no fields.") if not all(field.default is None for field in class_fields[1:]): raise ValueError(f"{self.__class__.__name__} should not have more than one required field.") first_field = getattr(self, class_fields[0].name) other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:]) if other_fields_are_none and not is_tensor(first_field): if isinstance(first_field, dict): iterator = first_field.items() first_field_iterator = True else: try: iterator = iter(first_field) first_field_iterator = True except TypeError: first_field_iterator = False # if we provided an iterator as first field and the iterator is a (key, value) iterator # set the associated fields if first_field_iterator: for element in iterator: if ( not isinstance(element, (list, tuple)) or not len(element) == 2 or not isinstance(element[0], str) ): break setattr(self, element[0], element[1]) if element[1] is not None: self[element[0]] = element[1] elif first_field is not None: self[class_fields[0].name] = first_field else: for field in class_fields: v = getattr(self, field.name) if v is not None: self[field.name] = v def __delitem__(self, *args, **kwargs): raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.") def setdefault(self, *args, **kwargs): raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.") def pop(self, *args, **kwargs): raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.") def update(self, *args, **kwargs): raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.") def __getitem__(self, k): if isinstance(k, str): inner_dict = {k: v for (k, v) in self.items()} return inner_dict[k] else: return self.to_tuple()[k] def __setattr__(self, name, value): if name in self.keys() and value is not None: # Don't call self.__setitem__ to avoid recursion errors super().__setitem__(name, value) super().__setattr__(name, value) def __setitem__(self, key, value): # Will raise a KeyException if needed super().__setitem__(key, value) # Don't call self.__setattr__ to avoid recursion errors super().__setattr__(key, value) def to_tuple(self) -> Tuple[Any]: """ Convert self to a tuple containing all the attributes/keys that are not `None`. """ return tuple(self[k] for k in self.keys()) class ExplicitEnum(str, Enum): """ Enum with more explicit error message for missing values. """ @classmethod def _missing_(cls, value): raise ValueError( f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}" ) class PaddingStrategy(ExplicitEnum): """ Possible values for the `padding` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ LONGEST = "longest" MAX_LENGTH = "max_length" DO_NOT_PAD = "do_not_pad" class TensorType(ExplicitEnum): """ Possible values for the `return_tensors` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ PYTORCH = "pt" TENSORFLOW = "tf" NUMPY = "np" JAX = "jax" class ContextManagers: """ Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers` in the `fastcore` library. """ def __init__(self, context_managers: List[ContextManager]): self.context_managers = context_managers self.stack = ExitStack() def __enter__(self): for context_manager in self.context_managers: self.stack.enter_context(context_manager) def __exit__(self, *args, **kwargs): self.stack.__exit__(*args, **kwargs) def can_return_loss(model_class): """ Check if a given model can return loss. Args: model_class (`type`): The class of the model. """ model_name = model_class.__name__ if model_name.startswith("TF"): signature = inspect.signature(model_class.call) elif model_name.startswith("Flax"): signature = inspect.signature(model_class.__call__) else: signature = inspect.signature(model_class.forward) for p in signature.parameters: if p == "return_loss" and signature.parameters[p].default is True: return True return False def find_labels(model_class): """ Find the labels used by a given model. Args: model_class (`type`): The class of the model. """ model_name = model_class.__name__ if model_name.startswith("TF"): signature = inspect.signature(model_class.call) elif model_name.startswith("Flax"): signature = inspect.signature(model_class.__call__) else: signature = inspect.signature(model_class.forward) if "QuestionAnswering" in model_name: return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")] else: return [p for p in signature.parameters if "label" in p] def flatten_dict(d: MutableMapping, parent_key: str = "", delimiter: str = "."): """Flatten a nested dict into a single level dict.""" def _flatten_dict(d, parent_key="", delimiter="."): for k, v in d.items(): key = str(parent_key) + delimiter + str(k) if parent_key else k if v and isinstance(v, MutableMapping): yield from flatten_dict(v, key, delimiter=delimiter).items() else: yield key, v return dict(_flatten_dict(d, parent_key, delimiter)) @contextmanager def working_or_temp_dir(working_dir, use_temp_dir: bool = False): if use_temp_dir: with tempfile.TemporaryDirectory() as tmp_dir: yield tmp_dir else: yield working_dir def transpose(array, axes=None): """ Framework-agnostic version of `numpy.transpose` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.transpose(array, axes=axes) elif is_torch_tensor(array): return array.T if axes is None else array.permute(*axes) elif is_tf_tensor(array): return tf.transpose(array, perm=axes) elif is_jax_tensor(array): return jnp.transpose(array, axes=axes) else: raise ValueError(f"Type not supported for transpose: {type(array)}.") def reshape(array, newshape): """ Framework-agnostic version of `numpy.reshape` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.reshape(array, newshape) elif is_torch_tensor(array): return array.reshape(*newshape) elif is_tf_tensor(array): return tf.reshape(array, newshape) elif is_jax_tensor(array): return jnp.reshape(array, newshape) else: raise ValueError(f"Type not supported for reshape: {type(array)}.") def squeeze(array, axis=None): """ Framework-agnostic version of `numpy.squeeze` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.squeeze(array, axis=axis) elif is_torch_tensor(array): return array.squeeze() if axis is None else array.squeeze(dim=axis) elif is_tf_tensor(array): return tf.squeeze(array, axis=axis) elif is_jax_tensor(array): return jnp.squeeze(array, axis=axis) else: raise ValueError(f"Type not supported for squeeze: {type(array)}.") def expand_dims(array, axis): """ Framework-agnostic version of `numpy.expand_dims` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.expand_dims(array, axis) elif is_torch_tensor(array): return array.unsqueeze(dim=axis) elif is_tf_tensor(array): return tf.expand_dims(array, axis=axis) elif is_jax_tensor(array): return jnp.expand_dims(array, axis=axis) else: raise ValueError(f"Type not supported for expand_dims: {type(array)}.") def tensor_size(array): """ Framework-agnostic version of `numpy.size` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.size(array) elif is_torch_tensor(array): return array.numel() elif is_tf_tensor(array): return tf.size(array) elif is_jax_tensor(array): return array.size else: raise ValueError(f"Type not supported for expand_dims: {type(array)}.")

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Generic utilities """ import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp class cached_property(property): """ Descriptor that mimics @property but caches output in member variable. From tensorflow_datasets Built-in in functools from Python 3.8. """ def __get__(self, obj, objtype=None): # See docs.python.org/3/howto/descriptor.html#properties if obj is None: return self if self.fget is None: raise AttributeError("unreadable attribute") attr = "__cached_" + self.fget.__name__ cached = getattr(obj, attr, None) if cached is None: cached = self.fget(obj) setattr(obj, attr, cached) return cached def is_tensor(x): """ Tests if `x` is a `torch.Tensor`, `tf.Tensor`, `jaxlib.xla_extension.DeviceArray` or `np.ndarray`. """ if is_torch_fx_proxy(x): return True if is_torch_available(): import torch if isinstance(x, torch.Tensor): return True if is_tf_available(): import tensorflow as tf if isinstance(x, tf.Tensor): return True if is_flax_available(): import jax.numpy as jnp from jax.core import Tracer if isinstance(x, (jnp.ndarray, Tracer)): return True return isinstance(x, np.ndarray) def _is_numpy(x): return isinstance(x, np.ndarray) def is_numpy_array(x): """ Tests if `x` is a numpy array or not. """ return _is_numpy(x) def _is_torch(x): import torch return isinstance(x, torch.Tensor) def is_torch_tensor(x): """ Tests if `x` is a torch tensor or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch(x) def _is_torch_device(x): import torch return isinstance(x, torch.device) def is_torch_device(x): """ Tests if `x` is a torch device or not. Safe to call even if torch is not installed. """ return False if not is_torch_available() else _is_torch_device(x) def _is_tensorflow(x): import tensorflow as tf return isinstance(x, tf.Tensor) def is_tf_tensor(x): """ Tests if `x` is a tensorflow tensor or not. Safe to call even if tensorflow is not installed. """ return False if not is_tf_available() else _is_tensorflow(x) def _is_jax(x): import jax.numpy as jnp # noqa: F811 return isinstance(x, jnp.ndarray) def is_jax_tensor(x): """ Tests if `x` is a Jax tensor or not. Safe to call even if jax is not installed. """ return False if not is_flax_available() else _is_jax(x) def to_py_obj(obj): """ Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list. """ if isinstance(obj, (dict, UserDict)): return {k: to_py_obj(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return [to_py_obj(o) for o in obj] elif is_tf_tensor(obj): return obj.numpy().tolist() elif is_torch_tensor(obj): return obj.detach().cpu().tolist() elif is_jax_tensor(obj): return np.asarray(obj).tolist() elif isinstance(obj, (np.ndarray, np.number)): # tolist also works on 0d np arrays return obj.tolist() else: return obj def to_numpy(obj): """ Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a Numpy array. """ if isinstance(obj, (dict, UserDict)): return {k: to_numpy(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return np.array(obj) elif is_tf_tensor(obj): return obj.numpy() elif is_torch_tensor(obj): return obj.detach().cpu().numpy() elif is_jax_tensor(obj): return np.asarray(obj) else: return obj class ModelOutput(OrderedDict): """ Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular python dictionary. <Tip warning={true}> You can't unpack a `ModelOutput` directly. Use the [`~utils.ModelOutput.to_tuple`] method to convert it to a tuple before. </Tip> """ def __post_init__(self): class_fields = fields(self) # Safety and consistency checks if not len(class_fields): raise ValueError(f"{self.__class__.__name__} has no fields.") if not all(field.default is None for field in class_fields[1:]): raise ValueError(f"{self.__class__.__name__} should not have more than one required field.") first_field = getattr(self, class_fields[0].name) other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:]) if other_fields_are_none and not is_tensor(first_field): if isinstance(first_field, dict): iterator = first_field.items() first_field_iterator = True else: try: iterator = iter(first_field) first_field_iterator = True except TypeError: first_field_iterator = False # if we provided an iterator as first field and the iterator is a (key, value) iterator # set the associated fields if first_field_iterator: for element in iterator: if ( not isinstance(element, (list, tuple)) or not len(element) == 2 or not isinstance(element[0], str) ): break setattr(self, element[0], element[1]) if element[1] is not None: self[element[0]] = element[1] elif first_field is not None: self[class_fields[0].name] = first_field else: for field in class_fields: v = getattr(self, field.name) if v is not None: self[field.name] = v def __delitem__(self, *args, **kwargs): raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.") def setdefault(self, *args, **kwargs): raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.") def pop(self, *args, **kwargs): raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.") def update(self, *args, **kwargs): raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.") def __getitem__(self, k): if isinstance(k, str): inner_dict = {k: v for (k, v) in self.items()} return inner_dict[k] else: return self.to_tuple()[k] def __setattr__(self, name, value): if name in self.keys() and value is not None: # Don't call self.__setitem__ to avoid recursion errors super().__setitem__(name, value) super().__setattr__(name, value) def __setitem__(self, key, value): # Will raise a KeyException if needed super().__setitem__(key, value) # Don't call self.__setattr__ to avoid recursion errors super().__setattr__(key, value) def to_tuple(self) -> Tuple[Any]: """ Convert self to a tuple containing all the attributes/keys that are not `None`. """ return tuple(self[k] for k in self.keys()) class ExplicitEnum(str, Enum): """ Enum with more explicit error message for missing values. """ @classmethod def _missing_(cls, value): raise ValueError( f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}" ) class PaddingStrategy(ExplicitEnum): """ Possible values for the `padding` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ LONGEST = "longest" MAX_LENGTH = "max_length" DO_NOT_PAD = "do_not_pad" class TensorType(ExplicitEnum): """ Possible values for the `return_tensors` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ PYTORCH = "pt" TENSORFLOW = "tf" NUMPY = "np" JAX = "jax" class ContextManagers: """ Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers` in the `fastcore` library. """ def __init__(self, context_managers: List[ContextManager]): self.context_managers = context_managers self.stack = ExitStack() def __enter__(self): for context_manager in self.context_managers: self.stack.enter_context(context_manager) def __exit__(self, *args, **kwargs): self.stack.__exit__(*args, **kwargs) def can_return_loss(model_class): """ Check if a given model can return loss. Args: model_class (`type`): The class of the model. """ model_name = model_class.__name__ if model_name.startswith("TF"): signature = inspect.signature(model_class.call) elif model_name.startswith("Flax"): signature = inspect.signature(model_class.__call__) else: signature = inspect.signature(model_class.forward) for p in signature.parameters: if p == "return_loss" and signature.parameters[p].default is True: return True return False def find_labels(model_class): """ Find the labels used by a given model. Args: model_class (`type`): The class of the model. """ model_name = model_class.__name__ if model_name.startswith("TF"): signature = inspect.signature(model_class.call) elif model_name.startswith("Flax"): signature = inspect.signature(model_class.__call__) else: signature = inspect.signature(model_class.forward) if "QuestionAnswering" in model_name: return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")] else: return [p for p in signature.parameters if "label" in p] def flatten_dict(d: MutableMapping, parent_key: str = "", delimiter: str = "."): """Flatten a nested dict into a single level dict.""" def _flatten_dict(d, parent_key="", delimiter="."): for k, v in d.items(): key = str(parent_key) + delimiter + str(k) if parent_key else k if v and isinstance(v, MutableMapping): yield from flatten_dict(v, key, delimiter=delimiter).items() else: yield key, v return dict(_flatten_dict(d, parent_key, delimiter)) @contextmanager def working_or_temp_dir(working_dir, use_temp_dir: bool = False): if use_temp_dir: with tempfile.TemporaryDirectory() as tmp_dir: yield tmp_dir else: yield working_dir def transpose(array, axes=None): """ Framework-agnostic version of `numpy.transpose` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.transpose(array, axes=axes) elif is_torch_tensor(array): return array.T if axes is None else array.permute(*axes) elif is_tf_tensor(array): return tf.transpose(array, perm=axes) elif is_jax_tensor(array): return jnp.transpose(array, axes=axes) else: raise ValueError(f"Type not supported for transpose: {type(array)}.") def reshape(array, newshape): """ Framework-agnostic version of `numpy.reshape` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.reshape(array, newshape) elif is_torch_tensor(array): return array.reshape(*newshape) elif is_tf_tensor(array): return tf.reshape(array, newshape) elif is_jax_tensor(array): return jnp.reshape(array, newshape) else: raise ValueError(f"Type not supported for reshape: {type(array)}.") def squeeze(array, axis=None): """ Framework-agnostic version of `numpy.squeeze` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.squeeze(array, axis=axis) elif is_torch_tensor(array): return array.squeeze() if axis is None else array.squeeze(dim=axis) elif is_tf_tensor(array): return tf.squeeze(array, axis=axis) elif is_jax_tensor(array): return jnp.squeeze(array, axis=axis) else: raise ValueError(f"Type not supported for squeeze: {type(array)}.") def expand_dims(array, axis): """ Framework-agnostic version of `numpy.expand_dims` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.expand_dims(array, axis) elif is_torch_tensor(array): return array.unsqueeze(dim=axis) elif is_tf_tensor(array): return tf.expand_dims(array, axis=axis) elif is_jax_tensor(array): return jnp.expand_dims(array, axis=axis) else: raise ValueError(f"Type not supported for expand_dims: {type(array)}.") def tensor_size(array): """ Framework-agnostic version of `numpy.size` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays. """ if is_numpy_array(array): return np.size(array) elif is_torch_tensor(array): return array.numel() elif is_tf_tensor(array): return tf.size(array) elif is_jax_tensor(array): return array.size else: raise ValueError(f"Type not supported for expand_dims: {type(array)}.")

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/openai/test_modeling_tf_openai.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import OpenAIGPTConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask if is_tf_available(): import tensorflow as tf from transformers.models.openai.modeling_tf_openai import ( TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification, TFOpenAIGPTLMHeadModel, TFOpenAIGPTModel, ) class TFOpenAIGPTModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.pad_token_id = self.vocab_size - 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = OpenAIGPTConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, # intermediate_size=self.intermediate_size, # hidden_act=self.hidden_act, # hidden_dropout_prob=self.hidden_dropout_prob, # attention_probs_dropout_prob=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFOpenAIGPTModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_openai_gpt_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFOpenAIGPTLMHeadModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_openai_gpt_double_head( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args ): model = TFOpenAIGPTDoubleHeadsModel(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "mc_token_ids": mc_token_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices)) def create_and_check_openai_gpt_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): config.num_labels = self.num_labels sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "labels": sequence_labels, } model = TFOpenAIGPTForSequenceClassification(config) result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification) if is_tf_available() else () ) all_generative_model_classes = ( (TFOpenAIGPTLMHeadModel,) if is_tf_available() else () ) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFOpenAIGPTModelTester(self) self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_openai_gpt_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs) def test_openai_gpt_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_lm_head(*config_and_inputs) def test_openai_gpt_double_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert name is None else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None def test_openai_gpt_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFOpenAIGPTModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFOPENAIGPTModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_openai_gpt(self): model = TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt") input_ids = tf.convert_to_tensor([[481, 4735, 544]], dtype=tf.int32) # the president is expected_output_ids = [ 481, 4735, 544, 246, 963, 870, 762, 239, 244, 40477, 244, 249, 719, 881, 487, 544, 240, 244, 603, 481, ] # the president is a very good man. " \n " i\'m sure he is, " said the output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import OpenAIGPTConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask if is_tf_available(): import tensorflow as tf from transformers.models.openai.modeling_tf_openai import ( TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification, TFOpenAIGPTLMHeadModel, TFOpenAIGPTModel, ) class TFOpenAIGPTModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.pad_token_id = self.vocab_size - 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = OpenAIGPTConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, # intermediate_size=self.intermediate_size, # hidden_act=self.hidden_act, # hidden_dropout_prob=self.hidden_dropout_prob, # attention_probs_dropout_prob=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFOpenAIGPTModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_openai_gpt_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFOpenAIGPTLMHeadModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_openai_gpt_double_head( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args ): model = TFOpenAIGPTDoubleHeadsModel(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "mc_token_ids": mc_token_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices)) def create_and_check_openai_gpt_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): config.num_labels = self.num_labels sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "labels": sequence_labels, } model = TFOpenAIGPTForSequenceClassification(config) result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification) if is_tf_available() else () ) all_generative_model_classes = ( (TFOpenAIGPTLMHeadModel,) if is_tf_available() else () ) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFOpenAIGPTModelTester(self) self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_openai_gpt_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs) def test_openai_gpt_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_lm_head(*config_and_inputs) def test_openai_gpt_double_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert name is None else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None def test_openai_gpt_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFOpenAIGPTModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFOPENAIGPTModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_openai_gpt(self): model = TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt") input_ids = tf.convert_to_tensor([[481, 4735, 544]], dtype=tf.int32) # the president is expected_output_ids = [ 481, 4735, 544, 246, 963, 870, 762, 239, 244, 40477, 244, 249, 719, 881, 487, 544, 240, 244, 603, 481, ] # the president is a very good man. " \n " i\'m sure he is, " said the output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/swin/convert_swin_simmim_to_pytorch.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swin SimMIM checkpoints from the original repository. URL: https://github.com/microsoft/Swin-Transformer/blob/main/MODELHUB.md#simmim-pretrained-swin-v1-models""" import argparse import torch from PIL import Image import requests from transformers import SwinConfig, SwinForMaskedImageModeling, ViTFeatureExtractor def get_swin_config(model_name): config = SwinConfig(image_size=192) if "base" in model_name: window_size = 6 embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) elif "large" in model_name: window_size = 12 embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) else: raise ValueError("Model not supported, only supports base and large variants") config.window_size = window_size config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads return config def rename_key(name): if "encoder.mask_token" in name: name = name.replace("encoder.mask_token", "embeddings.mask_token") if "encoder.patch_embed.proj" in name: name = name.replace("encoder.patch_embed.proj", "embeddings.patch_embeddings.projection") if "encoder.patch_embed.norm" in name: name = name.replace("encoder.patch_embed.norm", "embeddings.norm") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "encoder.norm.weight": name = "layernorm.weight" if name == "encoder.norm.bias": name = "layernorm.bias" if "decoder" in name: pass else: name = "swin." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "attn_mask" in key: pass elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[2]) block_num = int(key_split[4]) dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[ :dim ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[ -dim: ] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swin_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub): state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] config = get_swin_config(model_name) model = SwinForMaskedImageModeling(config) model.eval() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" feature_extractor = ViTFeatureExtractor(size={"height": 192, "width": 192}) image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits print(outputs.keys()) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and feature extractor for {model_name} to hub") model.push_to_hub(f"microsoft/{model_name}") feature_extractor.push_to_hub(f"microsoft/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="swin-base-simmim-window6-192", type=str, choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"], help="Name of the Swin SimMIM model you'd like to convert.", ) parser.add_argument( "--checkpoint_path", default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth", type=str, help="Path to the original PyTorch checkpoint (.pth file).", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swin SimMIM checkpoints from the original repository. URL: https://github.com/microsoft/Swin-Transformer/blob/main/MODELHUB.md#simmim-pretrained-swin-v1-models""" import argparse import torch from PIL import Image import requests from transformers import SwinConfig, SwinForMaskedImageModeling, ViTFeatureExtractor def get_swin_config(model_name): config = SwinConfig(image_size=192) if "base" in model_name: window_size = 6 embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) elif "large" in model_name: window_size = 12 embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) else: raise ValueError("Model not supported, only supports base and large variants") config.window_size = window_size config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads return config def rename_key(name): if "encoder.mask_token" in name: name = name.replace("encoder.mask_token", "embeddings.mask_token") if "encoder.patch_embed.proj" in name: name = name.replace("encoder.patch_embed.proj", "embeddings.patch_embeddings.projection") if "encoder.patch_embed.norm" in name: name = name.replace("encoder.patch_embed.norm", "embeddings.norm") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "encoder.norm.weight": name = "layernorm.weight" if name == "encoder.norm.bias": name = "layernorm.bias" if "decoder" in name: pass else: name = "swin." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "attn_mask" in key: pass elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[2]) block_num = int(key_split[4]) dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[ :dim ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[ -dim: ] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swin_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub): state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] config = get_swin_config(model_name) model = SwinForMaskedImageModeling(config) model.eval() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" feature_extractor = ViTFeatureExtractor(size={"height": 192, "width": 192}) image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits print(outputs.keys()) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and feature extractor for {model_name} to hub") model.push_to_hub(f"microsoft/{model_name}") feature_extractor.push_to_hub(f"microsoft/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="swin-base-simmim-window6-192", type=str, choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"], help="Name of the Swin SimMIM model you'd like to convert.", ) parser.add_argument( "--checkpoint_path", default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth", type=str, help="Path to the original PyTorch checkpoint (.pth file).", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/gpt2/convert_gpt2_original_tf_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert OpenAI GPT checkpoint.""" import argparse import torch from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2 from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging logging.set_verbosity_info() def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path): # Construct model if gpt2_config_file == "": config = GPT2Config() else: config = GPT2Config.from_json_file(gpt2_config_file) model = GPT2Model(config) # Load weights from numpy load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path) # Save pytorch-model pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME print(f"Save PyTorch model to {pytorch_weights_dump_path}") torch.save(model.state_dict(), pytorch_weights_dump_path) print(f"Save configuration file to {pytorch_config_dump_path}") with open(pytorch_config_dump_path, "w", encoding="utf-8") as f: f.write(config.to_json_string()) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--gpt2_config_file", default="", type=str, help=( "An optional config json file corresponding to the pre-trained OpenAI model. \n" "This specifies the model architecture." ), ) args = parser.parse_args() convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert OpenAI GPT checkpoint.""" import argparse import torch from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2 from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging logging.set_verbosity_info() def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path): # Construct model if gpt2_config_file == "": config = GPT2Config() else: config = GPT2Config.from_json_file(gpt2_config_file) model = GPT2Model(config) # Load weights from numpy load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path) # Save pytorch-model pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME print(f"Save PyTorch model to {pytorch_weights_dump_path}") torch.save(model.state_dict(), pytorch_weights_dump_path) print(f"Save configuration file to {pytorch_config_dump_path}") with open(pytorch_config_dump_path, "w", encoding="utf-8") as f: f.write(config.to_json_string()) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--gpt2_config_file", default="", type=str, help=( "An optional config json file corresponding to the pre-trained OpenAI model. \n" "This specifies the model architecture." ), ) args = parser.parse_args() convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/bert_japanese/__init__.py

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/xlm/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"], "tokenization_xlm": ["XLMTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_xlm"] = [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_xlm"] = [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] if TYPE_CHECKING: from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig from .tokenization_xlm import XLMTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"], "tokenization_xlm": ["XLMTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_xlm"] = [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_xlm"] = [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] if TYPE_CHECKING: from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig from .tokenization_xlm import XLMTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/bartpho/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available _import_structure = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_bartpho"] = ["BartphoTokenizer"] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_bartpho import BartphoTokenizer else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available _import_structure = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_bartpho"] = ["BartphoTokenizer"] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_bartpho import BartphoTokenizer else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/bert/convert_bert_token_dropping_original_tf2_checkpoint_to_pytorch.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script converts a lm-head checkpoint from the "Token Dropping" implementation into a PyTorch-compatible BERT model. The official implementation of "Token Dropping" can be found in the TensorFlow Models repository: https://github.com/tensorflow/models/tree/master/official/projects/token_dropping """ import argparse import tensorflow as tf import torch from transformers import BertConfig, BertForMaskedLM from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertPooler, BertSelfAttention, BertSelfOutput, ) from transformers.utils import logging logging.set_verbosity_info() def convert_checkpoint_to_pytorch(tf_checkpoint_path: str, config_path: str, pytorch_dump_path: str): def get_masked_lm_array(name: str): full_name = f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) def get_encoder_array(name: str): full_name = f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) def get_encoder_layer_array(layer_index: int, name: str): full_name = f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) def get_encoder_attention_layer_array(layer_index: int, name: str, orginal_shape): full_name = f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) array = array.reshape(orginal_shape) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) print(f"Loading model based on config from {config_path}...") config = BertConfig.from_json_file(config_path) model = BertForMaskedLM(config) # Layers for layer_index in range(0, config.num_hidden_layers): layer: BertLayer = model.bert.encoder.layer[layer_index] # Self-attention self_attn: BertSelfAttention = layer.attention.self self_attn.query.weight.data = get_encoder_attention_layer_array( layer_index, "_query_dense/kernel", self_attn.query.weight.data.shape ) self_attn.query.bias.data = get_encoder_attention_layer_array( layer_index, "_query_dense/bias", self_attn.query.bias.data.shape ) self_attn.key.weight.data = get_encoder_attention_layer_array( layer_index, "_key_dense/kernel", self_attn.key.weight.data.shape ) self_attn.key.bias.data = get_encoder_attention_layer_array( layer_index, "_key_dense/bias", self_attn.key.bias.data.shape ) self_attn.value.weight.data = get_encoder_attention_layer_array( layer_index, "_value_dense/kernel", self_attn.value.weight.data.shape ) self_attn.value.bias.data = get_encoder_attention_layer_array( layer_index, "_value_dense/bias", self_attn.value.bias.data.shape ) # Self-attention Output self_output: BertSelfOutput = layer.attention.output self_output.dense.weight.data = get_encoder_attention_layer_array( layer_index, "_output_dense/kernel", self_output.dense.weight.data.shape ) self_output.dense.bias.data = get_encoder_attention_layer_array( layer_index, "_output_dense/bias", self_output.dense.bias.data.shape ) self_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/gamma") self_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/beta") # Intermediate intermediate: BertIntermediate = layer.intermediate intermediate.dense.weight.data = get_encoder_layer_array(layer_index, "_intermediate_dense/kernel") intermediate.dense.bias.data = get_encoder_layer_array(layer_index, "_intermediate_dense/bias") # Output bert_output: BertOutput = layer.output bert_output.dense.weight.data = get_encoder_layer_array(layer_index, "_output_dense/kernel") bert_output.dense.bias.data = get_encoder_layer_array(layer_index, "_output_dense/bias") bert_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_output_layer_norm/gamma") bert_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_output_layer_norm/beta") # Embeddings model.bert.embeddings.position_embeddings.weight.data = get_encoder_array("_position_embedding_layer/embeddings") model.bert.embeddings.token_type_embeddings.weight.data = get_encoder_array("_type_embedding_layer/embeddings") model.bert.embeddings.LayerNorm.weight.data = get_encoder_array("_embedding_norm_layer/gamma") model.bert.embeddings.LayerNorm.bias.data = get_encoder_array("_embedding_norm_layer/beta") # LM Head lm_head = model.cls.predictions.transform lm_head.dense.weight.data = get_masked_lm_array("dense/kernel") lm_head.dense.bias.data = get_masked_lm_array("dense/bias") lm_head.LayerNorm.weight.data = get_masked_lm_array("layer_norm/gamma") lm_head.LayerNorm.bias.data = get_masked_lm_array("layer_norm/beta") model.bert.embeddings.word_embeddings.weight.data = get_masked_lm_array("embedding_table") # Pooling model.bert.pooler = BertPooler(config=config) model.bert.pooler.dense.weight.data: BertPooler = get_encoder_array("_pooler_layer/kernel") model.bert.pooler.dense.bias.data: BertPooler = get_encoder_array("_pooler_layer/bias") # Export final model model.save_pretrained(pytorch_dump_path) # Integration test - should load without any errors ;) new_model = BertForMaskedLM.from_pretrained(pytorch_dump_path) print(new_model.eval()) print("Model conversion was done sucessfully!") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path." ) parser.add_argument( "--bert_config_file", type=str, required=True, help="The config json file corresponding to the BERT model. This specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", type=str, required=True, help="Path to the output PyTorch model.", ) args = parser.parse_args() convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script converts a lm-head checkpoint from the "Token Dropping" implementation into a PyTorch-compatible BERT model. The official implementation of "Token Dropping" can be found in the TensorFlow Models repository: https://github.com/tensorflow/models/tree/master/official/projects/token_dropping """ import argparse import tensorflow as tf import torch from transformers import BertConfig, BertForMaskedLM from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertPooler, BertSelfAttention, BertSelfOutput, ) from transformers.utils import logging logging.set_verbosity_info() def convert_checkpoint_to_pytorch(tf_checkpoint_path: str, config_path: str, pytorch_dump_path: str): def get_masked_lm_array(name: str): full_name = f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) def get_encoder_array(name: str): full_name = f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) def get_encoder_layer_array(layer_index: int, name: str): full_name = f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) def get_encoder_attention_layer_array(layer_index: int, name: str, orginal_shape): full_name = f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE" array = tf.train.load_variable(tf_checkpoint_path, full_name) array = array.reshape(orginal_shape) if "kernel" in name: array = array.transpose() return torch.from_numpy(array) print(f"Loading model based on config from {config_path}...") config = BertConfig.from_json_file(config_path) model = BertForMaskedLM(config) # Layers for layer_index in range(0, config.num_hidden_layers): layer: BertLayer = model.bert.encoder.layer[layer_index] # Self-attention self_attn: BertSelfAttention = layer.attention.self self_attn.query.weight.data = get_encoder_attention_layer_array( layer_index, "_query_dense/kernel", self_attn.query.weight.data.shape ) self_attn.query.bias.data = get_encoder_attention_layer_array( layer_index, "_query_dense/bias", self_attn.query.bias.data.shape ) self_attn.key.weight.data = get_encoder_attention_layer_array( layer_index, "_key_dense/kernel", self_attn.key.weight.data.shape ) self_attn.key.bias.data = get_encoder_attention_layer_array( layer_index, "_key_dense/bias", self_attn.key.bias.data.shape ) self_attn.value.weight.data = get_encoder_attention_layer_array( layer_index, "_value_dense/kernel", self_attn.value.weight.data.shape ) self_attn.value.bias.data = get_encoder_attention_layer_array( layer_index, "_value_dense/bias", self_attn.value.bias.data.shape ) # Self-attention Output self_output: BertSelfOutput = layer.attention.output self_output.dense.weight.data = get_encoder_attention_layer_array( layer_index, "_output_dense/kernel", self_output.dense.weight.data.shape ) self_output.dense.bias.data = get_encoder_attention_layer_array( layer_index, "_output_dense/bias", self_output.dense.bias.data.shape ) self_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/gamma") self_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_attention_layer_norm/beta") # Intermediate intermediate: BertIntermediate = layer.intermediate intermediate.dense.weight.data = get_encoder_layer_array(layer_index, "_intermediate_dense/kernel") intermediate.dense.bias.data = get_encoder_layer_array(layer_index, "_intermediate_dense/bias") # Output bert_output: BertOutput = layer.output bert_output.dense.weight.data = get_encoder_layer_array(layer_index, "_output_dense/kernel") bert_output.dense.bias.data = get_encoder_layer_array(layer_index, "_output_dense/bias") bert_output.LayerNorm.weight.data = get_encoder_layer_array(layer_index, "_output_layer_norm/gamma") bert_output.LayerNorm.bias.data = get_encoder_layer_array(layer_index, "_output_layer_norm/beta") # Embeddings model.bert.embeddings.position_embeddings.weight.data = get_encoder_array("_position_embedding_layer/embeddings") model.bert.embeddings.token_type_embeddings.weight.data = get_encoder_array("_type_embedding_layer/embeddings") model.bert.embeddings.LayerNorm.weight.data = get_encoder_array("_embedding_norm_layer/gamma") model.bert.embeddings.LayerNorm.bias.data = get_encoder_array("_embedding_norm_layer/beta") # LM Head lm_head = model.cls.predictions.transform lm_head.dense.weight.data = get_masked_lm_array("dense/kernel") lm_head.dense.bias.data = get_masked_lm_array("dense/bias") lm_head.LayerNorm.weight.data = get_masked_lm_array("layer_norm/gamma") lm_head.LayerNorm.bias.data = get_masked_lm_array("layer_norm/beta") model.bert.embeddings.word_embeddings.weight.data = get_masked_lm_array("embedding_table") # Pooling model.bert.pooler = BertPooler(config=config) model.bert.pooler.dense.weight.data: BertPooler = get_encoder_array("_pooler_layer/kernel") model.bert.pooler.dense.bias.data: BertPooler = get_encoder_array("_pooler_layer/bias") # Export final model model.save_pretrained(pytorch_dump_path) # Integration test - should load without any errors ;) new_model = BertForMaskedLM.from_pretrained(pytorch_dump_path) print(new_model.eval()) print("Model conversion was done sucessfully!") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path." ) parser.add_argument( "--bert_config_file", type=str, required=True, help="The config json file corresponding to the BERT model. This specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", type=str, required=True, help="Path to the output PyTorch model.", ) args = parser.parse_args() convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py

import copy import itertools from typing import List, Optional, Tuple import torch import torch.nn.functional as F from transformers import BartConfig from transformers.generation import GenerationMixin def _convert_past_list_to_tuple(past_key_values): """ In Bart model, the type of past_key_values is tuple(tuple(torch.FloatTensor)) which is not TorchScript-compatible. To support this, we have to convert it during the export process. This function will convert past values from a list to tuple(tuple(torch.FloatTensor)) for the inner decoder. According to the definition of past_key_values, each inner tuple(torch.FloatTensor) has 4 tensors, so we convert every 4 elements in the list as a tuple(torch.FloatTensor). """ count_of_each_inner_tuple = 4 results = () temp_result = () count_n = len(past_key_values) // count_of_each_inner_tuple for idx in range(count_n): real_idx = idx * count_of_each_inner_tuple temp_result = tuple(past_key_values[real_idx : real_idx + count_of_each_inner_tuple]) results += ((temp_result),) return results class EncoderForONNX(torch.nn.Module): def __init__(self, encoder): super().__init__() self.encoder = encoder def forward(self, input_ids, attention_mask): return self.encoder( input_ids=input_ids, attention_mask=attention_mask, return_dict=False, ) class DecoderForONNX(torch.nn.Module): def __init__(self, decoder): super().__init__() self.decoder = decoder def forward(self, input_ids, encoder_state, attention_mask, past=None): all_results = None if past is not None: all_results = _convert_past_list_to_tuple(past) input_ids = input_ids[:, -1:] last_hidden_state, past_key_values = self.decoder( input_ids=input_ids, encoder_hidden_states=encoder_state, encoder_attention_mask=attention_mask, past_key_values=all_results, return_dict=False, ) past_values = [] for past in past_key_values: past_values = past_values + list(past) return last_hidden_state, past_values def _create_traced_encoder(encoder, input_ids, attention_mask): encoder_c = copy.deepcopy(encoder) encoder_for_onnx = EncoderForONNX(encoder_c) return torch.jit.trace(encoder_for_onnx, (input_ids, attention_mask)) def _create_traced_decoder(decoder, input_ids, encoder_state, attention_mask, past=None): decoder_c = copy.deepcopy(decoder) decoder_for_onnx = DecoderForONNX(decoder_c) past_values = list(itertools.chain.from_iterable(past or ())) # Do this twice so we got 2 different decoders for further work. if past_values: return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask, past_values)) else: return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask)) class BartConfigTS(BartConfig, torch.nn.Module): """ BartConfigTS is a TorchScript-compatible transformers.models.bart.configuration_bart.BartConfig. TorchScript only supports sub-classes of torch.nn.Module. """ def __init__(self, config): BartConfig.__init__(self, config) torch.nn.Module.__init__(self) class MinLengthLogitsProcessorTS(torch.nn.Module): r""" :class:`transformers.LogitsProcessor` enforcing a min-length by setting EOS probability to 0. Args: min_length (:obj:`int`): The minimum length below which the score of :obj:`eos_token_id` is set to :obj:`-float("Inf")`. eos_token_id (:obj:`int`): The id of the `end-of-sequence` token. """ def __init__(self, min_length: int, eos_token_id: int): super().__init__() if not isinstance(min_length, int) or min_length < 0: raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}") if not isinstance(eos_token_id, int) or eos_token_id < 0: raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}") self.min_length = min_length self.eos_token_id = eos_token_id def forward(self, input_ids, scores) -> torch.Tensor: cur_len = input_ids.shape[-1] if cur_len < self.min_length: scores[:, self.eos_token_id] = -float("inf") return scores class BARTGenerator(torch.nn.Module, GenerationMixin): def __init__(self, model): super().__init__() self.config = BartConfigTS(model.config) self.config.force_bos_token_to_be_generated = False self._trace_modules(model) self.logits_processor = MinLengthLogitsProcessorTS(self.config.min_length, self.config.eos_token_id) self.final_logits_weight = model.model.shared.weight self.final_logits_bias = model.final_logits_bias self.decoder_layers = model.config.decoder_layers def _trace_modules(self, model): input_ids = torch.tensor( [ [ 19, 669, 18, 420, 8, 664, 57, 42, 8, 664, 21, 3028, 195, 4445, 331, 1293, 34, 21, 10, 6174, 1100, 6, 69, 104, 42, 32, 2621, 1638, 144, 4, 6174, 558, 108, 4419, 1091, 28, 4, 1668, 9, 1509, 1621, 279, 35, 867, 2734, 85, 11, 2216, 2734, 85, 203, 2244, 7, 6, 15, 8102, 7, 57, 8629, 5, model.config.eos_token_id, ] ], device=model.device, dtype=torch.long, ) attention_mask = torch.tensor( [[True] * input_ids.shape[-1]], device=model.device, dtype=torch.bool, ) self.encoder = _create_traced_encoder(model.get_encoder(), input_ids, attention_mask) encoder_outputs = model.get_encoder()(input_ids, attention_mask=attention_mask, return_dict=True) decoder = model.model.decoder decoder_outputs = decoder(input_ids, attention_mask, encoder_outputs["last_hidden_state"], None, None, None) self.decoder_no_past = _create_traced_decoder( model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask ) self.decoder_with_past = _create_traced_decoder( model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask, decoder_outputs[1] ) def _encoder_forward(self, input_ids, attention_mask): return self.encoder(input_ids, attention_mask)[0] @staticmethod def _init_sequence_length_for_generation( input_ids: torch.LongTensor, max_length: int ) -> Tuple[torch.Tensor, torch.Tensor, int]: unfinished_sequences = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + 1 sequence_lengths = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + max_length cur_len = input_ids.shape[-1] return sequence_lengths, unfinished_sequences, cur_len def _decoder_forward(self, input_ids, encoder_output, attention_mask, past: List[torch.Tensor]): # Update here to use different decoder for different values of past. if past is None or len(past) == 0: decoder_output, past = self.decoder_no_past( input_ids=input_ids, encoder_state=encoder_output, attention_mask=attention_mask ) else: decoder_output, past = self.decoder_with_past( input_ids=input_ids, encoder_state=encoder_output, attention_mask=attention_mask, past=past ) lm_logits = F.linear(decoder_output, self.final_logits_weight, bias=self.final_logits_bias) return lm_logits, past def greedy_search( self, input_ids, encoder_output, attention_mask, max_length, pad_token_id: int, eos_token_id: int ): # init sequence length tensors sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation( input_ids, max_length ) past: List[torch.Tensor] = [] while cur_len < max_length: logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past) next_token_logits = logits[:, -1, :] # pre-process distribution scores = self.logits_processor(input_ids, next_token_logits) # argmax next_tokens = torch.argmax(scores, dim=-1) # add code that transfomers next_tokens to tokens_to_add if eos_token_id is not None: assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined." next_tokens = next_tokens * unfinished_sequences + (pad_token_id) * (1 - unfinished_sequences) # add token and increase length by one input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) # update sequence length if eos_token_id is not None: sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation( sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id ) # stop when there is a </s> in each sentence, or if we exceed the maximul length if unfinished_sequences.max() == 0: break # increase cur_len cur_len = cur_len + 1 return input_ids def _prepare_decoder_input_ids_for_generation( self, input_ids: torch.LongTensor, decoder_start_token_id, bos_token_id: Optional[int] = None, ) -> torch.LongTensor: decoder_input_ids = ( torch.ones((input_ids.shape[0], 1), dtype=input_ids.dtype, device=input_ids.device) * decoder_start_token_id ) return decoder_input_ids def forward(self, input_ids, attention_mask, max_length, decoder_start_token_id): pad_token_id = self.config.pad_token_id bos_token_id = self.config.bos_token_id eos_token_id = self.config.eos_token_id # special case if pad_token_id is not defined if pad_token_id is None and eos_token_id is not None: # Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation. pad_token_id = eos_token_id encoder_output = self._encoder_forward(input_ids, attention_mask) input_ids = self._prepare_decoder_input_ids_for_generation( input_ids, decoder_start_token_id=decoder_start_token_id, bos_token_id=bos_token_id, ) return self.greedy_search( input_ids, encoder_output, attention_mask, max_length=max_length, pad_token_id=pad_token_id, eos_token_id=eos_token_id, ) # TorchScript compatible BeamSearchScorer class BeamSearchScorerTS(torch.nn.Module): def __init__(self): super().__init__() self.max_length: int = 200 self.num_beams: int = 3 self.batch_size: int = 1 self.length_penalty: float = 1.0 self.do_early_stopping: bool = True self.num_beam_hyps_to_keep: int = 1 self.num_beam_groups: int = 1 self.group_size: int = self.num_beams // self.num_beam_groups self._done = torch.zeros(self.batch_size, dtype=torch.bool) self._beam_hyps_count = torch.zeros(self.batch_size, dtype=torch.long) self._beam_hyps_worst_scores = torch.zeros(self.batch_size) + 1e9 self._beam_hyps_max_length: int = self.max_length - 1 self._beam_hyps: List[torch.Tensor] = [torch.zeros(2)] # placeholder for TorchScript compatibility self._beam_scores: List[torch.Tensor] = [torch.zeros(2)] # placeholder for TorchScript compatibility def is_done(self) -> torch.Tensor: return self._done.all() def init( self, batch_size: int, max_length: int, num_beams: int, device: torch.device, length_penalty: float = 1.0, do_early_stopping: bool = False, num_beam_hyps_to_keep: int = 1, num_beam_groups: int = 1, ): self.max_length = max_length self.num_beams = num_beams self.batch_size = batch_size self.length_penalty = length_penalty self.do_early_stopping = do_early_stopping self.num_beam_hyps_to_keep = num_beam_hyps_to_keep self.num_beam_groups = num_beam_groups self.group_size = self.num_beams // self.num_beam_groups # NOTE: TorchScript does not support List of Modules # Rewritten BeamHypotheses with tensors and list of tensors. self._done = torch.zeros(batch_size, dtype=torch.bool, device=device) self._beam_hyps_count = torch.zeros(batch_size, dtype=torch.long, device=device) self._beam_hyps_worst_scores = torch.zeros(batch_size, device=device) + 1e9 self._beam_hyps = [] self._beam_scores = [] self._beam_hyps_max_length = max_length - 1 # ignoring bos_token if not isinstance(num_beams, int) or num_beams <= 1: raise ValueError( f"`num_beams` has to be an integer strictly greater than 1, but is {num_beams}. For `num_beams` == 1," " one should make use of `greedy_search` instead." ) if not isinstance(num_beam_groups, int) or (num_beam_groups > num_beams) or (num_beams % num_beam_groups != 0): raise ValueError( "`num_beam_groups` has to be an integer smaller or equal than `num_beams` and `num_beams` has to be" f" divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}." ) def hypo_len(self, hypo_idx: int): """ Number of hypotheses in the list. """ return self._beam_hyps_count[hypo_idx] def hypo_add(self, hyp: torch.Tensor, sum_logprobs: float, hypo_idx: int): """ Add a new hypothesis to the list. """ score = sum_logprobs / (hyp.shape[-1] ** self.length_penalty) hyps_count = self.hypo_len(hypo_idx) if hyps_count < self.num_beams or score > self._beam_hyps_worst_scores[hypo_idx]: # NOTE: work around difference of torch.sum(empty_tensor) == 0, while error in onnx. # Bug: https://msdata.visualstudio.com/Vienna/_workitems/edit/1486599 beam_idx = ( torch.sum(self._beam_hyps_count[:hypo_idx]) if hypo_idx != 0 else torch.tensor(0, dtype=torch.long) ) self._beam_scores.insert(beam_idx, torch.tensor([score])) self._beam_hyps.insert(beam_idx, hyp) if hyps_count + 1 > self.num_beams: sorted_next_scores, sorted_indices = torch.topk( torch.cat(self._beam_scores)[beam_idx : beam_idx + hyps_count + 1], hyps_count + 1, largest=False ) del self._beam_hyps[int((sorted_indices[0] + beam_idx))] del self._beam_scores[int((sorted_indices[0] + beam_idx))] self._beam_hyps_worst_scores[hypo_idx] = sorted_next_scores[1] else: self._beam_hyps_worst_scores[hypo_idx] = min(score, self._beam_hyps_worst_scores[hypo_idx]) self._beam_hyps_count[hypo_idx] = hyps_count + 1 def hypo_is_done(self, hypo_idx: int, best_sum_logprobs: float, cur_len: int) -> bool: """ If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst one in the heap, then we are done with this sentence. """ if self.hypo_len(hypo_idx) < self.num_beams: return False elif self.do_early_stopping: return True else: cur_score = best_sum_logprobs / cur_len**self.length_penalty ret = self._beam_hyps_worst_scores[hypo_idx].item() >= cur_score return ret def process( self, input_ids: torch.Tensor, next_scores: torch.Tensor, next_tokens: torch.Tensor, next_indices: torch.Tensor, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: cur_len = input_ids.shape[-1] batch_size = len(self._beam_hyps_count) assert batch_size == (input_ids.shape[0] // self.group_size) device = input_ids.device next_beam_scores = torch.zeros((batch_size, self.group_size), dtype=next_scores.dtype, device=device) next_beam_tokens = torch.zeros((batch_size, self.group_size), dtype=next_tokens.dtype, device=device) next_beam_indices = torch.zeros((batch_size, self.group_size), dtype=next_indices.dtype, device=device) for batch_idx in range(batch_size): if self._done[batch_idx]: assert ( self.hypo_len(batch_idx) >= self.num_beams ), "Batch can only be done if at least {} beams have been generated".format(self.num_beams) assert ( eos_token_id is not None and pad_token_id is not None ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined" # pad the batch next_beam_scores[batch_idx, :] = 0 next_beam_tokens[batch_idx, :] = pad_token_id next_beam_indices[batch_idx, :] = 0 continue # next tokens for this sentence beam_idx = 0 for beam_token_rank, (next_token, next_score, next_index) in enumerate( zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx]) ): batch_beam_idx = batch_idx * self.group_size + next_index # add to generated hypotheses if end of sentence if (eos_token_id is not None) and (next_token == eos_token_id): # if beam_token does not belong to top num_beams tokens, it should not be added is_beam_token_worse_than_top_num_beams = beam_token_rank >= self.group_size if is_beam_token_worse_than_top_num_beams: continue self.hypo_add( input_ids[batch_beam_idx].clone(), next_score.item(), batch_idx, ) else: # add next predicted token since it is not eos_token next_beam_scores[batch_idx, beam_idx] = next_score next_beam_tokens[batch_idx, beam_idx] = next_token next_beam_indices[batch_idx, beam_idx] = batch_beam_idx beam_idx += 1 # once the beam for next step is full, don't add more tokens to it. if beam_idx == self.group_size: break if beam_idx < self.group_size: raise ValueError( f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id:" f" {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected." ) # Check if we are done so that we can save a pad step if all(done) self._done[batch_idx] = self._done[batch_idx] or self.hypo_is_done( batch_idx, next_scores[batch_idx].max().item(), cur_len, ) return next_beam_scores.view(-1), next_beam_tokens.view(-1), next_beam_indices.view(-1) def finalize( self, input_ids: torch.Tensor, final_beam_scores: torch.Tensor, final_beam_tokens: torch.Tensor, final_beam_indices: torch.Tensor, pad_token_id: int, eos_token_id: int, ) -> Tuple[torch.Tensor, torch.Tensor]: batch_size = len(self._beam_hyps_count) # finalize all open beam hypotheses and add to generated hypotheses for batch_idx in range(batch_size): if self._done[batch_idx]: continue # all open beam hypotheses are added to the beam hypothesis # beam hypothesis class automatically keeps the best beams for beam_id in range(self.num_beams): batch_beam_idx = batch_idx * self.num_beams + beam_id final_score = final_beam_scores[batch_beam_idx].item() final_tokens = input_ids[batch_beam_idx] self.hypo_add(final_tokens, final_score, batch_idx) # select the best hypotheses # NOTE: torch.Tensor.new_zeros() is not scriptable sent_lengths = torch.zeros(batch_size * self.num_beam_hyps_to_keep, dtype=torch.long) best = [] best_scores = torch.zeros( batch_size * self.num_beam_hyps_to_keep, device=input_ids.device, dtype=torch.float32 ) # retrieve best hypotheses for i in range(batch_size): # NOTE: lambda is not scriptable batch_hypo_start = torch.sum(self._beam_hyps_count[:i]) if i > 0 else torch.tensor(0, dtype=torch.long) batch_hypo_end = torch.sum(self._beam_hyps_count[: i + 1]) beam_scores = torch.cat(self._beam_scores)[batch_hypo_start:batch_hypo_end] sorted_next_scores, sorted_indices = torch.topk(beam_scores, len(beam_scores), largest=True) for j in range(self.num_beam_hyps_to_keep): best_score = beam_scores[sorted_indices[j]] best_hyp = self._beam_hyps[batch_hypo_start + sorted_indices[j]] sent_lengths[self.num_beam_hyps_to_keep * i + j] = len(best_hyp) # append to lists best.append(best_hyp) best_scores[i * self.num_beam_hyps_to_keep + j] = best_score # prepare for adding eos sent_max_len = min(sent_lengths.max() + 1, self.max_length) decoded = torch.zeros(batch_size * self.num_beam_hyps_to_keep, sent_max_len, dtype=torch.long) # shorter batches are padded if needed if sent_lengths.min() != sent_lengths.max(): assert pad_token_id is not None, "`pad_token_id` has to be defined" decoded.fill_(pad_token_id) # fill with hypotheses and eos_token_id if the latter fits in for i, hypo in enumerate(best): decoded[i, : sent_lengths[i]] = hypo if sent_lengths[i] < self.max_length: decoded[i, sent_lengths[i]] = eos_token_id return decoded, best_scores class BARTBeamSearchGenerator(BARTGenerator): def __init__(self, model): super().__init__(model) self.beam_scorer = BeamSearchScorerTS() self.device = model.device @staticmethod def _expand_inputs_for_generation( input_ids: torch.Tensor, attention_mask: torch.Tensor, last_hidden_state: torch.Tensor, expand_size: int = 1, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: expanded_return_idx = ( torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device) ) input_ids = input_ids.index_select(0, expanded_return_idx) attention_mask = attention_mask.index_select(0, expanded_return_idx) last_hidden_state = last_hidden_state.index_select(0, expanded_return_idx.to(last_hidden_state.device)) return input_ids, attention_mask, last_hidden_state def adjust_logits_during_generation(self, logits, cur_len: int, max_length: int): if cur_len == 1 and self.config.force_bos_token_to_be_generated: logits = self._force_token_id_to_be_generated(logits, self.config.bos_token_id) elif cur_len == max_length - 1 and self.config.eos_token_id is not None: logits = self._force_token_id_to_be_generated(logits, self.config.eos_token_id) return logits @staticmethod def _force_token_id_to_be_generated(scores, token_id: int): """force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))""" mask = torch.full_like(scores, 1, dtype=torch.bool) mask[:, token_id] = False return scores.masked_fill(mask, -float("inf")) def _reorder_cache(self, past: List[torch.Tensor], beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder reordered_decoder_past = [] for state in past: reordered_decoder_past.append(state.index_select(0, beam_idx)) return reordered_decoder_past def beam_search( self, input_ids, encoder_output, attention_mask, num_beams, max_length, pad_token_id: int, eos_token_id: int ): batch_size = self.beam_scorer.batch_size num_beams = self.beam_scorer.num_beams batch_beam_size, cur_len = input_ids.shape assert ( num_beams * batch_size == batch_beam_size ), f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device) beam_scores[:, 1:] = -1e9 beam_scores = beam_scores.view((batch_size * num_beams,)) next_tokens = torch.zeros((batch_size, num_beams), dtype=torch.long, device=input_ids.device) next_indices = torch.zeros((batch_size, num_beams), dtype=torch.long, device=input_ids.device) past: List[torch.Tensor] = [] while cur_len < max_length: logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past) next_token_logits = logits[:, -1, :] # adjust tokens for Bart, *e.g.* next_token_logits = self.adjust_logits_during_generation( next_token_logits, cur_len=cur_len, max_length=max_length ) next_token_scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) # pre-process distribution next_token_scores = self.logits_processor(input_ids, next_token_scores) next_token_scores = next_token_scores + beam_scores[:, None].expand_as(next_token_scores) # reshape for beam search vocab_size = next_token_scores.shape[-1] next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size) next_token_scores, next_tokens = torch.topk( next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True ) next_indices = next_tokens // vocab_size next_tokens = next_tokens % vocab_size beam_scores, beam_next_tokens, beam_idx = self.beam_scorer.process( input_ids, next_token_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, ) input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1) cur_len = cur_len + 1 if len(past) > 0: past = self._reorder_cache(past, beam_idx) if self.beam_scorer.is_done(): break sequences, sequence_scores = self.beam_scorer.finalize( input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, ) return sequences def forward(self, input_ids, attention_mask, num_beams, max_length, decoder_start_token_id): pad_token_id = self.config.pad_token_id bos_token_id = self.config.bos_token_id eos_token_id = self.config.eos_token_id # special case if pad_token_id is not defined if pad_token_id is None and eos_token_id is not None: # logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") pad_token_id = eos_token_id encoder_output = self._encoder_forward(input_ids, attention_mask) input_ids = self._prepare_decoder_input_ids_for_generation( input_ids, decoder_start_token_id=decoder_start_token_id, bos_token_id=bos_token_id, ) batch_size = input_ids.shape[0] length_penalty = self.config.length_penalty num_return_sequences = self.config.num_return_sequences early_stopping = True self.beam_scorer.init( batch_size=batch_size, max_length=max_length, num_beams=num_beams, device=self.device, length_penalty=length_penalty, do_early_stopping=early_stopping, num_beam_hyps_to_keep=num_return_sequences, ) input_ids, attention_mask, encoder_output = self._expand_inputs_for_generation( input_ids, attention_mask, encoder_output, expand_size=num_beams, ) return self.beam_search( input_ids=input_ids, encoder_output=encoder_output, attention_mask=attention_mask, num_beams=num_beams, max_length=max_length, pad_token_id=pad_token_id, eos_token_id=eos_token_id, )

import copy import itertools from typing import List, Optional, Tuple import torch import torch.nn.functional as F from transformers import BartConfig from transformers.generation import GenerationMixin def _convert_past_list_to_tuple(past_key_values): """ In Bart model, the type of past_key_values is tuple(tuple(torch.FloatTensor)) which is not TorchScript-compatible. To support this, we have to convert it during the export process. This function will convert past values from a list to tuple(tuple(torch.FloatTensor)) for the inner decoder. According to the definition of past_key_values, each inner tuple(torch.FloatTensor) has 4 tensors, so we convert every 4 elements in the list as a tuple(torch.FloatTensor). """ count_of_each_inner_tuple = 4 results = () temp_result = () count_n = len(past_key_values) // count_of_each_inner_tuple for idx in range(count_n): real_idx = idx * count_of_each_inner_tuple temp_result = tuple(past_key_values[real_idx : real_idx + count_of_each_inner_tuple]) results += ((temp_result),) return results class EncoderForONNX(torch.nn.Module): def __init__(self, encoder): super().__init__() self.encoder = encoder def forward(self, input_ids, attention_mask): return self.encoder( input_ids=input_ids, attention_mask=attention_mask, return_dict=False, ) class DecoderForONNX(torch.nn.Module): def __init__(self, decoder): super().__init__() self.decoder = decoder def forward(self, input_ids, encoder_state, attention_mask, past=None): all_results = None if past is not None: all_results = _convert_past_list_to_tuple(past) input_ids = input_ids[:, -1:] last_hidden_state, past_key_values = self.decoder( input_ids=input_ids, encoder_hidden_states=encoder_state, encoder_attention_mask=attention_mask, past_key_values=all_results, return_dict=False, ) past_values = [] for past in past_key_values: past_values = past_values + list(past) return last_hidden_state, past_values def _create_traced_encoder(encoder, input_ids, attention_mask): encoder_c = copy.deepcopy(encoder) encoder_for_onnx = EncoderForONNX(encoder_c) return torch.jit.trace(encoder_for_onnx, (input_ids, attention_mask)) def _create_traced_decoder(decoder, input_ids, encoder_state, attention_mask, past=None): decoder_c = copy.deepcopy(decoder) decoder_for_onnx = DecoderForONNX(decoder_c) past_values = list(itertools.chain.from_iterable(past or ())) # Do this twice so we got 2 different decoders for further work. if past_values: return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask, past_values)) else: return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask)) class BartConfigTS(BartConfig, torch.nn.Module): """ BartConfigTS is a TorchScript-compatible transformers.models.bart.configuration_bart.BartConfig. TorchScript only supports sub-classes of torch.nn.Module. """ def __init__(self, config): BartConfig.__init__(self, config) torch.nn.Module.__init__(self) class MinLengthLogitsProcessorTS(torch.nn.Module): r""" :class:`transformers.LogitsProcessor` enforcing a min-length by setting EOS probability to 0. Args: min_length (:obj:`int`): The minimum length below which the score of :obj:`eos_token_id` is set to :obj:`-float("Inf")`. eos_token_id (:obj:`int`): The id of the `end-of-sequence` token. """ def __init__(self, min_length: int, eos_token_id: int): super().__init__() if not isinstance(min_length, int) or min_length < 0: raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}") if not isinstance(eos_token_id, int) or eos_token_id < 0: raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}") self.min_length = min_length self.eos_token_id = eos_token_id def forward(self, input_ids, scores) -> torch.Tensor: cur_len = input_ids.shape[-1] if cur_len < self.min_length: scores[:, self.eos_token_id] = -float("inf") return scores class BARTGenerator(torch.nn.Module, GenerationMixin): def __init__(self, model): super().__init__() self.config = BartConfigTS(model.config) self.config.force_bos_token_to_be_generated = False self._trace_modules(model) self.logits_processor = MinLengthLogitsProcessorTS(self.config.min_length, self.config.eos_token_id) self.final_logits_weight = model.model.shared.weight self.final_logits_bias = model.final_logits_bias self.decoder_layers = model.config.decoder_layers def _trace_modules(self, model): input_ids = torch.tensor( [ [ 19, 669, 18, 420, 8, 664, 57, 42, 8, 664, 21, 3028, 195, 4445, 331, 1293, 34, 21, 10, 6174, 1100, 6, 69, 104, 42, 32, 2621, 1638, 144, 4, 6174, 558, 108, 4419, 1091, 28, 4, 1668, 9, 1509, 1621, 279, 35, 867, 2734, 85, 11, 2216, 2734, 85, 203, 2244, 7, 6, 15, 8102, 7, 57, 8629, 5, model.config.eos_token_id, ] ], device=model.device, dtype=torch.long, ) attention_mask = torch.tensor( [[True] * input_ids.shape[-1]], device=model.device, dtype=torch.bool, ) self.encoder = _create_traced_encoder(model.get_encoder(), input_ids, attention_mask) encoder_outputs = model.get_encoder()(input_ids, attention_mask=attention_mask, return_dict=True) decoder = model.model.decoder decoder_outputs = decoder(input_ids, attention_mask, encoder_outputs["last_hidden_state"], None, None, None) self.decoder_no_past = _create_traced_decoder( model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask ) self.decoder_with_past = _create_traced_decoder( model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask, decoder_outputs[1] ) def _encoder_forward(self, input_ids, attention_mask): return self.encoder(input_ids, attention_mask)[0] @staticmethod def _init_sequence_length_for_generation( input_ids: torch.LongTensor, max_length: int ) -> Tuple[torch.Tensor, torch.Tensor, int]: unfinished_sequences = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + 1 sequence_lengths = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + max_length cur_len = input_ids.shape[-1] return sequence_lengths, unfinished_sequences, cur_len def _decoder_forward(self, input_ids, encoder_output, attention_mask, past: List[torch.Tensor]): # Update here to use different decoder for different values of past. if past is None or len(past) == 0: decoder_output, past = self.decoder_no_past( input_ids=input_ids, encoder_state=encoder_output, attention_mask=attention_mask ) else: decoder_output, past = self.decoder_with_past( input_ids=input_ids, encoder_state=encoder_output, attention_mask=attention_mask, past=past ) lm_logits = F.linear(decoder_output, self.final_logits_weight, bias=self.final_logits_bias) return lm_logits, past def greedy_search( self, input_ids, encoder_output, attention_mask, max_length, pad_token_id: int, eos_token_id: int ): # init sequence length tensors sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation( input_ids, max_length ) past: List[torch.Tensor] = [] while cur_len < max_length: logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past) next_token_logits = logits[:, -1, :] # pre-process distribution scores = self.logits_processor(input_ids, next_token_logits) # argmax next_tokens = torch.argmax(scores, dim=-1) # add code that transfomers next_tokens to tokens_to_add if eos_token_id is not None: assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined." next_tokens = next_tokens * unfinished_sequences + (pad_token_id) * (1 - unfinished_sequences) # add token and increase length by one input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) # update sequence length if eos_token_id is not None: sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation( sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id ) # stop when there is a </s> in each sentence, or if we exceed the maximul length if unfinished_sequences.max() == 0: break # increase cur_len cur_len = cur_len + 1 return input_ids def _prepare_decoder_input_ids_for_generation( self, input_ids: torch.LongTensor, decoder_start_token_id, bos_token_id: Optional[int] = None, ) -> torch.LongTensor: decoder_input_ids = ( torch.ones((input_ids.shape[0], 1), dtype=input_ids.dtype, device=input_ids.device) * decoder_start_token_id ) return decoder_input_ids def forward(self, input_ids, attention_mask, max_length, decoder_start_token_id): pad_token_id = self.config.pad_token_id bos_token_id = self.config.bos_token_id eos_token_id = self.config.eos_token_id # special case if pad_token_id is not defined if pad_token_id is None and eos_token_id is not None: # Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation. pad_token_id = eos_token_id encoder_output = self._encoder_forward(input_ids, attention_mask) input_ids = self._prepare_decoder_input_ids_for_generation( input_ids, decoder_start_token_id=decoder_start_token_id, bos_token_id=bos_token_id, ) return self.greedy_search( input_ids, encoder_output, attention_mask, max_length=max_length, pad_token_id=pad_token_id, eos_token_id=eos_token_id, ) # TorchScript compatible BeamSearchScorer class BeamSearchScorerTS(torch.nn.Module): def __init__(self): super().__init__() self.max_length: int = 200 self.num_beams: int = 3 self.batch_size: int = 1 self.length_penalty: float = 1.0 self.do_early_stopping: bool = True self.num_beam_hyps_to_keep: int = 1 self.num_beam_groups: int = 1 self.group_size: int = self.num_beams // self.num_beam_groups self._done = torch.zeros(self.batch_size, dtype=torch.bool) self._beam_hyps_count = torch.zeros(self.batch_size, dtype=torch.long) self._beam_hyps_worst_scores = torch.zeros(self.batch_size) + 1e9 self._beam_hyps_max_length: int = self.max_length - 1 self._beam_hyps: List[torch.Tensor] = [torch.zeros(2)] # placeholder for TorchScript compatibility self._beam_scores: List[torch.Tensor] = [torch.zeros(2)] # placeholder for TorchScript compatibility def is_done(self) -> torch.Tensor: return self._done.all() def init( self, batch_size: int, max_length: int, num_beams: int, device: torch.device, length_penalty: float = 1.0, do_early_stopping: bool = False, num_beam_hyps_to_keep: int = 1, num_beam_groups: int = 1, ): self.max_length = max_length self.num_beams = num_beams self.batch_size = batch_size self.length_penalty = length_penalty self.do_early_stopping = do_early_stopping self.num_beam_hyps_to_keep = num_beam_hyps_to_keep self.num_beam_groups = num_beam_groups self.group_size = self.num_beams // self.num_beam_groups # NOTE: TorchScript does not support List of Modules # Rewritten BeamHypotheses with tensors and list of tensors. self._done = torch.zeros(batch_size, dtype=torch.bool, device=device) self._beam_hyps_count = torch.zeros(batch_size, dtype=torch.long, device=device) self._beam_hyps_worst_scores = torch.zeros(batch_size, device=device) + 1e9 self._beam_hyps = [] self._beam_scores = [] self._beam_hyps_max_length = max_length - 1 # ignoring bos_token if not isinstance(num_beams, int) or num_beams <= 1: raise ValueError( f"`num_beams` has to be an integer strictly greater than 1, but is {num_beams}. For `num_beams` == 1," " one should make use of `greedy_search` instead." ) if not isinstance(num_beam_groups, int) or (num_beam_groups > num_beams) or (num_beams % num_beam_groups != 0): raise ValueError( "`num_beam_groups` has to be an integer smaller or equal than `num_beams` and `num_beams` has to be" f" divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}." ) def hypo_len(self, hypo_idx: int): """ Number of hypotheses in the list. """ return self._beam_hyps_count[hypo_idx] def hypo_add(self, hyp: torch.Tensor, sum_logprobs: float, hypo_idx: int): """ Add a new hypothesis to the list. """ score = sum_logprobs / (hyp.shape[-1] ** self.length_penalty) hyps_count = self.hypo_len(hypo_idx) if hyps_count < self.num_beams or score > self._beam_hyps_worst_scores[hypo_idx]: # NOTE: work around difference of torch.sum(empty_tensor) == 0, while error in onnx. # Bug: https://msdata.visualstudio.com/Vienna/_workitems/edit/1486599 beam_idx = ( torch.sum(self._beam_hyps_count[:hypo_idx]) if hypo_idx != 0 else torch.tensor(0, dtype=torch.long) ) self._beam_scores.insert(beam_idx, torch.tensor([score])) self._beam_hyps.insert(beam_idx, hyp) if hyps_count + 1 > self.num_beams: sorted_next_scores, sorted_indices = torch.topk( torch.cat(self._beam_scores)[beam_idx : beam_idx + hyps_count + 1], hyps_count + 1, largest=False ) del self._beam_hyps[int((sorted_indices[0] + beam_idx))] del self._beam_scores[int((sorted_indices[0] + beam_idx))] self._beam_hyps_worst_scores[hypo_idx] = sorted_next_scores[1] else: self._beam_hyps_worst_scores[hypo_idx] = min(score, self._beam_hyps_worst_scores[hypo_idx]) self._beam_hyps_count[hypo_idx] = hyps_count + 1 def hypo_is_done(self, hypo_idx: int, best_sum_logprobs: float, cur_len: int) -> bool: """ If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst one in the heap, then we are done with this sentence. """ if self.hypo_len(hypo_idx) < self.num_beams: return False elif self.do_early_stopping: return True else: cur_score = best_sum_logprobs / cur_len**self.length_penalty ret = self._beam_hyps_worst_scores[hypo_idx].item() >= cur_score return ret def process( self, input_ids: torch.Tensor, next_scores: torch.Tensor, next_tokens: torch.Tensor, next_indices: torch.Tensor, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: cur_len = input_ids.shape[-1] batch_size = len(self._beam_hyps_count) assert batch_size == (input_ids.shape[0] // self.group_size) device = input_ids.device next_beam_scores = torch.zeros((batch_size, self.group_size), dtype=next_scores.dtype, device=device) next_beam_tokens = torch.zeros((batch_size, self.group_size), dtype=next_tokens.dtype, device=device) next_beam_indices = torch.zeros((batch_size, self.group_size), dtype=next_indices.dtype, device=device) for batch_idx in range(batch_size): if self._done[batch_idx]: assert ( self.hypo_len(batch_idx) >= self.num_beams ), "Batch can only be done if at least {} beams have been generated".format(self.num_beams) assert ( eos_token_id is not None and pad_token_id is not None ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined" # pad the batch next_beam_scores[batch_idx, :] = 0 next_beam_tokens[batch_idx, :] = pad_token_id next_beam_indices[batch_idx, :] = 0 continue # next tokens for this sentence beam_idx = 0 for beam_token_rank, (next_token, next_score, next_index) in enumerate( zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx]) ): batch_beam_idx = batch_idx * self.group_size + next_index # add to generated hypotheses if end of sentence if (eos_token_id is not None) and (next_token == eos_token_id): # if beam_token does not belong to top num_beams tokens, it should not be added is_beam_token_worse_than_top_num_beams = beam_token_rank >= self.group_size if is_beam_token_worse_than_top_num_beams: continue self.hypo_add( input_ids[batch_beam_idx].clone(), next_score.item(), batch_idx, ) else: # add next predicted token since it is not eos_token next_beam_scores[batch_idx, beam_idx] = next_score next_beam_tokens[batch_idx, beam_idx] = next_token next_beam_indices[batch_idx, beam_idx] = batch_beam_idx beam_idx += 1 # once the beam for next step is full, don't add more tokens to it. if beam_idx == self.group_size: break if beam_idx < self.group_size: raise ValueError( f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id:" f" {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected." ) # Check if we are done so that we can save a pad step if all(done) self._done[batch_idx] = self._done[batch_idx] or self.hypo_is_done( batch_idx, next_scores[batch_idx].max().item(), cur_len, ) return next_beam_scores.view(-1), next_beam_tokens.view(-1), next_beam_indices.view(-1) def finalize( self, input_ids: torch.Tensor, final_beam_scores: torch.Tensor, final_beam_tokens: torch.Tensor, final_beam_indices: torch.Tensor, pad_token_id: int, eos_token_id: int, ) -> Tuple[torch.Tensor, torch.Tensor]: batch_size = len(self._beam_hyps_count) # finalize all open beam hypotheses and add to generated hypotheses for batch_idx in range(batch_size): if self._done[batch_idx]: continue # all open beam hypotheses are added to the beam hypothesis # beam hypothesis class automatically keeps the best beams for beam_id in range(self.num_beams): batch_beam_idx = batch_idx * self.num_beams + beam_id final_score = final_beam_scores[batch_beam_idx].item() final_tokens = input_ids[batch_beam_idx] self.hypo_add(final_tokens, final_score, batch_idx) # select the best hypotheses # NOTE: torch.Tensor.new_zeros() is not scriptable sent_lengths = torch.zeros(batch_size * self.num_beam_hyps_to_keep, dtype=torch.long) best = [] best_scores = torch.zeros( batch_size * self.num_beam_hyps_to_keep, device=input_ids.device, dtype=torch.float32 ) # retrieve best hypotheses for i in range(batch_size): # NOTE: lambda is not scriptable batch_hypo_start = torch.sum(self._beam_hyps_count[:i]) if i > 0 else torch.tensor(0, dtype=torch.long) batch_hypo_end = torch.sum(self._beam_hyps_count[: i + 1]) beam_scores = torch.cat(self._beam_scores)[batch_hypo_start:batch_hypo_end] sorted_next_scores, sorted_indices = torch.topk(beam_scores, len(beam_scores), largest=True) for j in range(self.num_beam_hyps_to_keep): best_score = beam_scores[sorted_indices[j]] best_hyp = self._beam_hyps[batch_hypo_start + sorted_indices[j]] sent_lengths[self.num_beam_hyps_to_keep * i + j] = len(best_hyp) # append to lists best.append(best_hyp) best_scores[i * self.num_beam_hyps_to_keep + j] = best_score # prepare for adding eos sent_max_len = min(sent_lengths.max() + 1, self.max_length) decoded = torch.zeros(batch_size * self.num_beam_hyps_to_keep, sent_max_len, dtype=torch.long) # shorter batches are padded if needed if sent_lengths.min() != sent_lengths.max(): assert pad_token_id is not None, "`pad_token_id` has to be defined" decoded.fill_(pad_token_id) # fill with hypotheses and eos_token_id if the latter fits in for i, hypo in enumerate(best): decoded[i, : sent_lengths[i]] = hypo if sent_lengths[i] < self.max_length: decoded[i, sent_lengths[i]] = eos_token_id return decoded, best_scores class BARTBeamSearchGenerator(BARTGenerator): def __init__(self, model): super().__init__(model) self.beam_scorer = BeamSearchScorerTS() self.device = model.device @staticmethod def _expand_inputs_for_generation( input_ids: torch.Tensor, attention_mask: torch.Tensor, last_hidden_state: torch.Tensor, expand_size: int = 1, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: expanded_return_idx = ( torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device) ) input_ids = input_ids.index_select(0, expanded_return_idx) attention_mask = attention_mask.index_select(0, expanded_return_idx) last_hidden_state = last_hidden_state.index_select(0, expanded_return_idx.to(last_hidden_state.device)) return input_ids, attention_mask, last_hidden_state def adjust_logits_during_generation(self, logits, cur_len: int, max_length: int): if cur_len == 1 and self.config.force_bos_token_to_be_generated: logits = self._force_token_id_to_be_generated(logits, self.config.bos_token_id) elif cur_len == max_length - 1 and self.config.eos_token_id is not None: logits = self._force_token_id_to_be_generated(logits, self.config.eos_token_id) return logits @staticmethod def _force_token_id_to_be_generated(scores, token_id: int): """force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))""" mask = torch.full_like(scores, 1, dtype=torch.bool) mask[:, token_id] = False return scores.masked_fill(mask, -float("inf")) def _reorder_cache(self, past: List[torch.Tensor], beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder reordered_decoder_past = [] for state in past: reordered_decoder_past.append(state.index_select(0, beam_idx)) return reordered_decoder_past def beam_search( self, input_ids, encoder_output, attention_mask, num_beams, max_length, pad_token_id: int, eos_token_id: int ): batch_size = self.beam_scorer.batch_size num_beams = self.beam_scorer.num_beams batch_beam_size, cur_len = input_ids.shape assert ( num_beams * batch_size == batch_beam_size ), f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device) beam_scores[:, 1:] = -1e9 beam_scores = beam_scores.view((batch_size * num_beams,)) next_tokens = torch.zeros((batch_size, num_beams), dtype=torch.long, device=input_ids.device) next_indices = torch.zeros((batch_size, num_beams), dtype=torch.long, device=input_ids.device) past: List[torch.Tensor] = [] while cur_len < max_length: logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past) next_token_logits = logits[:, -1, :] # adjust tokens for Bart, *e.g.* next_token_logits = self.adjust_logits_during_generation( next_token_logits, cur_len=cur_len, max_length=max_length ) next_token_scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) # pre-process distribution next_token_scores = self.logits_processor(input_ids, next_token_scores) next_token_scores = next_token_scores + beam_scores[:, None].expand_as(next_token_scores) # reshape for beam search vocab_size = next_token_scores.shape[-1] next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size) next_token_scores, next_tokens = torch.topk( next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True ) next_indices = next_tokens // vocab_size next_tokens = next_tokens % vocab_size beam_scores, beam_next_tokens, beam_idx = self.beam_scorer.process( input_ids, next_token_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, ) input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1) cur_len = cur_len + 1 if len(past) > 0: past = self._reorder_cache(past, beam_idx) if self.beam_scorer.is_done(): break sequences, sequence_scores = self.beam_scorer.finalize( input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, ) return sequences def forward(self, input_ids, attention_mask, num_beams, max_length, decoder_start_token_id): pad_token_id = self.config.pad_token_id bos_token_id = self.config.bos_token_id eos_token_id = self.config.eos_token_id # special case if pad_token_id is not defined if pad_token_id is None and eos_token_id is not None: # logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") pad_token_id = eos_token_id encoder_output = self._encoder_forward(input_ids, attention_mask) input_ids = self._prepare_decoder_input_ids_for_generation( input_ids, decoder_start_token_id=decoder_start_token_id, bos_token_id=bos_token_id, ) batch_size = input_ids.shape[0] length_penalty = self.config.length_penalty num_return_sequences = self.config.num_return_sequences early_stopping = True self.beam_scorer.init( batch_size=batch_size, max_length=max_length, num_beams=num_beams, device=self.device, length_penalty=length_penalty, do_early_stopping=early_stopping, num_beam_hyps_to_keep=num_return_sequences, ) input_ids, attention_mask, encoder_output = self._expand_inputs_for_generation( input_ids, attention_mask, encoder_output, expand_size=num_beams, ) return self.beam_search( input_ids=input_ids, encoder_output=encoder_output, attention_mask=attention_mask, num_beams=num_beams, max_length=max_length, pad_token_id=pad_token_id, eos_token_id=eos_token_id, )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/tensorflow/translation/run_translation.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for translation. """ # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments. import json import logging import os import sys from dataclasses import dataclass, field from typing import Optional import datasets import numpy as np import tensorflow as tf from datasets import load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoTokenizer, DataCollatorForSeq2Seq, HfArgumentParser, KerasMetricCallback, M2M100Tokenizer, MBart50Tokenizer, MBart50TokenizerFast, MBartTokenizer, MBartTokenizerFast, PushToHubCallback, TFAutoModelForSeq2SeqLM, TFTrainingArguments, create_optimizer, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # region Dependencies and constants # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt") logger = logging.getLogger(__name__) MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer] # endregion # region Arguments @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ source_lang: str = field(default=None, metadata={"help": "Source language id for translation."}) target_lang: str = field(default=None, metadata={"help": "Target language id for translation."}) dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field( default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."} ) validation_file: Optional[str] = field( default=None, metadata={ "help": ( "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." ) }, ) test_file: Optional[str] = field( default=None, metadata={ "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_source_length: Optional[int] = field( default=1024, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) max_target_length: Optional[int] = field( default=128, metadata={ "help": ( "The maximum total sequence length for target text after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) val_max_target_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total sequence length for validation target text after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`." "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used " "during ``evaluate`` and ``predict``." ) }, ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to model maximum sentence length. " "If False, will pad the samples dynamically when batching to the maximum length in the batch. More " "efficient on GPU but very bad for TPU." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_predict_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of prediction examples to this " "value if set." ) }, ) num_beams: Optional[int] = field( default=None, metadata={ "help": ( "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " "which is used during ``evaluate`` and ``predict``." ) }, ) ignore_pad_token_for_loss: bool = field( default=True, metadata={ "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not." }, ) source_prefix: Optional[str] = field( default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."} ) forced_bos_token: Optional[str] = field( default=None, metadata={ "help": ( "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for" " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to" " be the target language token.(Usually it is the target language token)" ) }, ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if self.val_max_target_length is None: self.val_max_target_length = self.max_target_length # endregion def main(): # region Argument parsing # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_translation", model_args, data_args, framework="tensorflow") # endregion # region Logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO) datasets.utils.logging.set_verbosity(logging.INFO) transformers.utils.logging.set_verbosity(logging.INFO) # Log on each process the small summary: logger.info(f"Training/evaluation parameters {training_args}") # endregion # region Detecting last checkpoint last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # endregion # Set seed before initializing model. set_seed(training_args.seed) # region Load datasets # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files this script will use the first column for the full texts and the second column for the # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] raw_datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # endregion # region Load model config and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) prefix = data_args.source_prefix if data_args.source_prefix is not None else "" # endregion # region Dataset preprocessing # We need to tokenize inputs and targets. if training_args.do_train: column_names = raw_datasets["train"].column_names elif training_args.do_eval: column_names = raw_datasets["validation"].column_names else: logger.info("There is nothing to do. Please pass `do_train`, and/or `do_eval`.") return column_names = raw_datasets["train"].column_names # For translation we set the codes of our source and target languages (only useful for mBART, the others will # ignore those attributes). if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)): assert data_args.target_lang is not None and data_args.source_lang is not None, ( f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and " "--target_lang arguments." ) tokenizer.src_lang = data_args.source_lang tokenizer.tgt_lang = data_args.target_lang forced_bos_token_id = ( tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None ) # Get the language codes for input/target. source_lang = data_args.source_lang.split("_")[0] target_lang = data_args.target_lang.split("_")[0] padding = "max_length" if data_args.pad_to_max_length else False # Temporarily set max_target_length for training. max_target_length = data_args.max_target_length padding = "max_length" if data_args.pad_to_max_length else False def preprocess_function(examples): inputs = [ex[source_lang] for ex in examples["translation"]] targets = [ex[target_lang] for ex in examples["translation"]] inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True) # Tokenize targets with the `text_target` keyword argument labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True) # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore # padding in the loss. if padding == "max_length" and data_args.ignore_pad_token_for_loss: labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = labels["input_ids"] return model_inputs if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) with training_args.main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) else: train_dataset = None if training_args.do_eval: max_target_length = data_args.val_max_target_length if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) with training_args.main_process_first(desc="validation dataset map pre-processing"): eval_dataset = eval_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on validation dataset", ) else: eval_dataset = None # endregion with training_args.strategy.scope(): # region Prepare model model = TFAutoModelForSeq2SeqLM.from_pretrained( model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)): model.config.forced_bos_token_id = forced_bos_token_id # endregion # region Set decoder_start_token_id if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)): assert ( data_args.target_lang is not None and data_args.source_lang is not None ), "mBart requires --target_lang and --source_lang" if isinstance(tokenizer, MBartTokenizer): model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang] else: model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang) if model.config.decoder_start_token_id is None: raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") # endregion # region Prepare TF Dataset objects label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id data_collator = DataCollatorForSeq2Seq( tokenizer, model=model, label_pad_token_id=label_pad_token_id, pad_to_multiple_of=64, # Reduce the number of unique shapes for XLA, especially for generation return_tensors="tf", ) num_replicas = training_args.strategy.num_replicas_in_sync total_train_batch_size = training_args.per_device_train_batch_size * num_replicas total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas dataset_options = tf.data.Options() dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names # yourself if you use this method, whereas they are automatically inferred from the model input names when # using model.prepare_tf_dataset() # For more info see the docs: # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset tf_train_dataset = model.prepare_tf_dataset( train_dataset, collate_fn=data_collator, batch_size=total_train_batch_size, shuffle=True, ).with_options(dataset_options) tf_eval_dataset = model.prepare_tf_dataset( eval_dataset, collate_fn=data_collator, batch_size=total_eval_batch_size, shuffle=False ).with_options(dataset_options) # endregion # region Optimizer and LR scheduling num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs) if training_args.warmup_steps > 0: num_warmup_steps = training_args.warmup_steps elif training_args.warmup_ratio > 0: num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) else: num_warmup_steps = 0 if training_args.do_train: optimizer, lr_schedule = create_optimizer( init_lr=training_args.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, adam_beta1=training_args.adam_beta1, adam_beta2=training_args.adam_beta2, adam_epsilon=training_args.adam_epsilon, weight_decay_rate=training_args.weight_decay, adam_global_clipnorm=training_args.max_grad_norm, ) else: optimizer = None # endregion # region Metric and postprocessing if training_args.do_eval: metric = evaluate.load("sacrebleu") if data_args.val_max_target_length is None: data_args.val_max_target_length = data_args.max_target_length gen_kwargs = { "max_length": data_args.val_max_target_length, "num_beams": data_args.num_beams, "no_repeat_ngram_size": 0, # Not supported under XLA right now, and some models set it by default } def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [[label.strip()] for label in labels] return preds, labels def compute_metrics(preds): predictions, labels = preds if isinstance(predictions, tuple): predictions = predictions[0] decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) metrics = metric.compute(predictions=decoded_preds, references=decoded_labels) return {"bleu": metrics["score"]} # The KerasMetricCallback allows metrics that are too complex to write as standard Keras metrics # to be computed each epoch. Any Python code can be included in the metric_fn. This is especially # useful for metrics like BLEU and ROUGE that perform string comparisons on decoded model outputs. # For more information, see the docs at # https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.KerasMetricCallback metric_callback = KerasMetricCallback( metric_fn=compute_metrics, eval_dataset=tf_eval_dataset, predict_with_generate=True, use_xla_generation=True, generate_kwargs=gen_kwargs, ) callbacks = [metric_callback] else: callbacks = [] # endregion # region Preparing push_to_hub and model card push_to_hub_model_id = training_args.push_to_hub_model_id model_name = model_args.model_name_or_path.split("/")[-1] if not push_to_hub_model_id: push_to_hub_model_id = f"{model_name}-finetuned-{data_args.source_lang}-{data_args.target_lang}" model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"} if data_args.dataset_name is not None: model_card_kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: model_card_kwargs["dataset_args"] = data_args.dataset_config_name model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: model_card_kwargs["dataset"] = data_args.dataset_name languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None] if len(languages) > 0: model_card_kwargs["language"] = languages if training_args.push_to_hub: # Because this training can be quite long, we save once per epoch. callbacks.append( PushToHubCallback( output_dir=training_args.output_dir, model_id=push_to_hub_model_id, organization=training_args.push_to_hub_organization, token=training_args.push_to_hub_token, tokenizer=tokenizer, **model_card_kwargs, ) ) # endregion # region Training eval_metrics = None model.compile(optimizer=optimizer, jit_compile=training_args.xla) if training_args.do_train: logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {training_args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size = {total_train_batch_size}") logger.info(f" Total optimization steps = {num_train_steps}") if training_args.xla and not data_args.pad_to_max_length: logger.warning( "XLA training may be slow at first when --pad_to_max_length is not set " "until all possible shapes have been compiled." ) history = model.fit(tf_train_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks) eval_metrics = {key: val[-1] for key, val in history.history.items()} # endregion # region Validation if training_args.do_eval and not training_args.do_train: # Compiling generation with XLA yields enormous speedups, see https://huggingface.co/blog/tf-xla-generate @tf.function(jit_compile=True) def generate(**kwargs): return model.generate(**kwargs) if training_args.do_eval: logger.info("Evaluation...") for batch, labels in tf_eval_dataset: batch.update(gen_kwargs) generated_tokens = generate(**batch) if isinstance(generated_tokens, tuple): generated_tokens = generated_tokens[0] decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) metric.add_batch(predictions=decoded_preds, references=decoded_labels) eval_metrics = metric.compute() logger.info({"bleu": eval_metrics["score"]}) # endregion if training_args.output_dir is not None and eval_metrics is not None: output_eval_file = os.path.join(training_args.output_dir, "all_results.json") with open(output_eval_file, "w") as writer: writer.write(json.dumps(eval_metrics)) if training_args.output_dir is not None and not training_args.push_to_hub: # If we're not pushing to hub, at least save a local copy when we're done model.save_pretrained(training_args.output_dir) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for translation. """ # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments. import json import logging import os import sys from dataclasses import dataclass, field from typing import Optional import datasets import numpy as np import tensorflow as tf from datasets import load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoTokenizer, DataCollatorForSeq2Seq, HfArgumentParser, KerasMetricCallback, M2M100Tokenizer, MBart50Tokenizer, MBart50TokenizerFast, MBartTokenizer, MBartTokenizerFast, PushToHubCallback, TFAutoModelForSeq2SeqLM, TFTrainingArguments, create_optimizer, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # region Dependencies and constants # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt") logger = logging.getLogger(__name__) MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer] # endregion # region Arguments @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ source_lang: str = field(default=None, metadata={"help": "Source language id for translation."}) target_lang: str = field(default=None, metadata={"help": "Target language id for translation."}) dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field( default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."} ) validation_file: Optional[str] = field( default=None, metadata={ "help": ( "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." ) }, ) test_file: Optional[str] = field( default=None, metadata={ "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_source_length: Optional[int] = field( default=1024, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) max_target_length: Optional[int] = field( default=128, metadata={ "help": ( "The maximum total sequence length for target text after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) val_max_target_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total sequence length for validation target text after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`." "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used " "during ``evaluate`` and ``predict``." ) }, ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to model maximum sentence length. " "If False, will pad the samples dynamically when batching to the maximum length in the batch. More " "efficient on GPU but very bad for TPU." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_predict_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of prediction examples to this " "value if set." ) }, ) num_beams: Optional[int] = field( default=None, metadata={ "help": ( "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " "which is used during ``evaluate`` and ``predict``." ) }, ) ignore_pad_token_for_loss: bool = field( default=True, metadata={ "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not." }, ) source_prefix: Optional[str] = field( default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."} ) forced_bos_token: Optional[str] = field( default=None, metadata={ "help": ( "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for" " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to" " be the target language token.(Usually it is the target language token)" ) }, ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if self.val_max_target_length is None: self.val_max_target_length = self.max_target_length # endregion def main(): # region Argument parsing # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_translation", model_args, data_args, framework="tensorflow") # endregion # region Logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO) datasets.utils.logging.set_verbosity(logging.INFO) transformers.utils.logging.set_verbosity(logging.INFO) # Log on each process the small summary: logger.info(f"Training/evaluation parameters {training_args}") # endregion # region Detecting last checkpoint last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # endregion # Set seed before initializing model. set_seed(training_args.seed) # region Load datasets # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files this script will use the first column for the full texts and the second column for the # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] raw_datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # endregion # region Load model config and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) prefix = data_args.source_prefix if data_args.source_prefix is not None else "" # endregion # region Dataset preprocessing # We need to tokenize inputs and targets. if training_args.do_train: column_names = raw_datasets["train"].column_names elif training_args.do_eval: column_names = raw_datasets["validation"].column_names else: logger.info("There is nothing to do. Please pass `do_train`, and/or `do_eval`.") return column_names = raw_datasets["train"].column_names # For translation we set the codes of our source and target languages (only useful for mBART, the others will # ignore those attributes). if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)): assert data_args.target_lang is not None and data_args.source_lang is not None, ( f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and " "--target_lang arguments." ) tokenizer.src_lang = data_args.source_lang tokenizer.tgt_lang = data_args.target_lang forced_bos_token_id = ( tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None ) # Get the language codes for input/target. source_lang = data_args.source_lang.split("_")[0] target_lang = data_args.target_lang.split("_")[0] padding = "max_length" if data_args.pad_to_max_length else False # Temporarily set max_target_length for training. max_target_length = data_args.max_target_length padding = "max_length" if data_args.pad_to_max_length else False def preprocess_function(examples): inputs = [ex[source_lang] for ex in examples["translation"]] targets = [ex[target_lang] for ex in examples["translation"]] inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True) # Tokenize targets with the `text_target` keyword argument labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True) # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore # padding in the loss. if padding == "max_length" and data_args.ignore_pad_token_for_loss: labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = labels["input_ids"] return model_inputs if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) with training_args.main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) else: train_dataset = None if training_args.do_eval: max_target_length = data_args.val_max_target_length if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) with training_args.main_process_first(desc="validation dataset map pre-processing"): eval_dataset = eval_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on validation dataset", ) else: eval_dataset = None # endregion with training_args.strategy.scope(): # region Prepare model model = TFAutoModelForSeq2SeqLM.from_pretrained( model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)): model.config.forced_bos_token_id = forced_bos_token_id # endregion # region Set decoder_start_token_id if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)): assert ( data_args.target_lang is not None and data_args.source_lang is not None ), "mBart requires --target_lang and --source_lang" if isinstance(tokenizer, MBartTokenizer): model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang] else: model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang) if model.config.decoder_start_token_id is None: raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") # endregion # region Prepare TF Dataset objects label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id data_collator = DataCollatorForSeq2Seq( tokenizer, model=model, label_pad_token_id=label_pad_token_id, pad_to_multiple_of=64, # Reduce the number of unique shapes for XLA, especially for generation return_tensors="tf", ) num_replicas = training_args.strategy.num_replicas_in_sync total_train_batch_size = training_args.per_device_train_batch_size * num_replicas total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas dataset_options = tf.data.Options() dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names # yourself if you use this method, whereas they are automatically inferred from the model input names when # using model.prepare_tf_dataset() # For more info see the docs: # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset tf_train_dataset = model.prepare_tf_dataset( train_dataset, collate_fn=data_collator, batch_size=total_train_batch_size, shuffle=True, ).with_options(dataset_options) tf_eval_dataset = model.prepare_tf_dataset( eval_dataset, collate_fn=data_collator, batch_size=total_eval_batch_size, shuffle=False ).with_options(dataset_options) # endregion # region Optimizer and LR scheduling num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs) if training_args.warmup_steps > 0: num_warmup_steps = training_args.warmup_steps elif training_args.warmup_ratio > 0: num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) else: num_warmup_steps = 0 if training_args.do_train: optimizer, lr_schedule = create_optimizer( init_lr=training_args.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, adam_beta1=training_args.adam_beta1, adam_beta2=training_args.adam_beta2, adam_epsilon=training_args.adam_epsilon, weight_decay_rate=training_args.weight_decay, adam_global_clipnorm=training_args.max_grad_norm, ) else: optimizer = None # endregion # region Metric and postprocessing if training_args.do_eval: metric = evaluate.load("sacrebleu") if data_args.val_max_target_length is None: data_args.val_max_target_length = data_args.max_target_length gen_kwargs = { "max_length": data_args.val_max_target_length, "num_beams": data_args.num_beams, "no_repeat_ngram_size": 0, # Not supported under XLA right now, and some models set it by default } def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [[label.strip()] for label in labels] return preds, labels def compute_metrics(preds): predictions, labels = preds if isinstance(predictions, tuple): predictions = predictions[0] decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) metrics = metric.compute(predictions=decoded_preds, references=decoded_labels) return {"bleu": metrics["score"]} # The KerasMetricCallback allows metrics that are too complex to write as standard Keras metrics # to be computed each epoch. Any Python code can be included in the metric_fn. This is especially # useful for metrics like BLEU and ROUGE that perform string comparisons on decoded model outputs. # For more information, see the docs at # https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.KerasMetricCallback metric_callback = KerasMetricCallback( metric_fn=compute_metrics, eval_dataset=tf_eval_dataset, predict_with_generate=True, use_xla_generation=True, generate_kwargs=gen_kwargs, ) callbacks = [metric_callback] else: callbacks = [] # endregion # region Preparing push_to_hub and model card push_to_hub_model_id = training_args.push_to_hub_model_id model_name = model_args.model_name_or_path.split("/")[-1] if not push_to_hub_model_id: push_to_hub_model_id = f"{model_name}-finetuned-{data_args.source_lang}-{data_args.target_lang}" model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"} if data_args.dataset_name is not None: model_card_kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: model_card_kwargs["dataset_args"] = data_args.dataset_config_name model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: model_card_kwargs["dataset"] = data_args.dataset_name languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None] if len(languages) > 0: model_card_kwargs["language"] = languages if training_args.push_to_hub: # Because this training can be quite long, we save once per epoch. callbacks.append( PushToHubCallback( output_dir=training_args.output_dir, model_id=push_to_hub_model_id, organization=training_args.push_to_hub_organization, token=training_args.push_to_hub_token, tokenizer=tokenizer, **model_card_kwargs, ) ) # endregion # region Training eval_metrics = None model.compile(optimizer=optimizer, jit_compile=training_args.xla) if training_args.do_train: logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {training_args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size = {total_train_batch_size}") logger.info(f" Total optimization steps = {num_train_steps}") if training_args.xla and not data_args.pad_to_max_length: logger.warning( "XLA training may be slow at first when --pad_to_max_length is not set " "until all possible shapes have been compiled." ) history = model.fit(tf_train_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks) eval_metrics = {key: val[-1] for key, val in history.history.items()} # endregion # region Validation if training_args.do_eval and not training_args.do_train: # Compiling generation with XLA yields enormous speedups, see https://huggingface.co/blog/tf-xla-generate @tf.function(jit_compile=True) def generate(**kwargs): return model.generate(**kwargs) if training_args.do_eval: logger.info("Evaluation...") for batch, labels in tf_eval_dataset: batch.update(gen_kwargs) generated_tokens = generate(**batch) if isinstance(generated_tokens, tuple): generated_tokens = generated_tokens[0] decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) metric.add_batch(predictions=decoded_preds, references=decoded_labels) eval_metrics = metric.compute() logger.info({"bleu": eval_metrics["score"]}) # endregion if training_args.output_dir is not None and eval_metrics is not None: output_eval_file = os.path.join(training_args.output_dir, "all_results.json") with open(output_eval_file, "w") as writer: writer.write(json.dumps(eval_metrics)) if training_args.output_dir is not None and not training_args.push_to_hub: # If we're not pushing to hub, at least save a local copy when we're done model.save_pretrained(training_args.output_dir) if __name__ == "__main__": main()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition""" import functools import json import logging import os import re import sys import warnings from dataclasses import dataclass, field from typing import Dict, List, Optional, Union import datasets import numpy as np import torch from datasets import DatasetDict, load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoProcessor, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2Processor, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt") logger = logging.getLogger(__name__) def list_field(default=None, metadata=None): return field(default_factory=lambda: default, metadata=metadata) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) tokenizer_name_or_path: Optional[str] = field( default=None, metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"}, ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."} ) activation_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} ) feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."}) hidden_dropout: float = field( default=0.0, metadata={ "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler." }, ) final_dropout: float = field( default=0.0, metadata={"help": "The dropout probability for the final projection layer."}, ) mask_time_prob: float = field( default=0.05, metadata={ "help": ( "Probability of each feature vector along the time axis to be chosen as the start of the vector" "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature" "vectors will be masked along the time axis." ) }, ) mask_time_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the time axis."}, ) mask_feature_prob: float = field( default=0.0, metadata={ "help": ( "Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan" " to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature" " bins will be masked along the time axis." ) }, ) mask_feature_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the feature axis."}, ) layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."}) ctc_loss_reduction: Optional[str] = field( default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: str = field( metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) dataset_config_name: str = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_split_name: str = field( default="train+validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to " "'train+validation'" ) }, ) eval_split_name: str = field( default="test", metadata={ "help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'" }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of validation examples to this " "value if set." ) }, ) chars_to_ignore: Optional[List[str]] = list_field( default=None, metadata={"help": "A list of characters to remove from the transcripts."}, ) eval_metrics: List[str] = list_field( default=["wer"], metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"}, ) max_duration_in_seconds: float = field( default=20.0, metadata={ "help": ( "Filter audio files that are longer than `max_duration_in_seconds` seconds to" " 'max_duration_in_seconds`" ) }, ) min_duration_in_seconds: float = field( default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"} ) preprocessing_only: bool = field( default=False, metadata={ "help": ( "Whether to only do data preprocessing and skip training. This is especially useful when data" " preprocessing errors out in distributed training due to timeout. In this case, one should run the" " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" " can consequently be loaded in distributed training" ) }, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "If :obj:`True`, will use the token generated when running" ":obj:`huggingface-cli login` as HTTP bearer authorization for remote files." ) }, ) unk_token: str = field( default="[UNK]", metadata={"help": "The unk token for the tokenizer"}, ) pad_token: str = field( default="[PAD]", metadata={"help": "The padding token for the tokenizer"}, ) word_delimiter_token: str = field( default="|", metadata={"help": "The word delimiter token for the tokenizer"}, ) phoneme_language: Optional[str] = field( default=None, metadata={ "help": ( "The target language that should be used be" " passed to the tokenizer for tokenization. Note that" " this is only relevant if the model classifies the" " input audio to a sequence of phoneme sequences." ) }, ) @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.AutoProcessor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). max_length_labels (:obj:`int`, `optional`): Maximum length of the ``labels`` returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ processor: AutoProcessor padding: Union[bool, str] = "longest" pad_to_multiple_of: Optional[int] = None pad_to_multiple_of_labels: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lenghts and need # different padding methods input_features = [{"input_values": feature["input_values"]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.pad( input_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) labels_batch = self.processor.pad( labels=label_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of_labels, return_tensors="pt", ) # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels if "attention_mask" in batch: batch["attention_mask"] = batch["attention_mask"].to(torch.long) return batch def create_vocabulary_from_data( datasets: DatasetDict, word_delimiter_token: Optional[str] = None, unk_token: Optional[str] = None, pad_token: Optional[str] = None, ): # Given training and test labels create vocabulary def extract_all_chars(batch): all_text = " ".join(batch["target_text"]) vocab = list(set(all_text)) return {"vocab": [vocab], "all_text": [all_text]} vocabs = datasets.map( extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=datasets["train"].column_names, ) # take union of all unique characters in each dataset vocab_set = functools.reduce( lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values() ) vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))} # replace white space with delimiter token if word_delimiter_token is not None: vocab_dict[word_delimiter_token] = vocab_dict[" "] del vocab_dict[" "] # add unk and pad token if unk_token is not None: vocab_dict[unk_token] = len(vocab_dict) if pad_token is not None: vocab_dict[pad_token] = len(vocab_dict) return vocab_dict def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_speech_recognition_ctc", model_args, data_args) # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # 1. First, let's load the dataset raw_datasets = DatasetDict() if training_args.do_train: raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=data_args.use_auth_token, ) if data_args.audio_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'." " Make sure to set `--audio_column_name` to the correct audio column - one of" f" {', '.join(raw_datasets['train'].column_names)}." ) if data_args.text_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--text_column_name` to the correct text column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) if data_args.max_train_samples is not None: raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples)) if training_args.do_eval: raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=data_args.use_auth_token, ) if data_args.max_eval_samples is not None: raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples)) # 2. We remove some special characters from the datasets # that make training complicated and do not help in transcribing the speech # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic # that could be easily picked up by the model chars_to_ignore_regex = ( f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None ) text_column_name = data_args.text_column_name def remove_special_characters(batch): if chars_to_ignore_regex is not None: batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " " else: batch["target_text"] = batch[text_column_name].lower() + " " return batch with training_args.main_process_first(desc="dataset map special characters removal"): raw_datasets = raw_datasets.map( remove_special_characters, remove_columns=[text_column_name], desc="remove special characters from datasets", ) # save special tokens for tokenizer word_delimiter_token = data_args.word_delimiter_token unk_token = data_args.unk_token pad_token = data_args.pad_token # 3. Next, let's load the config as we might need it to create # the tokenizer # load config config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) # 4. Next, if no tokenizer file is defined, # we create the vocabulary of the model by extracting all unique characters from # the training and evaluation datasets # We need to make sure that only first rank saves vocabulary # make sure all processes wait until vocab is created tokenizer_name_or_path = model_args.tokenizer_name_or_path tokenizer_kwargs = {} if tokenizer_name_or_path is None: # save vocab in training output dir tokenizer_name_or_path = training_args.output_dir vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json") with training_args.main_process_first(): if training_args.overwrite_output_dir and os.path.isfile(vocab_file): try: os.remove(vocab_file) except OSError: # in shared file-systems it might be the case that # two processes try to delete the vocab file at the some time pass with training_args.main_process_first(desc="dataset map vocabulary creation"): if not os.path.isfile(vocab_file): os.makedirs(tokenizer_name_or_path, exist_ok=True) vocab_dict = create_vocabulary_from_data( raw_datasets, word_delimiter_token=word_delimiter_token, unk_token=unk_token, pad_token=pad_token, ) # save vocab dict to be loaded into tokenizer with open(vocab_file, "w") as file: json.dump(vocab_dict, file) # if tokenizer has just been created # it is defined by `tokenizer_class` if present in config else by `model_type` tokenizer_kwargs = { "config": config if config.tokenizer_class is not None else None, "tokenizer_type": config.model_type if config.tokenizer_class is None else None, "unk_token": unk_token, "pad_token": pad_token, "word_delimiter_token": word_delimiter_token, } # 5. Now we can instantiate the feature extractor, tokenizer and model # Note for distributed training, the .from_pretrained methods guarantee that only # one local process can concurrently download model & vocab. # load feature_extractor and tokenizer tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path, use_auth_token=data_args.use_auth_token, **tokenizer_kwargs, ) feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) # adapt config config.update( { "feat_proj_dropout": model_args.feat_proj_dropout, "attention_dropout": model_args.attention_dropout, "hidden_dropout": model_args.hidden_dropout, "final_dropout": model_args.final_dropout, "mask_time_prob": model_args.mask_time_prob, "mask_time_length": model_args.mask_time_length, "mask_feature_prob": model_args.mask_feature_prob, "mask_feature_length": model_args.mask_feature_length, "gradient_checkpointing": training_args.gradient_checkpointing, "layerdrop": model_args.layerdrop, "ctc_loss_reduction": model_args.ctc_loss_reduction, "pad_token_id": tokenizer.pad_token_id, "vocab_size": len(tokenizer), "activation_dropout": model_args.activation_dropout, } ) # create model model = AutoModelForCTC.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config, use_auth_token=data_args.use_auth_token, ) # freeze encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() # 6. Now we preprocess the datasets including loading the audio, resampling and normalization # Thankfully, `datasets` takes care of automatically loading and resampling the audio, # so that we just need to set the correct target sampling rate and normalize the input # via the `feature_extractor` # make sure that dataset decodes audio with correct sampling rate dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate if dataset_sampling_rate != feature_extractor.sampling_rate: raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) # derive max & min input length for sample rate & max duration max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate audio_column_name = data_args.audio_column_name num_workers = data_args.preprocessing_num_workers # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification phoneme_language = data_args.phoneme_language # Preprocessing the datasets. # We need to read the audio files as arrays and tokenize the targets. def prepare_dataset(batch): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch with training_args.main_process_first(desc="dataset map preprocessing"): vectorized_datasets = raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=num_workers, desc="preprocess datasets", ) def is_audio_in_length_range(length): return length > min_input_length and length < max_input_length # filter data that is shorter than min_input_length vectorized_datasets = vectorized_datasets.filter( is_audio_in_length_range, num_proc=num_workers, input_columns=["input_length"], ) # 7. Next, we can prepare the training. # Let's use word error rate (WER) as our evaluation metric, # instantiate a data collator and the trainer # Define evaluation metrics during training, *i.e.* word error rate, character error rate eval_metrics = {metric: evaluate.load(metric) for metric in data_args.eval_metrics} # for large datasets it is advised to run the preprocessing on a # single machine first with ``args.preprocessing_only`` since there will mostly likely # be a timeout when running the script in distributed mode. # In a second step ``args.preprocessing_only`` can then be set to `False` to load the # cached dataset if data_args.preprocessing_only: logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}") return def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id pred_str = tokenizer.batch_decode(pred_ids) # we do not want to group tokens when computing the metrics label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False) metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} return metrics # Now save everything to be able to create a single processor later if is_main_process(training_args.local_rank): # save feature extractor, tokenizer and config feature_extractor.save_pretrained(training_args.output_dir) tokenizer.save_pretrained(training_args.output_dir) config.save_pretrained(training_args.output_dir) try: processor = AutoProcessor.from_pretrained(training_args.output_dir) except (OSError, KeyError): warnings.warn( "Loading a processor from a feature extractor config that does not" " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following " " attribute to your `preprocessor_config.json` file to suppress this warning: " " `'processor_class': 'Wav2Vec2Processor'`", FutureWarning, ) processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir) # Instantiate custom data collator data_collator = DataCollatorCTCWithPadding(processor=processor) # Initialize Trainer trainer = Trainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=vectorized_datasets["train"] if training_args.do_train else None, eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, tokenizer=feature_extractor, ) # 8. Finally, we can start training # Training if training_args.do_train: # use last checkpoint if exist if last_checkpoint is not None: checkpoint = last_checkpoint elif os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(vectorized_datasets["train"]) ) metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"])) trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() max_eval_samples = ( data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"]) ) metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"])) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na" kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition", "tags": ["automatic-speech-recognition", data_args.dataset_name], "dataset_args": ( f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:" f" {data_args.eval_split_name}" ), "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}", } if "common_voice" in data_args.dataset_name: kwargs["language"] = config_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition""" import functools import json import logging import os import re import sys import warnings from dataclasses import dataclass, field from typing import Dict, List, Optional, Union import datasets import numpy as np import torch from datasets import DatasetDict, load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoProcessor, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2Processor, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt") logger = logging.getLogger(__name__) def list_field(default=None, metadata=None): return field(default_factory=lambda: default, metadata=metadata) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) tokenizer_name_or_path: Optional[str] = field( default=None, metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"}, ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."} ) activation_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} ) feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."}) hidden_dropout: float = field( default=0.0, metadata={ "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler." }, ) final_dropout: float = field( default=0.0, metadata={"help": "The dropout probability for the final projection layer."}, ) mask_time_prob: float = field( default=0.05, metadata={ "help": ( "Probability of each feature vector along the time axis to be chosen as the start of the vector" "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature" "vectors will be masked along the time axis." ) }, ) mask_time_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the time axis."}, ) mask_feature_prob: float = field( default=0.0, metadata={ "help": ( "Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan" " to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature" " bins will be masked along the time axis." ) }, ) mask_feature_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the feature axis."}, ) layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."}) ctc_loss_reduction: Optional[str] = field( default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: str = field( metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) dataset_config_name: str = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_split_name: str = field( default="train+validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to " "'train+validation'" ) }, ) eval_split_name: str = field( default="test", metadata={ "help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'" }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of validation examples to this " "value if set." ) }, ) chars_to_ignore: Optional[List[str]] = list_field( default=None, metadata={"help": "A list of characters to remove from the transcripts."}, ) eval_metrics: List[str] = list_field( default=["wer"], metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"}, ) max_duration_in_seconds: float = field( default=20.0, metadata={ "help": ( "Filter audio files that are longer than `max_duration_in_seconds` seconds to" " 'max_duration_in_seconds`" ) }, ) min_duration_in_seconds: float = field( default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"} ) preprocessing_only: bool = field( default=False, metadata={ "help": ( "Whether to only do data preprocessing and skip training. This is especially useful when data" " preprocessing errors out in distributed training due to timeout. In this case, one should run the" " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" " can consequently be loaded in distributed training" ) }, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "If :obj:`True`, will use the token generated when running" ":obj:`huggingface-cli login` as HTTP bearer authorization for remote files." ) }, ) unk_token: str = field( default="[UNK]", metadata={"help": "The unk token for the tokenizer"}, ) pad_token: str = field( default="[PAD]", metadata={"help": "The padding token for the tokenizer"}, ) word_delimiter_token: str = field( default="|", metadata={"help": "The word delimiter token for the tokenizer"}, ) phoneme_language: Optional[str] = field( default=None, metadata={ "help": ( "The target language that should be used be" " passed to the tokenizer for tokenization. Note that" " this is only relevant if the model classifies the" " input audio to a sequence of phoneme sequences." ) }, ) @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.AutoProcessor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). max_length_labels (:obj:`int`, `optional`): Maximum length of the ``labels`` returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ processor: AutoProcessor padding: Union[bool, str] = "longest" pad_to_multiple_of: Optional[int] = None pad_to_multiple_of_labels: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lenghts and need # different padding methods input_features = [{"input_values": feature["input_values"]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.pad( input_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) labels_batch = self.processor.pad( labels=label_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of_labels, return_tensors="pt", ) # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels if "attention_mask" in batch: batch["attention_mask"] = batch["attention_mask"].to(torch.long) return batch def create_vocabulary_from_data( datasets: DatasetDict, word_delimiter_token: Optional[str] = None, unk_token: Optional[str] = None, pad_token: Optional[str] = None, ): # Given training and test labels create vocabulary def extract_all_chars(batch): all_text = " ".join(batch["target_text"]) vocab = list(set(all_text)) return {"vocab": [vocab], "all_text": [all_text]} vocabs = datasets.map( extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=datasets["train"].column_names, ) # take union of all unique characters in each dataset vocab_set = functools.reduce( lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values() ) vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))} # replace white space with delimiter token if word_delimiter_token is not None: vocab_dict[word_delimiter_token] = vocab_dict[" "] del vocab_dict[" "] # add unk and pad token if unk_token is not None: vocab_dict[unk_token] = len(vocab_dict) if pad_token is not None: vocab_dict[pad_token] = len(vocab_dict) return vocab_dict def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_speech_recognition_ctc", model_args, data_args) # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # 1. First, let's load the dataset raw_datasets = DatasetDict() if training_args.do_train: raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=data_args.use_auth_token, ) if data_args.audio_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'." " Make sure to set `--audio_column_name` to the correct audio column - one of" f" {', '.join(raw_datasets['train'].column_names)}." ) if data_args.text_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--text_column_name` to the correct text column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) if data_args.max_train_samples is not None: raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples)) if training_args.do_eval: raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=data_args.use_auth_token, ) if data_args.max_eval_samples is not None: raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples)) # 2. We remove some special characters from the datasets # that make training complicated and do not help in transcribing the speech # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic # that could be easily picked up by the model chars_to_ignore_regex = ( f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None ) text_column_name = data_args.text_column_name def remove_special_characters(batch): if chars_to_ignore_regex is not None: batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " " else: batch["target_text"] = batch[text_column_name].lower() + " " return batch with training_args.main_process_first(desc="dataset map special characters removal"): raw_datasets = raw_datasets.map( remove_special_characters, remove_columns=[text_column_name], desc="remove special characters from datasets", ) # save special tokens for tokenizer word_delimiter_token = data_args.word_delimiter_token unk_token = data_args.unk_token pad_token = data_args.pad_token # 3. Next, let's load the config as we might need it to create # the tokenizer # load config config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) # 4. Next, if no tokenizer file is defined, # we create the vocabulary of the model by extracting all unique characters from # the training and evaluation datasets # We need to make sure that only first rank saves vocabulary # make sure all processes wait until vocab is created tokenizer_name_or_path = model_args.tokenizer_name_or_path tokenizer_kwargs = {} if tokenizer_name_or_path is None: # save vocab in training output dir tokenizer_name_or_path = training_args.output_dir vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json") with training_args.main_process_first(): if training_args.overwrite_output_dir and os.path.isfile(vocab_file): try: os.remove(vocab_file) except OSError: # in shared file-systems it might be the case that # two processes try to delete the vocab file at the some time pass with training_args.main_process_first(desc="dataset map vocabulary creation"): if not os.path.isfile(vocab_file): os.makedirs(tokenizer_name_or_path, exist_ok=True) vocab_dict = create_vocabulary_from_data( raw_datasets, word_delimiter_token=word_delimiter_token, unk_token=unk_token, pad_token=pad_token, ) # save vocab dict to be loaded into tokenizer with open(vocab_file, "w") as file: json.dump(vocab_dict, file) # if tokenizer has just been created # it is defined by `tokenizer_class` if present in config else by `model_type` tokenizer_kwargs = { "config": config if config.tokenizer_class is not None else None, "tokenizer_type": config.model_type if config.tokenizer_class is None else None, "unk_token": unk_token, "pad_token": pad_token, "word_delimiter_token": word_delimiter_token, } # 5. Now we can instantiate the feature extractor, tokenizer and model # Note for distributed training, the .from_pretrained methods guarantee that only # one local process can concurrently download model & vocab. # load feature_extractor and tokenizer tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path, use_auth_token=data_args.use_auth_token, **tokenizer_kwargs, ) feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) # adapt config config.update( { "feat_proj_dropout": model_args.feat_proj_dropout, "attention_dropout": model_args.attention_dropout, "hidden_dropout": model_args.hidden_dropout, "final_dropout": model_args.final_dropout, "mask_time_prob": model_args.mask_time_prob, "mask_time_length": model_args.mask_time_length, "mask_feature_prob": model_args.mask_feature_prob, "mask_feature_length": model_args.mask_feature_length, "gradient_checkpointing": training_args.gradient_checkpointing, "layerdrop": model_args.layerdrop, "ctc_loss_reduction": model_args.ctc_loss_reduction, "pad_token_id": tokenizer.pad_token_id, "vocab_size": len(tokenizer), "activation_dropout": model_args.activation_dropout, } ) # create model model = AutoModelForCTC.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config, use_auth_token=data_args.use_auth_token, ) # freeze encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() # 6. Now we preprocess the datasets including loading the audio, resampling and normalization # Thankfully, `datasets` takes care of automatically loading and resampling the audio, # so that we just need to set the correct target sampling rate and normalize the input # via the `feature_extractor` # make sure that dataset decodes audio with correct sampling rate dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate if dataset_sampling_rate != feature_extractor.sampling_rate: raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) # derive max & min input length for sample rate & max duration max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate audio_column_name = data_args.audio_column_name num_workers = data_args.preprocessing_num_workers # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification phoneme_language = data_args.phoneme_language # Preprocessing the datasets. # We need to read the audio files as arrays and tokenize the targets. def prepare_dataset(batch): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch with training_args.main_process_first(desc="dataset map preprocessing"): vectorized_datasets = raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=num_workers, desc="preprocess datasets", ) def is_audio_in_length_range(length): return length > min_input_length and length < max_input_length # filter data that is shorter than min_input_length vectorized_datasets = vectorized_datasets.filter( is_audio_in_length_range, num_proc=num_workers, input_columns=["input_length"], ) # 7. Next, we can prepare the training. # Let's use word error rate (WER) as our evaluation metric, # instantiate a data collator and the trainer # Define evaluation metrics during training, *i.e.* word error rate, character error rate eval_metrics = {metric: evaluate.load(metric) for metric in data_args.eval_metrics} # for large datasets it is advised to run the preprocessing on a # single machine first with ``args.preprocessing_only`` since there will mostly likely # be a timeout when running the script in distributed mode. # In a second step ``args.preprocessing_only`` can then be set to `False` to load the # cached dataset if data_args.preprocessing_only: logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}") return def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id pred_str = tokenizer.batch_decode(pred_ids) # we do not want to group tokens when computing the metrics label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False) metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} return metrics # Now save everything to be able to create a single processor later if is_main_process(training_args.local_rank): # save feature extractor, tokenizer and config feature_extractor.save_pretrained(training_args.output_dir) tokenizer.save_pretrained(training_args.output_dir) config.save_pretrained(training_args.output_dir) try: processor = AutoProcessor.from_pretrained(training_args.output_dir) except (OSError, KeyError): warnings.warn( "Loading a processor from a feature extractor config that does not" " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following " " attribute to your `preprocessor_config.json` file to suppress this warning: " " `'processor_class': 'Wav2Vec2Processor'`", FutureWarning, ) processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir) # Instantiate custom data collator data_collator = DataCollatorCTCWithPadding(processor=processor) # Initialize Trainer trainer = Trainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=vectorized_datasets["train"] if training_args.do_train else None, eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, tokenizer=feature_extractor, ) # 8. Finally, we can start training # Training if training_args.do_train: # use last checkpoint if exist if last_checkpoint is not None: checkpoint = last_checkpoint elif os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(vectorized_datasets["train"]) ) metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"])) trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() max_eval_samples = ( data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"]) ) metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"])) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na" kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition", "tags": ["automatic-speech-recognition", data_args.dataset_name], "dataset_args": ( f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:" f" {data_args.eval_split_name}" ), "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}", } if "common_voice" in data_args.dataset_name: kwargs["language"] = config_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/legacy/seq2seq/seq2seq_training_args.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from dataclasses import dataclass, field from typing import Optional from seq2seq_trainer import arg_to_scheduler from transformers import TrainingArguments logger = logging.getLogger(__name__) @dataclass class Seq2SeqTrainingArguments(TrainingArguments): """ Parameters: label_smoothing (:obj:`float`, `optional`, defaults to 0): The label smoothing epsilon to apply (if not zero). sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to SortishSamler or not. It sorts the inputs according to lenghts in-order to minimizing the padding size. predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to use generate to calculate generative metrics (ROUGE, BLEU). """ label_smoothing: Optional[float] = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (if not zero)."} ) sortish_sampler: bool = field(default=False, metadata={"help": "Whether to SortishSamler or not."}) predict_with_generate: bool = field( default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} ) adafactor: bool = field(default=False, metadata={"help": "whether to use adafactor"}) encoder_layerdrop: Optional[float] = field( default=None, metadata={"help": "Encoder layer dropout probability. Goes into model.config."} ) decoder_layerdrop: Optional[float] = field( default=None, metadata={"help": "Decoder layer dropout probability. Goes into model.config."} ) dropout: Optional[float] = field(default=None, metadata={"help": "Dropout probability. Goes into model.config."}) attention_dropout: Optional[float] = field( default=None, metadata={"help": "Attention dropout probability. Goes into model.config."} ) lr_scheduler: Optional[str] = field( default="linear", metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys())}"}, )

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from dataclasses import dataclass, field from typing import Optional from seq2seq_trainer import arg_to_scheduler from transformers import TrainingArguments logger = logging.getLogger(__name__) @dataclass class Seq2SeqTrainingArguments(TrainingArguments): """ Parameters: label_smoothing (:obj:`float`, `optional`, defaults to 0): The label smoothing epsilon to apply (if not zero). sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to SortishSamler or not. It sorts the inputs according to lenghts in-order to minimizing the padding size. predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to use generate to calculate generative metrics (ROUGE, BLEU). """ label_smoothing: Optional[float] = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (if not zero)."} ) sortish_sampler: bool = field(default=False, metadata={"help": "Whether to SortishSamler or not."}) predict_with_generate: bool = field( default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} ) adafactor: bool = field(default=False, metadata={"help": "whether to use adafactor"}) encoder_layerdrop: Optional[float] = field( default=None, metadata={"help": "Encoder layer dropout probability. Goes into model.config."} ) decoder_layerdrop: Optional[float] = field( default=None, metadata={"help": "Decoder layer dropout probability. Goes into model.config."} ) dropout: Optional[float] = field(default=None, metadata={"help": "Dropout probability. Goes into model.config."}) attention_dropout: Optional[float] = field( default=None, metadata={"help": "Attention dropout probability. Goes into model.config."} ) lr_scheduler: Optional[str] = field( default="linear", metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys())}"}, )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/trajectory_transformer/test_modeling_trajectory_transformer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch TrajectoryTransformer model. """ import inspect import unittest import numpy as np from transformers import TrajectoryTransformerConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, random_attention_mask if is_torch_available(): import torch from transformers import TrajectoryTransformerModel from transformers.models.trajectory_transformer.modeling_trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ) class TrajectoryTransformerModelTester: def __init__(self, parent, batch_size=13, n_embd=128, action_dim=6, observation_dim=17, is_training=True): self.parent = parent self.batch_size = batch_size self.n_embd = n_embd self.action_dim = action_dim self.observation_dim = observation_dim self.is_training = is_training self.seq_length = self.action_dim + self.observation_dim + 1 def prepare_config_and_inputs(self): trajectories = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(self.batch_size)]).to( torch_device ) attention_mask = random_attention_mask((self.batch_size, self.seq_length)).to(torch_device) targets = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(self.batch_size)]).to( torch_device ) config = self.get_config() return config, trajectories, attention_mask, targets def get_config(self): return TrajectoryTransformerConfig( batch_size=self.batch_size, n_embd=self.n_embd, action_dim=self.action_dim, observation_dim=self.observation_dim, ) def create_and_check_model(self, config, input_dict): model = TrajectoryTransformerModel(config=config) model.to(torch_device) model.eval() result = model(trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"]) result = model( trajectories=input_dict["trajectories"], output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ) self.parent.assertEqual(result.hidden_states[-1].shape, (self.batch_size, self.seq_length, self.n_embd)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, trajectories, attention_mask, targets) = config_and_inputs inputs_dict = {"trajectories": trajectories, "attention_mask": attention_mask, "targets": targets} return config, inputs_dict @require_torch class TrajectoryTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (TrajectoryTransformerModel,) if is_torch_available() else () # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids test_generate_without_input_ids = False # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features test_pruning = False test_resize_embeddings = False test_head_masking = False test_attention_outputs = False test_hidden_states_output = False test_inputs_embeds = False test_model_common_attributes = False test_torchscript = False def setUp(self): self.model_tester = TrajectoryTransformerModelTester(self) self.config_tester = ConfigTester(self, config_class=TrajectoryTransformerConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_model(*config_and_inputs) def test_conditional_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_model(*config_and_inputs) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["trajectories"] self.assertListEqual(arg_names[:1], expected_arg_names) # # Input is 'trajectories' not 'input_ids' def test_model_main_input_name(self): model_signature = inspect.signature(getattr(TrajectoryTransformerModel, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(TrajectoryTransformerModel.main_input_name, observed_main_input_name) def test_retain_grad_hidden_states_attentions(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions model = TrajectoryTransformerModel(config) model.to(torch_device) outputs = model( trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"], targets=input_dict["targets"], output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ) output = outputs[0] hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) def test_training(self): if not self.model_tester.is_training: return config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = TrajectoryTransformerModel(config) model.to(torch_device) model.train() loss = model( trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"], targets=input_dict["targets"], output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = TrajectoryTransformerModel(config) model.gradient_checkpointing_enable() model.to(torch_device) model.train() loss = model( trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"], targets=input_dict["targets"], output_hidden_states=True, output_attentions=True, use_cache=False, return_dict=True, ).loss loss.backward() def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) @slow def test_model_from_pretrained(self): for model_name in TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TrajectoryTransformerModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class TrajectoryTransformerModelIntegrationTest(unittest.TestCase): @slow def test_prediction(self): batch_size = 1 config = TrajectoryTransformerConfig.from_pretrained("CarlCochet/trajectory-transformer-halfcheetah-medium-v2") model = TrajectoryTransformerModel.from_pretrained( "CarlCochet/trajectory-transformer-halfcheetah-medium-v2", config=config ) model.to(torch_device) model.eval() seq_length = model.config.action_dim + model.config.observation_dim + 1 trajectories = torch.LongTensor( [[3, 19, 20, 22, 9, 7, 23, 10, 18, 14, 13, 4, 17, 11, 5, 6, 15, 21, 2, 8, 1, 0, 12, 16]] ).to(torch_device) outputs = model( trajectories=trajectories, output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ) output = outputs.logits expected_shape = torch.Size((batch_size, seq_length, model.config.vocab_size + 1)) expected_slice = torch.tensor( [[[-0.7193, -0.2532, -0.0898], [1.9429, 2.0434, 2.3975], [-3.3651, -2.8744, -2.4532]]] ).to(torch_device) output_slice = output[:, :3, :3] self.assertEqual(output.shape, expected_shape) self.assertTrue(torch.allclose(output_slice, expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch TrajectoryTransformer model. """ import inspect import unittest import numpy as np from transformers import TrajectoryTransformerConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, random_attention_mask if is_torch_available(): import torch from transformers import TrajectoryTransformerModel from transformers.models.trajectory_transformer.modeling_trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ) class TrajectoryTransformerModelTester: def __init__(self, parent, batch_size=13, n_embd=128, action_dim=6, observation_dim=17, is_training=True): self.parent = parent self.batch_size = batch_size self.n_embd = n_embd self.action_dim = action_dim self.observation_dim = observation_dim self.is_training = is_training self.seq_length = self.action_dim + self.observation_dim + 1 def prepare_config_and_inputs(self): trajectories = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(self.batch_size)]).to( torch_device ) attention_mask = random_attention_mask((self.batch_size, self.seq_length)).to(torch_device) targets = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(self.batch_size)]).to( torch_device ) config = self.get_config() return config, trajectories, attention_mask, targets def get_config(self): return TrajectoryTransformerConfig( batch_size=self.batch_size, n_embd=self.n_embd, action_dim=self.action_dim, observation_dim=self.observation_dim, ) def create_and_check_model(self, config, input_dict): model = TrajectoryTransformerModel(config=config) model.to(torch_device) model.eval() result = model(trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"]) result = model( trajectories=input_dict["trajectories"], output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ) self.parent.assertEqual(result.hidden_states[-1].shape, (self.batch_size, self.seq_length, self.n_embd)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, trajectories, attention_mask, targets) = config_and_inputs inputs_dict = {"trajectories": trajectories, "attention_mask": attention_mask, "targets": targets} return config, inputs_dict @require_torch class TrajectoryTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (TrajectoryTransformerModel,) if is_torch_available() else () # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids test_generate_without_input_ids = False # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features test_pruning = False test_resize_embeddings = False test_head_masking = False test_attention_outputs = False test_hidden_states_output = False test_inputs_embeds = False test_model_common_attributes = False test_torchscript = False def setUp(self): self.model_tester = TrajectoryTransformerModelTester(self) self.config_tester = ConfigTester(self, config_class=TrajectoryTransformerConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_model(*config_and_inputs) def test_conditional_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_model(*config_and_inputs) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["trajectories"] self.assertListEqual(arg_names[:1], expected_arg_names) # # Input is 'trajectories' not 'input_ids' def test_model_main_input_name(self): model_signature = inspect.signature(getattr(TrajectoryTransformerModel, "forward")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(TrajectoryTransformerModel.main_input_name, observed_main_input_name) def test_retain_grad_hidden_states_attentions(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions model = TrajectoryTransformerModel(config) model.to(torch_device) outputs = model( trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"], targets=input_dict["targets"], output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ) output = outputs[0] hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) def test_training(self): if not self.model_tester.is_training: return config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = TrajectoryTransformerModel(config) model.to(torch_device) model.train() loss = model( trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"], targets=input_dict["targets"], output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = TrajectoryTransformerModel(config) model.gradient_checkpointing_enable() model.to(torch_device) model.train() loss = model( trajectories=input_dict["trajectories"], attention_mask=input_dict["attention_mask"], targets=input_dict["targets"], output_hidden_states=True, output_attentions=True, use_cache=False, return_dict=True, ).loss loss.backward() def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) @slow def test_model_from_pretrained(self): for model_name in TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TrajectoryTransformerModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class TrajectoryTransformerModelIntegrationTest(unittest.TestCase): @slow def test_prediction(self): batch_size = 1 config = TrajectoryTransformerConfig.from_pretrained("CarlCochet/trajectory-transformer-halfcheetah-medium-v2") model = TrajectoryTransformerModel.from_pretrained( "CarlCochet/trajectory-transformer-halfcheetah-medium-v2", config=config ) model.to(torch_device) model.eval() seq_length = model.config.action_dim + model.config.observation_dim + 1 trajectories = torch.LongTensor( [[3, 19, 20, 22, 9, 7, 23, 10, 18, 14, 13, 4, 17, 11, 5, 6, 15, 21, 2, 8, 1, 0, 12, 16]] ).to(torch_device) outputs = model( trajectories=trajectories, output_hidden_states=True, output_attentions=True, use_cache=True, return_dict=True, ) output = outputs.logits expected_shape = torch.Size((batch_size, seq_length, model.config.vocab_size + 1)) expected_slice = torch.tensor( [[[-0.7193, -0.2532, -0.0898], [1.9429, 2.0434, 2.3975], [-3.3651, -2.8744, -2.4532]]] ).to(torch_device) output_slice = output[:, :3, :3] self.assertEqual(output.shape, expected_shape) self.assertTrue(torch.allclose(output_slice, expected_slice, atol=1e-4))

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_flax_{{cookiecutter.lowercase_modelname}}.py

# coding=utf-8 # Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax {{cookiecutter.modelname}} model. """ {% if cookiecutter.is_encoder_decoder_model == "False" %} from typing import Callable, Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, unfreeze, freeze from flax.linen import combine_masks, make_causal_mask from flax.linen import partitioning as nn_partitioning from flax.traverse_util import flatten_dict, unflatten_dict from flax.linen.attention import dot_product_attention_weights from jax import lax from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxBaseModelOutputWithPoolingAndCrossAttentions, FlaxCausalLMOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring, ) from ...utils import logging from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}" _CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config" _TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer" {{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`~{{cookiecutter.uppercase_modelname}}Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`~{{cookiecutter.uppercase_modelname}}ConfiTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`numpy.ndarray` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ remat = nn_partitioning.remat # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.word_embeddings = nn.Embed( self.config.vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.position_embeddings = nn.Embed( self.config.max_position_embeddings, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.token_type_embeddings = nn.Embed( self.config.type_vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True): # Embed inputs_embeds = self.word_embeddings(input_ids.astype("i4")) position_embeds = self.position_embeddings(position_ids.astype("i4")) token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) # Sum all embeddings hidden_states = inputs_embeds + token_type_embeddings + position_embeds # Layer Norm hidden_states = self.LayerNorm(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config causal: bool = False dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.head_dim = self.config.hidden_size // self.config.num_attention_heads if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError( "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`\ : {self.config.num_attention_heads}" ) self.query = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.key = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.value = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) @nn.compact # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states: Optional[jnp.array] = None, init_cache: bool = False, deterministic=True, output_attentions: bool = False, ): # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.query(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.key(key_value_states) value_states = self.value(key_value_states) else: # self_attention key_states = self.key(hidden_states) value_states = self.value(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, -1e10).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) # Mask heads if we want to if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}SelfOutput(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, hidden_states, input_tensor, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Attention(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config causal: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): self.self = Flax{{cookiecutter.camelcase_modelname}}SelfAttention(self.config, dtype=self.dtype) self.output = Flax{{cookiecutter.camelcase_modelname}}SelfOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states=None, init_cache=False, deterministic=True, output_attentions: bool = False, ): # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) attn_outputs = self.self( hidden_states, attention_mask, layer_head_mask=layer_head_mask, key_value_states=key_value_states, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attn_output = attn_outputs[0] hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attn_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Intermediate(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Output(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_output, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + attention_output) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Layer(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.attention = Flax{{cookiecutter.camelcase_modelname}}Attention(self.config, dtype=self.dtype) self.intermediate = Flax{{cookiecutter.camelcase_modelname}}Intermediate(self.config, dtype=self.dtype) self.output = Flax{{cookiecutter.camelcase_modelname}}Output(self.config, dtype=self.dtype) if self.config.add_cross_attention: self.crossattention = Flax{{cookiecutter.camelcase_modelname}}Attention(self.config, causal=False, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, ): # Self Attention attention_outputs = self.attention( hidden_states, attention_mask, layer_head_mask=layer_head_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = attention_outputs[0] # Cross-Attention Block if encoder_hidden_states is not None: cross_attention_outputs = self.crossattention( attention_output, attention_mask=encoder_attention_mask, layer_head_mask=layer_head_mask, key_value_states=encoder_hidden_states, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] hidden_states = self.intermediate(attention_output) hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attention_outputs[1],) if encoder_hidden_states is not None: outputs += (cross_attention_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}LayerCollection(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): if self.gradient_checkpointing: Flax{{cookiecutter.camelcase_modelname}}CheckpointLayer = remat(Flax{{cookiecutter.camelcase_modelname}}Layer, static_argnums=(5, 6, 7)) self.layers = [ Flax{{cookiecutter.camelcase_modelname}}CheckpointLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] else: self.layers = [ Flax{{cookiecutter.camelcase_modelname}}Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None # Check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.shape[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for \ {head_mask.shape[0]}." ) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, head_mask[i] if head_mask is not None else None, encoder_hidden_states, encoder_attention_mask, init_cache, deterministic, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states,) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Encoder(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): self.layer = Flax{{cookiecutter.camelcase_modelname}}LayerCollection(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layer( hidden_states, attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Pooler(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__(self, hidden_states): cls_hidden_state = hidden_states[:, 0] cls_hidden_state = self.dense(cls_hidden_state) return nn.tanh(cls_hidden_state) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype) self.activation = ACT2FN[self.config.hidden_act] self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return self.LayerNorm(hidden_states) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros def setup(self): self.transform = Flax{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(self.config, dtype=self.dtype) self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False) self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,)) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.transform(hidden_states) if shared_embedding is not None: hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: hidden_states = self.decoder(hidden_states) hidden_states += self.bias return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyMLMHead with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self): self.predictions = Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(self.config, dtype=self.dtype) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyNSPHead with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}OnlyNSPHead(nn.Module): dtype: jnp.dtype = jnp.float32 def setup(self): self.seq_relationship = nn.Dense(2, dtype=self.dtype) def __call__(self, pooled_output): return self.seq_relationship(pooled_output) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainingHeads with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}PreTrainingHeads(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self): self.predictions = Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(self.config, dtype=self.dtype) self.seq_relationship = nn.Dense(2, dtype=self.dtype) def __call__(self, hidden_states, pooled_output, shared_embedding=None): prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = {{cookiecutter.camelcase_modelname}}Config base_model_prefix = "{{cookiecutter.lowercase_modelname}}" module_class: nn.Module = None def __init__( self, config: {{cookiecutter.camelcase_modelname}}Config, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, gradient_checkpointing: bool = False, **kwargs ): module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing def enable_gradient_checkpointing(self): self._module = self.module_class( config=self.config, dtype=self.dtype, gradient_checkpointing=True, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_weights with Bert->{{cookiecutter.camelcase_modelname}} def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") token_type_ids = jnp.zeros_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) attention_mask = jnp.ones_like(input_ids) head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_cache with Bert->{{cookiecutter.camelcase_modelname}} def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length)) attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length")) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.__call__ with Bert->{{cookiecutter.camelcase_modelname}} def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, past_key_values: dict = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # init input tensors if not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} if self.config.add_cross_attention: # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxBertAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] else: outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, ) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Module(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation add_pooling_layer: bool = True gradient_checkpointing: bool = False def setup(self): self.embeddings = Flax{{cookiecutter.camelcase_modelname}}Embeddings(self.config, dtype=self.dtype) self.encoder = Flax{{cookiecutter.camelcase_modelname}}Encoder(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.pooler = Flax{{cookiecutter.camelcase_modelname}}Pooler(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # make sure `token_type_ids` is correctly initialized when not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) # make sure `position_ids` is correctly initialized when not passed if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) hidden_states = self.embeddings( input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic ) outputs = self.encoder( hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] pooled = self.pooler(hidden_states) if self.add_pooling_layer else None if not return_dict: # if pooled is None, don't return it if pooled is None: return (hidden_states,) + outputs[1:] return (hidden_states, pooled) + outputs[1:] return FlaxBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=hidden_states, pooler_output=pooled, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) add_start_docstrings( "The bare {{cookiecutter.camelcase_modelname}} Model transformer outputting raw hidden-states without any specific head on top.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}Model(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}Module class Flax{{cookiecutter.camelcase_modelname}}ForMaskedLMModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""{{cookiecutter.camelcase_modelname}} Model with a `language modeling` head on top for MLM training. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING) class Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLMModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""{{cookiecutter.camelcase_modelname}} Model with a `language modeling` head on top for CLM training. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense( self.config.num_labels, dtype=self.dtype, ) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) if not return_dict: return (logits,) + outputs[2:] return FlaxSequenceClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC, ) class Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoiceModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(1, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): num_choices = input_ids.shape[1] input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape(-1, num_choices) if not return_dict: return (reshaped_logits,) + outputs[2:] return FlaxMultipleChoiceModelOutput( logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoiceModule overwrite_call_docstring( Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForTokenClassificationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.classifier(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxTokenClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForTokenClassificationModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing) self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.qa_outputs(hidden_states) start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: return (start_logits, end_logits) + outputs[1:] return FlaxQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, token_type_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, ) {# encoder_decoder #} {% else %} import math import random from functools import partial from typing import Callable, Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, unfreeze, freeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...utils import add_start_docstrings, replace_return_docstrings from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput, FlaxSeq2SeqModelOutput, FlaxSeq2SeqQuestionAnsweringModelOutput, FlaxSeq2SeqSequenceClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import logging from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}" _CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config" _TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer" {{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`~{{cookiecutter.camelcase_modelname}}Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ {{cookiecutter.uppercase_modelname}}_ENCODE_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ {{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = jnp.roll(input_ids, 1, axis=-1) shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids class Flax{{cookiecutter.camelcase_modelname}}Attention(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads assert ( self.head_dim * self.num_heads == self.embed_dim ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class Flax{{cookiecutter.camelcase_modelname}}EncoderLayer(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = Flax{{cookiecutter.camelcase_modelname}}Attention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.encoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.fc1 = nn.Dense( self.config.encoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class Flax{{cookiecutter.camelcase_modelname}}EncoderLayerCollection(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ Flax{{cookiecutter.camelcase_modelname}}EncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers) ] self.layerdrop = self.config.encoder_layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for encoder_layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions, deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class Flax{{cookiecutter.camelcase_modelname}}DecoderLayer(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = Flax{{cookiecutter.camelcase_modelname}}Attention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype) self.encoder_attn = Flax{{cookiecutter.camelcase_modelname}}Attention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype) self.fc1 = nn.Dense( self.config.decoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class Flax{{cookiecutter.camelcase_modelname}}DecoderLayerCollection(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ Flax{{cookiecutter.camelcase_modelname}}DecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers) ] self.layerdrop = self.config.decoder_layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class Flax{{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" config: {{cookiecutter.camelcase_modelname}}Config inner_dim: int num_classes: int pooler_dropout: float dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.dropout = nn.Dropout(rate=self.pooler_dropout) self.out_proj = nn.Dense( self.num_classes, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__(self, hidden_states: jnp.ndarray, deterministic: bool): hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = jnp.tanh(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class Flax{{cookiecutter.camelcase_modelname}}Encoder(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation embed_tokens: Optional[nn.Embed] = None def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_source_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0 if self.embed_tokens is None: self.embed_tokens = nn.Embed( self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) # {{cookiecutter.camelcase_modelname}} is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = Flax{{cookiecutter.camelcase_modelname}}EncoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs return FlaxBaseModelOutput( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class Flax{{cookiecutter.camelcase_modelname}}Decoder(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation embed_tokens: Optional[nn.Embed] = None def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 if self.embed_tokens is None: self.embed_tokens = nn.Embed( self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) # {{cookiecutter.camelcase_modelname}} is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = Flax{{cookiecutter.camelcase_modelname}}DecoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions positions = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) class Flax{{cookiecutter.camelcase_modelname}}Module(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.shared = nn.Embed( self.config.vocab_size, self.config.d_model, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.encoder = Flax{{cookiecutter.camelcase_modelname}}Encoder(self.config, dtype=self.dtype, embed_tokens=self.shared) self.decoder = Flax{{cookiecutter.camelcase_modelname}}Decoder(self.config, dtype=self.dtype, embed_tokens=self.shared) def _get_encoder_module(self): return self.encoder def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedModel): config_class = {{cookiecutter.camelcase_modelname}}Config base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: {{cookiecutter.camelcase_modelname}}Config, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") # make sure initialization pass will work for Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id) attention_mask = jnp.ones_like(input_ids) decoder_input_ids = input_ids decoder_attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings({{cookiecutter.uppercase_modelname}}_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class={{cookiecutter.camelcase_modelname}}Config) def encode( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors='np') >>> encoder_outputs = model.encode(**inputs) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): encode_module = module._get_encoder_module() return encode_module(input_ids, attention_mask, position_ids, **kwargs) return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) @add_start_docstrings({{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class={{cookiecutter.camelcase_modelname}}Config) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors='np') >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> last_decoder_hidden_states = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by Flax{{cookiecutter.camelcase_modelname}}Attention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # prepare decoder inputs if decoder_input_ids is None: decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id ) if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) @add_start_docstrings( "The bare {{cookiecutter.camelcase_modelname}} Model transformer outputting raw hidden-states without any specific head on top.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}Model(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = Flax{{cookiecutter.camelcase_modelname}}Module append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}Model, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGenerationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros def setup(self): self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.model.shared.num_embeddings, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings)) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["shared"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) lm_logits += self.final_logits_bias.astype(self.dtype) if not return_dict: output = (lm_logits,) + outputs[1:] return output return FlaxSeq2SeqLMOutput( logits=lm_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( "The {{cookiecutter.uppercase_modelname}} Model with a language modeling head. Can be used for summarization.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING ) class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGenerationModule dtype: jnp.dtype = jnp.float32 @add_start_docstrings({{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class={{cookiecutter.camelcase_modelname}}Config) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, deterministic: bool = True, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors='np') >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by Flax{{cookiecutter.camelcase_modelname}}Attention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() outputs = decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = module.model.variables["params"]["shared"]["embedding"] lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = module.lm_head(hidden_states) lm_logits += module.final_logits_bias.astype(self.dtype) return lm_logits, outputs outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, rngs=rngs, mutable=mutable, method=_decoder_forward, ) if past_key_values is None: lm_logits, decoder_outputs = outputs else: (lm_logits, decoder_outputs), past = outputs if return_dict: outputs = FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, ) else: outputs = (lm_logits,) + decoder_outputs[1:] # add updated cache to model output if past_key_values is not None and return_dict: outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def prepare_inputs_for_generation( self, decoder_input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None, decoder_attention_mask: Optional[jnp.DeviceArray] = None, encoder_outputs=None, **kwargs ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "encoder_attention_mask": attention_mask, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING = """ Returns: Summarization example: ```python >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np') >>> # Generate Summary >>> summary_ids = model.generate(inputs['input_ids']).sequences >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)) ``` Mask filling example: ```python >>> import jax >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> TXT = "My friends are <mask> but they eat too many carbs." >>> input_ids = tokenizer([TXT], return_tensors='np')['input_ids'] >>> logits = model(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() >>> probs = jax.nn.softmax(logits[0, masked_index], axis=0) >>> values, predictions = jax.lax.top_k(probs, k=1) >>> tokenizer.decode(predictions).split() ``` """ overwrite_call_docstring( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING + FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING ) append_replace_return_docstrings( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 num_labels: Optional[int] = None def setup(self): self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype) self.classification_head = Flax{{cookiecutter.camelcase_modelname}}ClassificationHead( config=self.config, inner_dim=self.config.d_model, num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels, pooler_dropout=self.config.classifier_dropout, ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] # last hidden state eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0) # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer: if len(jnp.unique(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") if any(eos_mask.sum(1) == 0): raise ValueError("There are missing <eos> tokens in input_ids") # Ensure to keep 1 only for the last <eos> token for each example eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6 eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0) sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1) logits = self.classification_head(sentence_representation, deterministic=deterministic) if not return_dict: output = (logits,) + outputs[1:] return output return FlaxSeq2SeqSequenceClassifierOutput( logits=logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule dtype = jnp.float32 append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqSequenceClassifierOutput, _CONFIG_FOR_DOC, ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 num_labels = 2 def setup(self): self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype) self.qa_outputs = nn.Dense( self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: output = (start_logits, end_logits) + outputs[1:] return output return FlaxSeq2SeqQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ {{cookiecutter.uppercase_modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule dtype = jnp.float32 append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) {% endif -%}

# coding=utf-8 # Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax {{cookiecutter.modelname}} model. """ {% if cookiecutter.is_encoder_decoder_model == "False" %} from typing import Callable, Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, unfreeze, freeze from flax.linen import combine_masks, make_causal_mask from flax.linen import partitioning as nn_partitioning from flax.traverse_util import flatten_dict, unflatten_dict from flax.linen.attention import dot_product_attention_weights from jax import lax from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxBaseModelOutputWithPoolingAndCrossAttentions, FlaxCausalLMOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring, ) from ...utils import logging from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}" _CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config" _TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer" {{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`~{{cookiecutter.uppercase_modelname}}Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`~{{cookiecutter.uppercase_modelname}}ConfiTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`numpy.ndarray` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ remat = nn_partitioning.remat # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.word_embeddings = nn.Embed( self.config.vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.position_embeddings = nn.Embed( self.config.max_position_embeddings, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.token_type_embeddings = nn.Embed( self.config.type_vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True): # Embed inputs_embeds = self.word_embeddings(input_ids.astype("i4")) position_embeds = self.position_embeddings(position_ids.astype("i4")) token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) # Sum all embeddings hidden_states = inputs_embeds + token_type_embeddings + position_embeds # Layer Norm hidden_states = self.LayerNorm(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config causal: bool = False dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.head_dim = self.config.hidden_size // self.config.num_attention_heads if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError( "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`\ : {self.config.num_attention_heads}" ) self.query = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.key = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.value = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) @nn.compact # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states: Optional[jnp.array] = None, init_cache: bool = False, deterministic=True, output_attentions: bool = False, ): # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.query(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.key(key_value_states) value_states = self.value(key_value_states) else: # self_attention key_states = self.key(hidden_states) value_states = self.value(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, -1e10).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) # Mask heads if we want to if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}SelfOutput(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, hidden_states, input_tensor, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Attention(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config causal: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): self.self = Flax{{cookiecutter.camelcase_modelname}}SelfAttention(self.config, dtype=self.dtype) self.output = Flax{{cookiecutter.camelcase_modelname}}SelfOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, key_value_states=None, init_cache=False, deterministic=True, output_attentions: bool = False, ): # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) attn_outputs = self.self( hidden_states, attention_mask, layer_head_mask=layer_head_mask, key_value_states=key_value_states, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attn_output = attn_outputs[0] hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attn_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Intermediate(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Output(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_output, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + attention_output) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Layer(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.attention = Flax{{cookiecutter.camelcase_modelname}}Attention(self.config, dtype=self.dtype) self.intermediate = Flax{{cookiecutter.camelcase_modelname}}Intermediate(self.config, dtype=self.dtype) self.output = Flax{{cookiecutter.camelcase_modelname}}Output(self.config, dtype=self.dtype) if self.config.add_cross_attention: self.crossattention = Flax{{cookiecutter.camelcase_modelname}}Attention(self.config, causal=False, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, ): # Self Attention attention_outputs = self.attention( hidden_states, attention_mask, layer_head_mask=layer_head_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = attention_outputs[0] # Cross-Attention Block if encoder_hidden_states is not None: cross_attention_outputs = self.crossattention( attention_output, attention_mask=encoder_attention_mask, layer_head_mask=layer_head_mask, key_value_states=encoder_hidden_states, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] hidden_states = self.intermediate(attention_output) hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attention_outputs[1],) if encoder_hidden_states is not None: outputs += (cross_attention_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}LayerCollection(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): if self.gradient_checkpointing: Flax{{cookiecutter.camelcase_modelname}}CheckpointLayer = remat(Flax{{cookiecutter.camelcase_modelname}}Layer, static_argnums=(5, 6, 7)) self.layers = [ Flax{{cookiecutter.camelcase_modelname}}CheckpointLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] else: self.layers = [ Flax{{cookiecutter.camelcase_modelname}}Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None # Check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.shape[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for \ {head_mask.shape[0]}." ) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, head_mask[i] if head_mask is not None else None, encoder_hidden_states, encoder_attention_mask, init_cache, deterministic, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states,) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Encoder(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation gradient_checkpointing: bool = False def setup(self): self.layer = Flax{{cookiecutter.camelcase_modelname}}LayerCollection(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) def __call__( self, hidden_states, attention_mask, head_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layer( hidden_states, attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Pooler(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__(self, hidden_states): cls_hidden_state = hidden_states[:, 0] cls_hidden_state = self.dense(cls_hidden_state) return nn.tanh(cls_hidden_state) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype) self.activation = ACT2FN[self.config.hidden_act] self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return self.LayerNorm(hidden_states) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros def setup(self): self.transform = Flax{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(self.config, dtype=self.dtype) self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False) self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,)) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.transform(hidden_states) if shared_embedding is not None: hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: hidden_states = self.decoder(hidden_states) hidden_states += self.bias return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyMLMHead with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self): self.predictions = Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(self.config, dtype=self.dtype) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyNSPHead with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}OnlyNSPHead(nn.Module): dtype: jnp.dtype = jnp.float32 def setup(self): self.seq_relationship = nn.Dense(2, dtype=self.dtype) def __call__(self, pooled_output): return self.seq_relationship(pooled_output) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainingHeads with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}PreTrainingHeads(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self): self.predictions = Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(self.config, dtype=self.dtype) self.seq_relationship = nn.Dense(2, dtype=self.dtype) def __call__(self, hidden_states, pooled_output, shared_embedding=None): prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = {{cookiecutter.camelcase_modelname}}Config base_model_prefix = "{{cookiecutter.lowercase_modelname}}" module_class: nn.Module = None def __init__( self, config: {{cookiecutter.camelcase_modelname}}Config, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, gradient_checkpointing: bool = False, **kwargs ): module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing def enable_gradient_checkpointing(self): self._module = self.module_class( config=self.config, dtype=self.dtype, gradient_checkpointing=True, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_weights with Bert->{{cookiecutter.camelcase_modelname}} def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") token_type_ids = jnp.zeros_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) attention_mask = jnp.ones_like(input_ids) head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init( rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_cache with Bert->{{cookiecutter.camelcase_modelname}} def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length)) attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length")) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.__call__ with Bert->{{cookiecutter.camelcase_modelname}} def __call__( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, past_key_values: dict = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # init input tensors if not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} if self.config.add_cross_attention: # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxBertAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] else: outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), token_type_ids=jnp.array(token_type_ids, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), head_mask=jnp.array(head_mask, dtype="i4"), deterministic=not train, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, rngs=rngs, ) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->{{cookiecutter.camelcase_modelname}} class Flax{{cookiecutter.camelcase_modelname}}Module(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation add_pooling_layer: bool = True gradient_checkpointing: bool = False def setup(self): self.embeddings = Flax{{cookiecutter.camelcase_modelname}}Embeddings(self.config, dtype=self.dtype) self.encoder = Flax{{cookiecutter.camelcase_modelname}}Encoder(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.pooler = Flax{{cookiecutter.camelcase_modelname}}Pooler(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # make sure `token_type_ids` is correctly initialized when not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) # make sure `position_ids` is correctly initialized when not passed if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) hidden_states = self.embeddings( input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic ) outputs = self.encoder( hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] pooled = self.pooler(hidden_states) if self.add_pooling_layer else None if not return_dict: # if pooled is None, don't return it if pooled is None: return (hidden_states,) + outputs[1:] return (hidden_states, pooled) + outputs[1:] return FlaxBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=hidden_states, pooler_output=pooled, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) add_start_docstrings( "The bare {{cookiecutter.camelcase_modelname}} Model transformer outputting raw hidden-states without any specific head on top.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}Model(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}Module class Flax{{cookiecutter.camelcase_modelname}}ForMaskedLMModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""{{cookiecutter.camelcase_modelname}} Model with a `language modeling` head on top for MLM training. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING) class Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLMModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""{{cookiecutter.camelcase_modelname}} Model with a `language modeling` head on top for CLM training. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense( self.config.num_labels, dtype=self.dtype, ) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) if not return_dict: return (logits,) + outputs[2:] return FlaxSequenceClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC, ) class Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoiceModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(1, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): num_choices = input_ids.shape[1] input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape(-1, num_choices) if not return_dict: return (reshaped_logits,) + outputs[2:] return FlaxMultipleChoiceModelOutput( logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoiceModule overwrite_call_docstring( Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForTokenClassificationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.classifier(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxTokenClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForTokenClassificationModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing) self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.qa_outputs(hidden_states) start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: return (start_logits, end_logits) + outputs[1:] return FlaxQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 gradient_checkpointing: bool = False def setup(self): self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing) self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, token_type_ids: Optional[jnp.ndarray] = None, head_mask: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.{{cookiecutter.lowercase_modelname}}( input_ids, attention_mask, token_type_ids, position_ids, head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, ) {# encoder_decoder #} {% else %} import math import random from functools import partial from typing import Callable, Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, unfreeze, freeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...utils import add_start_docstrings, replace_return_docstrings from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput, FlaxSeq2SeqModelOutput, FlaxSeq2SeqQuestionAnsweringModelOutput, FlaxSeq2SeqSequenceClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import logging from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}" _CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config" _TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer" {{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`~{{cookiecutter.camelcase_modelname}}Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ {{cookiecutter.uppercase_modelname}}_ENCODE_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ {{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = jnp.roll(input_ids, 1, axis=-1) shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids class Flax{{cookiecutter.camelcase_modelname}}Attention(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads assert ( self.head_dim * self.num_heads == self.embed_dim ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class Flax{{cookiecutter.camelcase_modelname}}EncoderLayer(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = Flax{{cookiecutter.camelcase_modelname}}Attention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.encoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.fc1 = nn.Dense( self.config.encoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class Flax{{cookiecutter.camelcase_modelname}}EncoderLayerCollection(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ Flax{{cookiecutter.camelcase_modelname}}EncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers) ] self.layerdrop = self.config.encoder_layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for encoder_layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions, deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class Flax{{cookiecutter.camelcase_modelname}}DecoderLayer(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = Flax{{cookiecutter.camelcase_modelname}}Attention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype) self.encoder_attn = Flax{{cookiecutter.camelcase_modelname}}Attention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype) self.fc1 = nn.Dense( self.config.decoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class Flax{{cookiecutter.camelcase_modelname}}DecoderLayerCollection(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ Flax{{cookiecutter.camelcase_modelname}}DecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers) ] self.layerdrop = self.config.decoder_layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class Flax{{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" config: {{cookiecutter.camelcase_modelname}}Config inner_dim: int num_classes: int pooler_dropout: float dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.dropout = nn.Dropout(rate=self.pooler_dropout) self.out_proj = nn.Dense( self.num_classes, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__(self, hidden_states: jnp.ndarray, deterministic: bool): hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = jnp.tanh(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class Flax{{cookiecutter.camelcase_modelname}}Encoder(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation embed_tokens: Optional[nn.Embed] = None def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_source_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0 if self.embed_tokens is None: self.embed_tokens = nn.Embed( self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) # {{cookiecutter.camelcase_modelname}} is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = Flax{{cookiecutter.camelcase_modelname}}EncoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs return FlaxBaseModelOutput( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class Flax{{cookiecutter.camelcase_modelname}}Decoder(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation embed_tokens: Optional[nn.Embed] = None def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 if self.embed_tokens is None: self.embed_tokens = nn.Embed( self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) # {{cookiecutter.camelcase_modelname}} is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = Flax{{cookiecutter.camelcase_modelname}}DecoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions positions = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) class Flax{{cookiecutter.camelcase_modelname}}Module(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.shared = nn.Embed( self.config.vocab_size, self.config.d_model, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.encoder = Flax{{cookiecutter.camelcase_modelname}}Encoder(self.config, dtype=self.dtype, embed_tokens=self.shared) self.decoder = Flax{{cookiecutter.camelcase_modelname}}Decoder(self.config, dtype=self.dtype, embed_tokens=self.shared) def _get_encoder_module(self): return self.encoder def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedModel): config_class = {{cookiecutter.camelcase_modelname}}Config base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: {{cookiecutter.camelcase_modelname}}Config, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") # make sure initialization pass will work for Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id) attention_mask = jnp.ones_like(input_ids) decoder_input_ids = input_ids decoder_attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings({{cookiecutter.uppercase_modelname}}_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class={{cookiecutter.camelcase_modelname}}Config) def encode( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors='np') >>> encoder_outputs = model.encode(**inputs) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): encode_module = module._get_encoder_module() return encode_module(input_ids, attention_mask, position_ids, **kwargs) return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) @add_start_docstrings({{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class={{cookiecutter.camelcase_modelname}}Config) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors='np') >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> last_decoder_hidden_states = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by Flax{{cookiecutter.camelcase_modelname}}Attention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # prepare decoder inputs if decoder_input_ids is None: decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id ) if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) @add_start_docstrings( "The bare {{cookiecutter.camelcase_modelname}} Model transformer outputting raw hidden-states without any specific head on top.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}Model(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = Flax{{cookiecutter.camelcase_modelname}}Module append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}Model, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGenerationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros def setup(self): self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.model.shared.num_embeddings, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings)) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["shared"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) lm_logits += self.final_logits_bias.astype(self.dtype) if not return_dict: output = (lm_logits,) + outputs[1:] return output return FlaxSeq2SeqLMOutput( logits=lm_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( "The {{cookiecutter.uppercase_modelname}} Model with a language modeling head. Can be used for summarization.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING ) class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGenerationModule dtype: jnp.dtype = jnp.float32 @add_start_docstrings({{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class={{cookiecutter.camelcase_modelname}}Config) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, deterministic: bool = True, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors='np') >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by Flax{{cookiecutter.camelcase_modelname}}Attention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() outputs = decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = module.model.variables["params"]["shared"]["embedding"] lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = module.lm_head(hidden_states) lm_logits += module.final_logits_bias.astype(self.dtype) return lm_logits, outputs outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, rngs=rngs, mutable=mutable, method=_decoder_forward, ) if past_key_values is None: lm_logits, decoder_outputs = outputs else: (lm_logits, decoder_outputs), past = outputs if return_dict: outputs = FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, ) else: outputs = (lm_logits,) + decoder_outputs[1:] # add updated cache to model output if past_key_values is not None and return_dict: outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def prepare_inputs_for_generation( self, decoder_input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None, decoder_attention_mask: Optional[jnp.DeviceArray] = None, encoder_outputs=None, **kwargs ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "encoder_attention_mask": attention_mask, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING = """ Returns: Summarization example: ```python >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np') >>> # Generate Summary >>> summary_ids = model.generate(inputs['input_ids']).sequences >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)) ``` Mask filling example: ```python >>> import jax >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') >>> TXT = "My friends are <mask> but they eat too many carbs." >>> input_ids = tokenizer([TXT], return_tensors='np')['input_ids'] >>> logits = model(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() >>> probs = jax.nn.softmax(logits[0, masked_index], axis=0) >>> values, predictions = jax.lax.top_k(probs, k=1) >>> tokenizer.decode(predictions).split() ``` """ overwrite_call_docstring( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING + FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING ) append_replace_return_docstrings( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 num_labels: Optional[int] = None def setup(self): self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype) self.classification_head = Flax{{cookiecutter.camelcase_modelname}}ClassificationHead( config=self.config, inner_dim=self.config.d_model, num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels, pooler_dropout=self.config.classifier_dropout, ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] # last hidden state eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0) # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer: if len(jnp.unique(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") if any(eos_mask.sum(1) == 0): raise ValueError("There are missing <eos> tokens in input_ids") # Ensure to keep 1 only for the last <eos> token for each example eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6 eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0) sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1) logits = self.classification_head(sentence_representation, deterministic=deterministic) if not return_dict: output = (logits,) + outputs[1:] return output return FlaxSeq2SeqSequenceClassifierOutput( logits=logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ {{cookiecutter.camelcase_modelname}} model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule dtype = jnp.float32 append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqSequenceClassifierOutput, _CONFIG_FOR_DOC, ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule(nn.Module): config: {{cookiecutter.camelcase_modelname}}Config dtype: jnp.dtype = jnp.float32 num_labels = 2 def setup(self): self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype) self.qa_outputs = nn.Dense( self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: output = (start_logits, end_logits) + outputs[1:] return output return FlaxSeq2SeqQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ {{cookiecutter.uppercase_modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING, ) class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel): module_class = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule dtype = jnp.float32 append_call_sample_docstring( Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, ) {% endif -%}

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/ibert/modeling_ibert.py

# coding=utf-8 # Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao, # Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team. # Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch I-BERT model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_ibert import IBertConfig from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base" _CONFIG_FOR_DOC = "IBertConfig" _TOKENIZER_FOR_DOC = "RobertaTokenizer" IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "kssteven/ibert-roberta-base", "kssteven/ibert-roberta-large", "kssteven/ibert-roberta-large-mnli", ] class IBertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.embedding_bit = 8 self.embedding_act_bit = 16 self.act_bit = 8 self.ln_input_bit = 22 self.ln_output_bit = 32 self.word_embeddings = QuantEmbedding( config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id, weight_bit=self.embedding_bit, quant_mode=self.quant_mode, ) self.token_type_embeddings = QuantEmbedding( config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode ) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") # End copy self.padding_idx = config.pad_token_id self.position_embeddings = QuantEmbedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx, weight_bit=self.embedding_bit, quant_mode=self.quant_mode, ) # Integer-only addition between embeddings self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode) self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids( input_ids, self.padding_idx, past_key_values_length ).to(input_ids.device) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids) else: inputs_embeds_scaling_factor = None token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids) embeddings, embeddings_scaling_factor = self.embeddings_act1( inputs_embeds, inputs_embeds_scaling_factor, identity=token_type_embeddings, identity_scaling_factor=token_type_embeddings_scaling_factor, ) if self.position_embedding_type == "absolute": position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids) embeddings, embeddings_scaling_factor = self.embeddings_act1( embeddings, embeddings_scaling_factor, identity=position_embeddings, identity_scaling_factor=position_embeddings_scaling_factor, ) embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor) embeddings = self.dropout(embeddings) embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor) return embeddings, embeddings_scaling_factor def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) class IBertSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.quant_mode = config.quant_mode self.weight_bit = 8 self.bias_bit = 32 self.act_bit = 8 self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size # Q, K, V Linear layers self.query = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.key = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.value = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) # Requantization (32bit -> 8bit) for Q, K, V activations self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type != "absolute": raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`") self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): # Projection mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor) mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor) mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor) # Requantization query_layer, query_layer_scaling_factor = self.query_activation( mixed_query_layer, mixed_query_layer_scaling_factor ) key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor) value_layer, value_layer_scaling_factor = self.value_activation( mixed_value_layer, mixed_value_layer_scaling_factor ) # Transpose query_layer = self.transpose_for_scores(query_layer) key_layer = self.transpose_for_scores(key_layer) value_layer = self.transpose_for_scores(value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) scale = math.sqrt(self.attention_head_size) attention_scores = attention_scores / scale if self.quant_mode: attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale else: attention_scores_scaling_factor = None if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in IBertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs, attention_probs_scaling_factor = self.softmax( attention_scores, attention_scores_scaling_factor ) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) if attention_probs_scaling_factor is not None: context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor else: context_layer_scaling_factor = None context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) # requantization: 32-bit -> 8-bit context_layer, context_layer_scaling_factor = self.output_activation( context_layer, context_layer_scaling_factor ) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) output_scaling_factor = ( (context_layer_scaling_factor, attention_probs_scaling_factor) if output_attentions else (context_layer_scaling_factor,) ) return outputs, output_scaling_factor class IBertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.ln_input_bit = 22 self.ln_output_bit = 32 self.dense = QuantLinear( config.hidden_size, config.hidden_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode) self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states = self.dropout(hidden_states) hidden_states, hidden_states_scaling_factor = self.ln_input_act( hidden_states, hidden_states_scaling_factor, identity=input_tensor, identity_scaling_factor=input_tensor_scaling_factor, ) hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertAttention(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.self = IBertSelfAttention(config) self.output = IBertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): self_outputs, self_outputs_scaling_factor = self.self( hidden_states, hidden_states_scaling_factor, attention_mask, head_mask, output_attentions, ) attention_output, attention_output_scaling_factor = self.output( self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor ) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:] return outputs, outputs_scaling_factor class IBertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.dense = QuantLinear( config.hidden_size, config.intermediate_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) if config.hidden_act != "gelu": raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`") self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) def forward(self, hidden_states, hidden_states_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn( hidden_states, hidden_states_scaling_factor ) # Requantization: 32bit -> 8-bit hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertOutput(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.ln_input_bit = 22 self.ln_output_bit = 32 self.dense = QuantLinear( config.intermediate_size, config.hidden_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode) self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states = self.dropout(hidden_states) hidden_states, hidden_states_scaling_factor = self.ln_input_act( hidden_states, hidden_states_scaling_factor, identity=input_tensor, identity_scaling_factor=input_tensor_scaling_factor, ) hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertLayer(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.seq_len_dim = 1 self.attention = IBertAttention(config) self.intermediate = IBertIntermediate(config) self.output = IBertOutput(config) self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): self_attention_outputs, self_attention_outputs_scaling_factor = self.attention( hidden_states, hidden_states_scaling_factor, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] attention_output_scaling_factor = self_attention_outputs_scaling_factor[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output, layer_output_scaling_factor = self.feed_forward_chunk( attention_output, attention_output_scaling_factor ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output, attention_output_scaling_factor): attention_output, attention_output_scaling_factor = self.pre_intermediate_act( attention_output, attention_output_scaling_factor ) intermediate_output, intermediate_output_scaling_factor = self.intermediate( attention_output, attention_output_scaling_factor ) intermediate_output, intermediate_output_scaling_factor = self.pre_output_act( intermediate_output, intermediate_output_scaling_factor ) layer_output, layer_output_scaling_factor = self.output( intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor ) return layer_output, layer_output_scaling_factor class IBertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.quant_mode = config.quant_mode self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = None # `config.add_cross_attention` is not supported next_decoder_cache = None # `config.use_cache` is not supported for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None layer_outputs = layer_module( hidden_states, hidden_states_scaling_factor, attention_mask, layer_head_mask, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class IBertPooler(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class IBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = IBertConfig base_model_prefix = "ibert" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (QuantLinear, nn.Linear)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (QuantEmbedding, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, (IntLayerNorm, nn.LayerNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) def resize_token_embeddings(self, new_num_tokens=None): raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.") IBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`IBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ IBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.", IBERT_START_DOCSTRING, ) class IBertModel(IBertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. """ _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.quant_mode = config.quant_mode self.embeddings = IBertEmbeddings(config) self.encoder = IBertEncoder(config) self.pooler = IBertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output, embedding_output_scaling_factor = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, embedding_output_scaling_factor, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING) class IBertForMaskedLM(IBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.bias", "lm_head.decoder.weight"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.ibert = IBertModel(config, add_pooling_layer=False) self.lm_head = IBertLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class IBertLMHead(nn.Module): """I-BERT Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias @add_start_docstrings( """ I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, IBERT_START_DOCSTRING, ) class IBertForSequenceClassification(IBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.classifier = IBertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ I-BERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, IBERT_START_DOCSTRING, ) class IBertForMultipleChoice(IBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.ibert = IBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.ibert( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, IBERT_START_DOCSTRING, ) class IBertForTokenClassification(IBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class IBertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): hidden_states = features[:, 0, :] # take <s> token (equiv. to [CLS]) hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states @add_start_docstrings( """ I-BERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, IBERT_START_DOCSTRING, ) class IBertForQuestionAnswering(IBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's *utils.make_positions*. Args: input_ids (`torch.LongTensor`): Indices of input sequence tokens in the vocabulary. Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

# coding=utf-8 # Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao, # Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team. # Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch I-BERT model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_ibert import IBertConfig from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base" _CONFIG_FOR_DOC = "IBertConfig" _TOKENIZER_FOR_DOC = "RobertaTokenizer" IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "kssteven/ibert-roberta-base", "kssteven/ibert-roberta-large", "kssteven/ibert-roberta-large-mnli", ] class IBertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.embedding_bit = 8 self.embedding_act_bit = 16 self.act_bit = 8 self.ln_input_bit = 22 self.ln_output_bit = 32 self.word_embeddings = QuantEmbedding( config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id, weight_bit=self.embedding_bit, quant_mode=self.quant_mode, ) self.token_type_embeddings = QuantEmbedding( config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode ) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") # End copy self.padding_idx = config.pad_token_id self.position_embeddings = QuantEmbedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx, weight_bit=self.embedding_bit, quant_mode=self.quant_mode, ) # Integer-only addition between embeddings self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode) self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids( input_ids, self.padding_idx, past_key_values_length ).to(input_ids.device) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids) else: inputs_embeds_scaling_factor = None token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids) embeddings, embeddings_scaling_factor = self.embeddings_act1( inputs_embeds, inputs_embeds_scaling_factor, identity=token_type_embeddings, identity_scaling_factor=token_type_embeddings_scaling_factor, ) if self.position_embedding_type == "absolute": position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids) embeddings, embeddings_scaling_factor = self.embeddings_act1( embeddings, embeddings_scaling_factor, identity=position_embeddings, identity_scaling_factor=position_embeddings_scaling_factor, ) embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor) embeddings = self.dropout(embeddings) embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor) return embeddings, embeddings_scaling_factor def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) class IBertSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.quant_mode = config.quant_mode self.weight_bit = 8 self.bias_bit = 32 self.act_bit = 8 self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size # Q, K, V Linear layers self.query = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.key = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.value = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) # Requantization (32bit -> 8bit) for Q, K, V activations self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type != "absolute": raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`") self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): # Projection mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor) mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor) mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor) # Requantization query_layer, query_layer_scaling_factor = self.query_activation( mixed_query_layer, mixed_query_layer_scaling_factor ) key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor) value_layer, value_layer_scaling_factor = self.value_activation( mixed_value_layer, mixed_value_layer_scaling_factor ) # Transpose query_layer = self.transpose_for_scores(query_layer) key_layer = self.transpose_for_scores(key_layer) value_layer = self.transpose_for_scores(value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) scale = math.sqrt(self.attention_head_size) attention_scores = attention_scores / scale if self.quant_mode: attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale else: attention_scores_scaling_factor = None if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in IBertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs, attention_probs_scaling_factor = self.softmax( attention_scores, attention_scores_scaling_factor ) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) if attention_probs_scaling_factor is not None: context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor else: context_layer_scaling_factor = None context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) # requantization: 32-bit -> 8-bit context_layer, context_layer_scaling_factor = self.output_activation( context_layer, context_layer_scaling_factor ) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) output_scaling_factor = ( (context_layer_scaling_factor, attention_probs_scaling_factor) if output_attentions else (context_layer_scaling_factor,) ) return outputs, output_scaling_factor class IBertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.ln_input_bit = 22 self.ln_output_bit = 32 self.dense = QuantLinear( config.hidden_size, config.hidden_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode) self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states = self.dropout(hidden_states) hidden_states, hidden_states_scaling_factor = self.ln_input_act( hidden_states, hidden_states_scaling_factor, identity=input_tensor, identity_scaling_factor=input_tensor_scaling_factor, ) hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertAttention(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.self = IBertSelfAttention(config) self.output = IBertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): self_outputs, self_outputs_scaling_factor = self.self( hidden_states, hidden_states_scaling_factor, attention_mask, head_mask, output_attentions, ) attention_output, attention_output_scaling_factor = self.output( self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor ) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:] return outputs, outputs_scaling_factor class IBertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.dense = QuantLinear( config.hidden_size, config.intermediate_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) if config.hidden_act != "gelu": raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`") self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) def forward(self, hidden_states, hidden_states_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn( hidden_states, hidden_states_scaling_factor ) # Requantization: 32bit -> 8-bit hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertOutput(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.ln_input_bit = 22 self.ln_output_bit = 32 self.dense = QuantLinear( config.intermediate_size, config.hidden_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode) self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states = self.dropout(hidden_states) hidden_states, hidden_states_scaling_factor = self.ln_input_act( hidden_states, hidden_states_scaling_factor, identity=input_tensor, identity_scaling_factor=input_tensor_scaling_factor, ) hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertLayer(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.seq_len_dim = 1 self.attention = IBertAttention(config) self.intermediate = IBertIntermediate(config) self.output = IBertOutput(config) self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): self_attention_outputs, self_attention_outputs_scaling_factor = self.attention( hidden_states, hidden_states_scaling_factor, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] attention_output_scaling_factor = self_attention_outputs_scaling_factor[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output, layer_output_scaling_factor = self.feed_forward_chunk( attention_output, attention_output_scaling_factor ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output, attention_output_scaling_factor): attention_output, attention_output_scaling_factor = self.pre_intermediate_act( attention_output, attention_output_scaling_factor ) intermediate_output, intermediate_output_scaling_factor = self.intermediate( attention_output, attention_output_scaling_factor ) intermediate_output, intermediate_output_scaling_factor = self.pre_output_act( intermediate_output, intermediate_output_scaling_factor ) layer_output, layer_output_scaling_factor = self.output( intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor ) return layer_output, layer_output_scaling_factor class IBertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.quant_mode = config.quant_mode self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = None # `config.add_cross_attention` is not supported next_decoder_cache = None # `config.use_cache` is not supported for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None layer_outputs = layer_module( hidden_states, hidden_states_scaling_factor, attention_mask, layer_head_mask, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class IBertPooler(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class IBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = IBertConfig base_model_prefix = "ibert" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (QuantLinear, nn.Linear)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (QuantEmbedding, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, (IntLayerNorm, nn.LayerNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) def resize_token_embeddings(self, new_num_tokens=None): raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.") IBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`IBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ IBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.", IBERT_START_DOCSTRING, ) class IBertModel(IBertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. """ _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.quant_mode = config.quant_mode self.embeddings = IBertEmbeddings(config) self.encoder = IBertEncoder(config) self.pooler = IBertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output, embedding_output_scaling_factor = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, embedding_output_scaling_factor, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING) class IBertForMaskedLM(IBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.bias", "lm_head.decoder.weight"] _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.ibert = IBertModel(config, add_pooling_layer=False) self.lm_head = IBertLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class IBertLMHead(nn.Module): """I-BERT Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias @add_start_docstrings( """ I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, IBERT_START_DOCSTRING, ) class IBertForSequenceClassification(IBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.classifier = IBertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ I-BERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, IBERT_START_DOCSTRING, ) class IBertForMultipleChoice(IBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.ibert = IBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.ibert( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, IBERT_START_DOCSTRING, ) class IBertForTokenClassification(IBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class IBertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): hidden_states = features[:, 0, :] # take <s> token (equiv. to [CLS]) hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states @add_start_docstrings( """ I-BERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, IBERT_START_DOCSTRING, ) class IBertForQuestionAnswering(IBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's *utils.make_positions*. Args: input_ids (`torch.LongTensor`): Indices of input sequence tokens in the vocabulary. Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/mobilebert/modeling_mobilebert.py

# MIT License # # Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import math import os import warnings from dataclasses import dataclass from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mobilebert import MobileBertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/mobilebert-uncased" _CONFIG_FOR_DOC = "MobileBertConfig" _TOKENIZER_FOR_DOC = "MobileBertTokenizer" # TokenClassification docstring _CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "mrm8488/mobilebert-finetuned-ner" _TOKEN_CLASS_EXPECTED_OUTPUT = "['I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC']" _TOKEN_CLASS_EXPECTED_LOSS = 0.03 # QuestionAnswering docstring _CHECKPOINT_FOR_QA = "csarron/mobilebert-uncased-squad-v2" _QA_EXPECTED_OUTPUT = "'a nice puppet'" _QA_EXPECTED_LOSS = 3.98 _QA_TARGET_START_INDEX = 12 _QA_TARGET_END_INDEX = 13 # SequenceClassification docstring _CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "lordtt13/emo-mobilebert" _SEQ_CLASS_EXPECTED_OUTPUT = "'others'" _SEQ_CLASS_EXPECTED_LOSS = "4.72" MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = ["google/mobilebert-uncased"] def load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.replace("ffn_layer", "ffn") name = name.replace("FakeLayerNorm", "LayerNorm") name = name.replace("extra_output_weights", "dense/kernel") name = name.replace("bert", "mobilebert") name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class NoNorm(nn.Module): def __init__(self, feat_size, eps=None): super().__init__() self.bias = nn.Parameter(torch.zeros(feat_size)) self.weight = nn.Parameter(torch.ones(feat_size)) def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: return input_tensor * self.weight + self.bias NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm} class MobileBertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.trigram_input = config.trigram_input self.embedding_size = config.embedding_size self.hidden_size = config.hidden_size self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) embed_dim_multiplier = 3 if self.trigram_input else 1 embedded_input_size = self.embedding_size * embed_dim_multiplier self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if self.trigram_input: # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited # Devices (https://arxiv.org/abs/2004.02984) # # The embedding table in BERT models accounts for a substantial proportion of model size. To compress # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT. # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512 # dimensional output. inputs_embeds = torch.cat( [ nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0), inputs_embeds, nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0), ], dim=2, ) if self.trigram_input or self.embedding_size != self.hidden_size: inputs_embeds = self.embedding_transformation(inputs_embeds) # Add positional embeddings and token type embeddings, then layer # normalize and perform dropout. position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + position_embeddings + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class MobileBertSelfAttention(nn.Module): def __init__(self, config): super().__init__() self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.true_hidden_size, self.all_head_size) self.key = nn.Linear(config.true_hidden_size, self.all_head_size) self.value = nn.Linear( config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size ) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, query_tensor: torch.Tensor, key_tensor: torch.Tensor, value_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(query_tensor) mixed_key_layer = self.key(key_tensor) mixed_value_layer = self.value(value_tensor) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class MobileBertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.use_bottleneck = config.use_bottleneck self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps) if not self.use_bottleneck: self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor: layer_outputs = self.dense(hidden_states) if not self.use_bottleneck: layer_outputs = self.dropout(layer_outputs) layer_outputs = self.LayerNorm(layer_outputs + residual_tensor) return layer_outputs class MobileBertAttention(nn.Module): def __init__(self, config): super().__init__() self.self = MobileBertSelfAttention(config) self.output = MobileBertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, query_tensor: torch.Tensor, key_tensor: torch.Tensor, value_tensor: torch.Tensor, layer_input: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, ) -> Tuple[torch.Tensor]: self_outputs = self.self( query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, ) # Run a linear projection of `hidden_size` then add a residual # with `layer_input`. attention_output = self.output(self_outputs[0], layer_input) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class MobileBertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class OutputBottleneck(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.true_hidden_size, config.hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor: layer_outputs = self.dense(hidden_states) layer_outputs = self.dropout(layer_outputs) layer_outputs = self.LayerNorm(layer_outputs + residual_tensor) return layer_outputs class MobileBertOutput(nn.Module): def __init__(self, config): super().__init__() self.use_bottleneck = config.use_bottleneck self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size) if not self.use_bottleneck: self.dropout = nn.Dropout(config.hidden_dropout_prob) else: self.bottleneck = OutputBottleneck(config) def forward( self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor ) -> torch.Tensor: layer_output = self.dense(intermediate_states) if not self.use_bottleneck: layer_output = self.dropout(layer_output) layer_output = self.LayerNorm(layer_output + residual_tensor_1) else: layer_output = self.LayerNorm(layer_output + residual_tensor_1) layer_output = self.bottleneck(layer_output, residual_tensor_2) return layer_output class BottleneckLayer(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size) self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: layer_input = self.dense(hidden_states) layer_input = self.LayerNorm(layer_input) return layer_input class Bottleneck(nn.Module): def __init__(self, config): super().__init__() self.key_query_shared_bottleneck = config.key_query_shared_bottleneck self.use_bottleneck_attention = config.use_bottleneck_attention self.input = BottleneckLayer(config) if self.key_query_shared_bottleneck: self.attention = BottleneckLayer(config) def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]: # This method can return three different tuples of values. These different values make use of bottlenecks, # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory # usage. These linear layer have weights that are learned during training. # # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the # key, query, value, and "layer input" to be used by the attention layer. # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor # in the attention self output, after the attention scores have been computed. # # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return # four values, three of which have been passed through a bottleneck: the query and key, passed through the same # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck. # # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck, # and the residual layer will be this value passed through a bottleneck. bottlenecked_hidden_states = self.input(hidden_states) if self.use_bottleneck_attention: return (bottlenecked_hidden_states,) * 4 elif self.key_query_shared_bottleneck: shared_attention_input = self.attention(hidden_states) return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states) else: return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states) class FFNOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor: layer_outputs = self.dense(hidden_states) layer_outputs = self.LayerNorm(layer_outputs + residual_tensor) return layer_outputs class FFNLayer(nn.Module): def __init__(self, config): super().__init__() self.intermediate = MobileBertIntermediate(config) self.output = FFNOutput(config) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: intermediate_output = self.intermediate(hidden_states) layer_outputs = self.output(intermediate_output, hidden_states) return layer_outputs class MobileBertLayer(nn.Module): def __init__(self, config): super().__init__() self.use_bottleneck = config.use_bottleneck self.num_feedforward_networks = config.num_feedforward_networks self.attention = MobileBertAttention(config) self.intermediate = MobileBertIntermediate(config) self.output = MobileBertOutput(config) if self.use_bottleneck: self.bottleneck = Bottleneck(config) if config.num_feedforward_networks > 1: self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)]) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, ) -> Tuple[torch.Tensor]: if self.use_bottleneck: query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states) else: query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4 self_attention_outputs = self.attention( query_tensor, key_tensor, value_tensor, layer_input, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] s = (attention_output,) outputs = self_attention_outputs[1:] # add self attentions if we output attention weights if self.num_feedforward_networks != 1: for i, ffn_module in enumerate(self.ffn): attention_output = ffn_module(attention_output) s += (attention_output,) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output, hidden_states) outputs = ( (layer_output,) + outputs + ( torch.tensor(1000), query_tensor, key_tensor, value_tensor, layer_input, attention_output, intermediate_output, ) + s ) return outputs class MobileBertEncoder(nn.Module): def __init__(self, config): super().__init__() self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module( hidden_states, attention_mask, head_mask[i], output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class MobileBertPooler(nn.Module): def __init__(self, config): super().__init__() self.do_activate = config.classifier_activation if self.do_activate: self.dense = nn.Linear(config.hidden_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] if not self.do_activate: return first_token_tensor else: pooled_output = self.dense(first_token_tensor) pooled_output = torch.tanh(pooled_output) return pooled_output class MobileBertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class MobileBertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = MobileBertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False) self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.transform(hidden_states) hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0)) hidden_states += self.decoder.bias return hidden_states class MobileBertOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = MobileBertLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores class MobileBertPreTrainingHeads(nn.Module): def __init__(self, config): super().__init__() self.predictions = MobileBertLMPredictionHead(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class MobileBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileBertConfig pretrained_model_archive_map = MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST load_tf_weights = load_tf_weights_in_mobilebert base_model_prefix = "mobilebert" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, (nn.LayerNorm, NoNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) @dataclass class MobileBertForPreTrainingOutput(ModelOutput): """ Output type of [`MobileBertForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None prediction_logits: torch.FloatTensor = None seq_relationship_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None MOBILEBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MobileBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MOBILEBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top.", MOBILEBERT_START_DOCSTRING, ) class MobileBertModel(MobileBertPreTrainedModel): """ https://arxiv.org/pdf/2004.02984.pdf """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = MobileBertEmbeddings(config) self.encoder = MobileBertEncoder(config) self.pooler = MobileBertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForPreTraining(MobileBertPreTrainedModel): _keys_to_ignore_on_load_missing = [ "cls.predictions.decoder.weight", "cls.predictions.decoder.bias", "embeddings.position_ids", ] def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertPreTrainingHeads(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddigs): self.cls.predictions.decoder = new_embeddigs def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: # resize dense output embedings at first self.cls.predictions.dense = self._get_resized_lm_head( self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True ) return super().resize_token_embeddings(new_num_tokens=new_num_tokens) @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=MobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, next_sentence_label: Optional[torch.LongTensor] = None, output_attentions: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[torch.FloatTensor] = None, return_dict: Optional[torch.FloatTensor] = None, ) -> Union[Tuple, MobileBertForPreTrainingOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Examples: ```python >>> from transformers import MobileBertTokenizer, MobileBertForPreTraining >>> import torch >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased") >>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased") >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) >>> # Batch size 1 >>> outputs = model(input_ids) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return MobileBertForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING) class MobileBertForMaskedLM(MobileBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [ "cls.predictions.decoder.weight", "cls.predictions.decoder.bias", "embeddings.position_ids", ] def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config, add_pooling_layer=False) self.cls = MobileBertOnlyMLMHead(config) self.config = config # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddigs): self.cls.predictions.decoder = new_embeddigs def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: # resize dense output embedings at first self.cls.predictions.dense = self._get_resized_lm_head( self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True ) return super().resize_token_embeddings(new_num_tokens=new_num_tokens) @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, expected_output="'paris'", expected_loss=0.57, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class MobileBertOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output: torch.Tensor) -> torch.Tensor: seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score @add_start_docstrings( """MobileBert Model with a `next sentence prediction (classification)` head on top.""", MOBILEBERT_START_DOCSTRING, ) class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertOnlyNSPHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`. - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Examples: ```python >>> from transformers import MobileBertTokenizer, MobileBertForNextSentencePrediction >>> import torch >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased") >>> model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> loss = outputs.loss >>> logits = outputs.logits ```""" if "next_sentence_label" in kwargs: warnings.warn( "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" " `labels` instead.", FutureWarning, ) labels = kwargs.pop("next_sentence_label") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] seq_relationship_score = self.cls(pooled_output) next_sentence_loss = None if labels is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1)) if not return_dict: output = (seq_relationship_score,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing class MobileBertForSequenceClassification(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.mobilebert = MobileBertModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_SEQ_CLASS_EXPECTED_OUTPUT, expected_loss=_SEQ_CLASS_EXPECTED_LOSS, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering with Bert->MobileBert all-casing class MobileBertForQuestionAnswering(MobileBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_QA, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, qa_target_start_index=_QA_TARGET_START_INDEX, qa_target_end_index=_QA_TARGET_END_INDEX, expected_output=_QA_EXPECTED_OUTPUT, expected_loss=_QA_EXPECTED_LOSS, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice with Bert->MobileBert all-casing class MobileBertForMultipleChoice(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification with Bert->MobileBert all-casing class MobileBertForTokenClassification(MobileBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT, expected_loss=_TOKEN_CLASS_EXPECTED_LOSS, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# MIT License # # Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import math import os import warnings from dataclasses import dataclass from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mobilebert import MobileBertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/mobilebert-uncased" _CONFIG_FOR_DOC = "MobileBertConfig" _TOKENIZER_FOR_DOC = "MobileBertTokenizer" # TokenClassification docstring _CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "mrm8488/mobilebert-finetuned-ner" _TOKEN_CLASS_EXPECTED_OUTPUT = "['I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC']" _TOKEN_CLASS_EXPECTED_LOSS = 0.03 # QuestionAnswering docstring _CHECKPOINT_FOR_QA = "csarron/mobilebert-uncased-squad-v2" _QA_EXPECTED_OUTPUT = "'a nice puppet'" _QA_EXPECTED_LOSS = 3.98 _QA_TARGET_START_INDEX = 12 _QA_TARGET_END_INDEX = 13 # SequenceClassification docstring _CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "lordtt13/emo-mobilebert" _SEQ_CLASS_EXPECTED_OUTPUT = "'others'" _SEQ_CLASS_EXPECTED_LOSS = "4.72" MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = ["google/mobilebert-uncased"] def load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.replace("ffn_layer", "ffn") name = name.replace("FakeLayerNorm", "LayerNorm") name = name.replace("extra_output_weights", "dense/kernel") name = name.replace("bert", "mobilebert") name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class NoNorm(nn.Module): def __init__(self, feat_size, eps=None): super().__init__() self.bias = nn.Parameter(torch.zeros(feat_size)) self.weight = nn.Parameter(torch.ones(feat_size)) def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: return input_tensor * self.weight + self.bias NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm} class MobileBertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.trigram_input = config.trigram_input self.embedding_size = config.embedding_size self.hidden_size = config.hidden_size self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) embed_dim_multiplier = 3 if self.trigram_input else 1 embedded_input_size = self.embedding_size * embed_dim_multiplier self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if self.trigram_input: # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited # Devices (https://arxiv.org/abs/2004.02984) # # The embedding table in BERT models accounts for a substantial proportion of model size. To compress # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT. # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512 # dimensional output. inputs_embeds = torch.cat( [ nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0), inputs_embeds, nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0), ], dim=2, ) if self.trigram_input or self.embedding_size != self.hidden_size: inputs_embeds = self.embedding_transformation(inputs_embeds) # Add positional embeddings and token type embeddings, then layer # normalize and perform dropout. position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + position_embeddings + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class MobileBertSelfAttention(nn.Module): def __init__(self, config): super().__init__() self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.true_hidden_size, self.all_head_size) self.key = nn.Linear(config.true_hidden_size, self.all_head_size) self.value = nn.Linear( config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size ) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, query_tensor: torch.Tensor, key_tensor: torch.Tensor, value_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(query_tensor) mixed_key_layer = self.key(key_tensor) mixed_value_layer = self.value(value_tensor) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class MobileBertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.use_bottleneck = config.use_bottleneck self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps) if not self.use_bottleneck: self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor: layer_outputs = self.dense(hidden_states) if not self.use_bottleneck: layer_outputs = self.dropout(layer_outputs) layer_outputs = self.LayerNorm(layer_outputs + residual_tensor) return layer_outputs class MobileBertAttention(nn.Module): def __init__(self, config): super().__init__() self.self = MobileBertSelfAttention(config) self.output = MobileBertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, query_tensor: torch.Tensor, key_tensor: torch.Tensor, value_tensor: torch.Tensor, layer_input: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, ) -> Tuple[torch.Tensor]: self_outputs = self.self( query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, ) # Run a linear projection of `hidden_size` then add a residual # with `layer_input`. attention_output = self.output(self_outputs[0], layer_input) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class MobileBertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class OutputBottleneck(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.true_hidden_size, config.hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor: layer_outputs = self.dense(hidden_states) layer_outputs = self.dropout(layer_outputs) layer_outputs = self.LayerNorm(layer_outputs + residual_tensor) return layer_outputs class MobileBertOutput(nn.Module): def __init__(self, config): super().__init__() self.use_bottleneck = config.use_bottleneck self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size) if not self.use_bottleneck: self.dropout = nn.Dropout(config.hidden_dropout_prob) else: self.bottleneck = OutputBottleneck(config) def forward( self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor ) -> torch.Tensor: layer_output = self.dense(intermediate_states) if not self.use_bottleneck: layer_output = self.dropout(layer_output) layer_output = self.LayerNorm(layer_output + residual_tensor_1) else: layer_output = self.LayerNorm(layer_output + residual_tensor_1) layer_output = self.bottleneck(layer_output, residual_tensor_2) return layer_output class BottleneckLayer(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size) self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: layer_input = self.dense(hidden_states) layer_input = self.LayerNorm(layer_input) return layer_input class Bottleneck(nn.Module): def __init__(self, config): super().__init__() self.key_query_shared_bottleneck = config.key_query_shared_bottleneck self.use_bottleneck_attention = config.use_bottleneck_attention self.input = BottleneckLayer(config) if self.key_query_shared_bottleneck: self.attention = BottleneckLayer(config) def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]: # This method can return three different tuples of values. These different values make use of bottlenecks, # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory # usage. These linear layer have weights that are learned during training. # # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the # key, query, value, and "layer input" to be used by the attention layer. # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor # in the attention self output, after the attention scores have been computed. # # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return # four values, three of which have been passed through a bottleneck: the query and key, passed through the same # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck. # # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck, # and the residual layer will be this value passed through a bottleneck. bottlenecked_hidden_states = self.input(hidden_states) if self.use_bottleneck_attention: return (bottlenecked_hidden_states,) * 4 elif self.key_query_shared_bottleneck: shared_attention_input = self.attention(hidden_states) return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states) else: return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states) class FFNOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size) self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor: layer_outputs = self.dense(hidden_states) layer_outputs = self.LayerNorm(layer_outputs + residual_tensor) return layer_outputs class FFNLayer(nn.Module): def __init__(self, config): super().__init__() self.intermediate = MobileBertIntermediate(config) self.output = FFNOutput(config) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: intermediate_output = self.intermediate(hidden_states) layer_outputs = self.output(intermediate_output, hidden_states) return layer_outputs class MobileBertLayer(nn.Module): def __init__(self, config): super().__init__() self.use_bottleneck = config.use_bottleneck self.num_feedforward_networks = config.num_feedforward_networks self.attention = MobileBertAttention(config) self.intermediate = MobileBertIntermediate(config) self.output = MobileBertOutput(config) if self.use_bottleneck: self.bottleneck = Bottleneck(config) if config.num_feedforward_networks > 1: self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)]) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, ) -> Tuple[torch.Tensor]: if self.use_bottleneck: query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states) else: query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4 self_attention_outputs = self.attention( query_tensor, key_tensor, value_tensor, layer_input, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] s = (attention_output,) outputs = self_attention_outputs[1:] # add self attentions if we output attention weights if self.num_feedforward_networks != 1: for i, ffn_module in enumerate(self.ffn): attention_output = ffn_module(attention_output) s += (attention_output,) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output, hidden_states) outputs = ( (layer_output,) + outputs + ( torch.tensor(1000), query_tensor, key_tensor, value_tensor, layer_input, attention_output, intermediate_output, ) + s ) return outputs class MobileBertEncoder(nn.Module): def __init__(self, config): super().__init__() self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module( hidden_states, attention_mask, head_mask[i], output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class MobileBertPooler(nn.Module): def __init__(self, config): super().__init__() self.do_activate = config.classifier_activation if self.do_activate: self.dense = nn.Linear(config.hidden_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] if not self.do_activate: return first_token_tensor else: pooled_output = self.dense(first_token_tensor) pooled_output = torch.tanh(pooled_output) return pooled_output class MobileBertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class MobileBertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = MobileBertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False) self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.transform(hidden_states) hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0)) hidden_states += self.decoder.bias return hidden_states class MobileBertOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = MobileBertLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores class MobileBertPreTrainingHeads(nn.Module): def __init__(self, config): super().__init__() self.predictions = MobileBertLMPredictionHead(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class MobileBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileBertConfig pretrained_model_archive_map = MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST load_tf_weights = load_tf_weights_in_mobilebert base_model_prefix = "mobilebert" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, (nn.LayerNorm, NoNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) @dataclass class MobileBertForPreTrainingOutput(ModelOutput): """ Output type of [`MobileBertForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None prediction_logits: torch.FloatTensor = None seq_relationship_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None MOBILEBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MobileBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MOBILEBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top.", MOBILEBERT_START_DOCSTRING, ) class MobileBertModel(MobileBertPreTrainedModel): """ https://arxiv.org/pdf/2004.02984.pdf """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = MobileBertEmbeddings(config) self.encoder = MobileBertEncoder(config) self.pooler = MobileBertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForPreTraining(MobileBertPreTrainedModel): _keys_to_ignore_on_load_missing = [ "cls.predictions.decoder.weight", "cls.predictions.decoder.bias", "embeddings.position_ids", ] def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertPreTrainingHeads(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddigs): self.cls.predictions.decoder = new_embeddigs def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: # resize dense output embedings at first self.cls.predictions.dense = self._get_resized_lm_head( self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True ) return super().resize_token_embeddings(new_num_tokens=new_num_tokens) @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=MobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, next_sentence_label: Optional[torch.LongTensor] = None, output_attentions: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[torch.FloatTensor] = None, return_dict: Optional[torch.FloatTensor] = None, ) -> Union[Tuple, MobileBertForPreTrainingOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Examples: ```python >>> from transformers import MobileBertTokenizer, MobileBertForPreTraining >>> import torch >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased") >>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased") >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) >>> # Batch size 1 >>> outputs = model(input_ids) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return MobileBertForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING) class MobileBertForMaskedLM(MobileBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [ "cls.predictions.decoder.weight", "cls.predictions.decoder.bias", "embeddings.position_ids", ] def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config, add_pooling_layer=False) self.cls = MobileBertOnlyMLMHead(config) self.config = config # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddigs): self.cls.predictions.decoder = new_embeddigs def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: # resize dense output embedings at first self.cls.predictions.dense = self._get_resized_lm_head( self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True ) return super().resize_token_embeddings(new_num_tokens=new_num_tokens) @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, expected_output="'paris'", expected_loss=0.57, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class MobileBertOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output: torch.Tensor) -> torch.Tensor: seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score @add_start_docstrings( """MobileBert Model with a `next sentence prediction (classification)` head on top.""", MOBILEBERT_START_DOCSTRING, ) class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertOnlyNSPHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`. - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Examples: ```python >>> from transformers import MobileBertTokenizer, MobileBertForNextSentencePrediction >>> import torch >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased") >>> model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> loss = outputs.loss >>> logits = outputs.logits ```""" if "next_sentence_label" in kwargs: warnings.warn( "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" " `labels` instead.", FutureWarning, ) labels = kwargs.pop("next_sentence_label") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] seq_relationship_score = self.cls(pooled_output) next_sentence_loss = None if labels is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1)) if not return_dict: output = (seq_relationship_score,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing class MobileBertForSequenceClassification(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.mobilebert = MobileBertModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_SEQ_CLASS_EXPECTED_OUTPUT, expected_loss=_SEQ_CLASS_EXPECTED_LOSS, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering with Bert->MobileBert all-casing class MobileBertForQuestionAnswering(MobileBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_QA, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, qa_target_start_index=_QA_TARGET_START_INDEX, qa_target_end_index=_QA_TARGET_END_INDEX, expected_output=_QA_EXPECTED_OUTPUT, expected_loss=_QA_EXPECTED_LOSS, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice with Bert->MobileBert all-casing class MobileBertForMultipleChoice(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MobileBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, MOBILEBERT_START_DOCSTRING, ) # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification with Bert->MobileBert all-casing class MobileBertForTokenClassification(MobileBertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT, expected_loss=_TOKEN_CLASS_EXPECTED_LOSS, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/deformable_detr/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available _import_structure = { "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_deformable_detr"] = ["DeformableDetrFeatureExtractor"] try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_deformable_detr"] = [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available _import_structure = { "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_deformable_detr"] = ["DeformableDetrFeatureExtractor"] try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_deformable_detr"] = [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/generation/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available _import_structure = {} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["beam_constraints"] = [ "Constraint", "ConstraintListState", "DisjunctiveConstraint", "PhrasalConstraint", ] _import_structure["beam_search"] = [ "BeamHypotheses", "BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer", ] _import_structure["logits_process"] = [ "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitsProcessor", "LogitsProcessorList", "LogitsWarper", "MinLengthLogitsProcessor", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", "EncoderNoRepeatNGramLogitsProcessor", "ExponentialDecayLengthPenalty", "LogitNormalization", ] _import_structure["stopping_criteria"] = [ "MaxNewTokensCriteria", "MaxLengthCriteria", "MaxTimeCriteria", "StoppingCriteria", "StoppingCriteriaList", "validate_stopping_criteria", ] _import_structure["utils"] = [ "GenerationMixin", "top_k_top_p_filtering", "GreedySearchEncoderDecoderOutput", "GreedySearchDecoderOnlyOutput", "SampleEncoderDecoderOutput", "SampleDecoderOnlyOutput", "BeamSearchEncoderDecoderOutput", "BeamSearchDecoderOnlyOutput", "BeamSampleEncoderDecoderOutput", "BeamSampleDecoderOnlyOutput", "ContrastiveSearchEncoderDecoderOutput", "ContrastiveSearchDecoderOnlyOutput", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tf_logits_process"] = [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", "TFMinLengthLogitsProcessor", "TFNoBadWordsLogitsProcessor", "TFNoRepeatNGramLogitsProcessor", "TFRepetitionPenaltyLogitsProcessor", "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", "TFForceTokensLogitsProcessor", "TFSuppressTokensAtBeginLogitsProcessor", "TFSuppressTokensLogitsProcessor", ] _import_structure["tf_utils"] = [ "TFGenerationMixin", "tf_top_k_top_p_filtering", "TFGreedySearchDecoderOnlyOutput", "TFGreedySearchEncoderDecoderOutput", "TFSampleEncoderDecoderOutput", "TFSampleDecoderOnlyOutput", "TFBeamSearchEncoderDecoderOutput", "TFBeamSearchDecoderOnlyOutput", "TFBeamSampleEncoderDecoderOutput", "TFBeamSampleDecoderOnlyOutput", "TFContrastiveSearchEncoderDecoderOutput", "TFContrastiveSearchDecoderOnlyOutput", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["flax_logits_process"] = [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", "FlaxMinLengthLogitsProcessor", "FlaxTemperatureLogitsWarper", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", ] _import_structure["flax_utils"] = [ "FlaxGenerationMixin", "FlaxGreedySearchOutput", "FlaxSampleOutput", "FlaxBeamSearchOutput", ] if TYPE_CHECKING: try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer from .logits_process import ( EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessor, LogitsProcessorList, LogitsWarper, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, ) from .stopping_criteria import ( MaxLengthCriteria, MaxNewTokensCriteria, MaxTimeCriteria, StoppingCriteria, StoppingCriteriaList, validate_stopping_criteria, ) from .utils import ( BeamSampleDecoderOnlyOutput, BeamSampleEncoderDecoderOutput, BeamSearchDecoderOnlyOutput, BeamSearchEncoderDecoderOutput, ContrastiveSearchDecoderOnlyOutput, ContrastiveSearchEncoderDecoderOutput, GenerationMixin, GreedySearchDecoderOnlyOutput, GreedySearchEncoderDecoderOutput, SampleDecoderOnlyOutput, SampleEncoderDecoderOutput, top_k_top_p_filtering, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tf_logits_process import ( TFForcedBOSTokenLogitsProcessor, TFForcedEOSTokenLogitsProcessor, TFForceTokensLogitsProcessor, TFLogitsProcessor, TFLogitsProcessorList, TFLogitsWarper, TFMinLengthLogitsProcessor, TFNoBadWordsLogitsProcessor, TFNoRepeatNGramLogitsProcessor, TFRepetitionPenaltyLogitsProcessor, TFSuppressTokensAtBeginLogitsProcessor, TFSuppressTokensLogitsProcessor, TFTemperatureLogitsWarper, TFTopKLogitsWarper, TFTopPLogitsWarper, ) from .tf_utils import ( TFBeamSampleDecoderOnlyOutput, TFBeamSampleEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput, TFBeamSearchEncoderDecoderOutput, TFContrastiveSearchDecoderOnlyOutput, TFContrastiveSearchEncoderDecoderOutput, TFGenerationMixin, TFGreedySearchDecoderOnlyOutput, TFGreedySearchEncoderDecoderOutput, TFSampleDecoderOnlyOutput, TFSampleEncoderDecoderOutput, tf_top_k_top_p_filtering, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .flax_logits_process import ( FlaxForcedBOSTokenLogitsProcessor, FlaxForcedEOSTokenLogitsProcessor, FlaxLogitsProcessor, FlaxLogitsProcessorList, FlaxLogitsWarper, FlaxMinLengthLogitsProcessor, FlaxTemperatureLogitsWarper, FlaxTopKLogitsWarper, FlaxTopPLogitsWarper, ) from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available _import_structure = {} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["beam_constraints"] = [ "Constraint", "ConstraintListState", "DisjunctiveConstraint", "PhrasalConstraint", ] _import_structure["beam_search"] = [ "BeamHypotheses", "BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer", ] _import_structure["logits_process"] = [ "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitsProcessor", "LogitsProcessorList", "LogitsWarper", "MinLengthLogitsProcessor", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", "EncoderNoRepeatNGramLogitsProcessor", "ExponentialDecayLengthPenalty", "LogitNormalization", ] _import_structure["stopping_criteria"] = [ "MaxNewTokensCriteria", "MaxLengthCriteria", "MaxTimeCriteria", "StoppingCriteria", "StoppingCriteriaList", "validate_stopping_criteria", ] _import_structure["utils"] = [ "GenerationMixin", "top_k_top_p_filtering", "GreedySearchEncoderDecoderOutput", "GreedySearchDecoderOnlyOutput", "SampleEncoderDecoderOutput", "SampleDecoderOnlyOutput", "BeamSearchEncoderDecoderOutput", "BeamSearchDecoderOnlyOutput", "BeamSampleEncoderDecoderOutput", "BeamSampleDecoderOnlyOutput", "ContrastiveSearchEncoderDecoderOutput", "ContrastiveSearchDecoderOnlyOutput", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tf_logits_process"] = [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", "TFMinLengthLogitsProcessor", "TFNoBadWordsLogitsProcessor", "TFNoRepeatNGramLogitsProcessor", "TFRepetitionPenaltyLogitsProcessor", "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", "TFForceTokensLogitsProcessor", "TFSuppressTokensAtBeginLogitsProcessor", "TFSuppressTokensLogitsProcessor", ] _import_structure["tf_utils"] = [ "TFGenerationMixin", "tf_top_k_top_p_filtering", "TFGreedySearchDecoderOnlyOutput", "TFGreedySearchEncoderDecoderOutput", "TFSampleEncoderDecoderOutput", "TFSampleDecoderOnlyOutput", "TFBeamSearchEncoderDecoderOutput", "TFBeamSearchDecoderOnlyOutput", "TFBeamSampleEncoderDecoderOutput", "TFBeamSampleDecoderOnlyOutput", "TFContrastiveSearchEncoderDecoderOutput", "TFContrastiveSearchDecoderOnlyOutput", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["flax_logits_process"] = [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", "FlaxMinLengthLogitsProcessor", "FlaxTemperatureLogitsWarper", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", ] _import_structure["flax_utils"] = [ "FlaxGenerationMixin", "FlaxGreedySearchOutput", "FlaxSampleOutput", "FlaxBeamSearchOutput", ] if TYPE_CHECKING: try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer from .logits_process import ( EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessor, LogitsProcessorList, LogitsWarper, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, ) from .stopping_criteria import ( MaxLengthCriteria, MaxNewTokensCriteria, MaxTimeCriteria, StoppingCriteria, StoppingCriteriaList, validate_stopping_criteria, ) from .utils import ( BeamSampleDecoderOnlyOutput, BeamSampleEncoderDecoderOutput, BeamSearchDecoderOnlyOutput, BeamSearchEncoderDecoderOutput, ContrastiveSearchDecoderOnlyOutput, ContrastiveSearchEncoderDecoderOutput, GenerationMixin, GreedySearchDecoderOnlyOutput, GreedySearchEncoderDecoderOutput, SampleDecoderOnlyOutput, SampleEncoderDecoderOutput, top_k_top_p_filtering, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tf_logits_process import ( TFForcedBOSTokenLogitsProcessor, TFForcedEOSTokenLogitsProcessor, TFForceTokensLogitsProcessor, TFLogitsProcessor, TFLogitsProcessorList, TFLogitsWarper, TFMinLengthLogitsProcessor, TFNoBadWordsLogitsProcessor, TFNoRepeatNGramLogitsProcessor, TFRepetitionPenaltyLogitsProcessor, TFSuppressTokensAtBeginLogitsProcessor, TFSuppressTokensLogitsProcessor, TFTemperatureLogitsWarper, TFTopKLogitsWarper, TFTopPLogitsWarper, ) from .tf_utils import ( TFBeamSampleDecoderOnlyOutput, TFBeamSampleEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput, TFBeamSearchEncoderDecoderOutput, TFContrastiveSearchDecoderOnlyOutput, TFContrastiveSearchEncoderDecoderOutput, TFGenerationMixin, TFGreedySearchDecoderOnlyOutput, TFGreedySearchEncoderDecoderOutput, TFSampleDecoderOnlyOutput, TFSampleEncoderDecoderOutput, tf_top_k_top_p_filtering, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .flax_logits_process import ( FlaxForcedBOSTokenLogitsProcessor, FlaxForcedEOSTokenLogitsProcessor, FlaxLogitsProcessor, FlaxLogitsProcessorList, FlaxLogitsWarper, FlaxMinLengthLogitsProcessor, FlaxTemperatureLogitsWarper, FlaxTopKLogitsWarper, FlaxTopPLogitsWarper, ) from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/wav2vec2/tokenization_wav2vec2.py

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Wav2Vec2.""" import json import os import sys import warnings from dataclasses import dataclass from itertools import groupby from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list from ...tokenization_utils_base import AddedToken, BatchEncoding from ...utils import ( ModelOutput, PaddingStrategy, TensorType, add_end_docstrings, is_flax_available, is_tf_available, is_torch_available, logging, to_py_obj, ) logger = logging.get_logger(__name__) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp # noqa: F401 VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json", }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer_config.json" ), }, } # Wav2Vec2 has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-base-960h": sys.maxsize} WAV2VEC2_KWARGS_DOCSTRING = r""" padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ ListOfDict = List[Dict[str, Union[int, str]]] @dataclass class Wav2Vec2CTCTokenizerOutput(ModelOutput): """ Output type of [` Wav2Vec2CTCTokenizer`], with transcription. Args: text (list of `str` or `str`): Decoded logits in text from. Usually the speech transcription. char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with produced text. word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets can be used to compute time stamps for each word. """ text: Union[List[str], str] char_offsets: Union[List[ListOfDict], ListOfDict] = None word_offsets: Union[List[ListOfDict], ListOfDict] = None class Wav2Vec2CTCTokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2CTC tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to accept lowercase input and lowercase the output when decoding. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", replace_word_delimiter_char=" ", do_lower_case=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, word_delimiter_token=word_delimiter_token, replace_word_delimiter_char=replace_word_delimiter_char, **kwargs, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.replace_word_delimiter_char = replace_word_delimiter_char with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} # make sure that tokens made of several # characters are not split at tokenization for token in self.encoder.keys(): if len(token) > 1: self.unique_no_split_tokens.append(token) self._create_trie(self.unique_no_split_tokens) @property def word_delimiter_token(self) -> str: """ `str`: Word delimiter token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _tokenize(self, text, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. """ if self.do_lower_case: text = text.upper() return list(text.replace(" ", self.word_delimiter_token)) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string( self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_char_offsets: bool = False, output_word_offsets: bool = False, ) -> Dict[str, Union[str, float]]: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ if len(tokens) == 0: return {"text": "", "char_offsets": [], "word_offsets": []} # group same tokens into non-repeating tokens in CTC style decoding if group_tokens: chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens))) else: chars = tokens char_repetitions = len(tokens) * [1] # filter self.pad_token which is used as CTC-blank token processed_chars = list(filter(lambda char: char != self.pad_token, chars)) # replace delimiter token processed_chars = [ self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars ] # retrieve offsets char_offsets = word_offsets = None if output_char_offsets or output_word_offsets: char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token) if len(char_offsets) != len(processed_chars): raise ValueError( f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}" " have to be of the same length, but are: " f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" f" {len(processed_chars)}" ) # set tokens to correct processed token for i, char in enumerate(processed_chars): char_offsets[i]["char"] = char # retrieve word offsets from character offsets word_offsets = None if output_word_offsets: word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char) # don't output chars if not set to True if not output_char_offsets: char_offsets = None # join to string join_char = " " if spaces_between_special_tokens else "" string = join_char.join(processed_chars).strip() if self.do_lower_case: string = string.lower() return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets} @staticmethod def _compute_offsets( char_repetitions: List[int], chars: List[str], ctc_token: int ) -> List[Dict[str, Union[str, int]]]: end_indices = np.asarray(char_repetitions).cumsum() start_indices = np.concatenate(([0], end_indices[:-1])) offsets = [ {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices) ] # filter out CTC token offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) return offsets @staticmethod def _get_word_offsets( offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " ) -> Dict[str, Union[str, float]]: word_offsets = [] last_state = "SPACE" word = "" start_offset = 0 end_offset = 0 for i, offset in enumerate(offsets): char = offset["char"] state = "SPACE" if char == word_delimiter_char else "WORD" if state == last_state: # If we are in the same state as before, we simply repeat what we've done before end_offset = offset["end_offset"] word += char else: # Switching state if state == "SPACE": # Finishing a word word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) else: # Starting a new word start_offset = offset["start_offset"] end_offset = offset["end_offset"] word = char last_state = state if last_state == "WORD": word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) return word_offsets def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): if is_split_into_words: text = " " + text return (text, kwargs) def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_word_offsets: Optional[bool] = False, output_char_offsets: Optional[bool] = False, ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) string_output = self.convert_tokens_to_string( result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens, output_word_offsets=output_word_offsets, output_char_offsets=output_char_offsets, ) text = string_output["text"] if clean_up_tokenization_spaces: text = self.clean_up_tokenization(text) if output_word_offsets or output_char_offsets: return Wav2Vec2CTCTokenizerOutput( text=text, char_offsets=string_output["char_offsets"], word_offsets=string_output["word_offsets"], ) else: return text # overwritten from `tokenization_utils_base.py` because tokenizer can output # `ModelOutput` which should not be a list for batched output and # because we need docs for `output_char_offsets` here def batch_decode( self, sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> List[str]: """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. """ batch_decoded = [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) for seq in sequences ] if output_char_offsets or output_word_offsets: # transform list of dicts to dict of lists return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]}) return batch_decoded # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` # and `output_word_offsets` here def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the example below to better understand how to make use of `output_char_offsets`. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the example below to better understand how to make use of `output_word_offsets`. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. Example: ```python >>> # Let's see how to retrieve time steps for a model >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC >>> from datasets import load_dataset >>> import datasets >>> import torch >>> # import model, feature extractor, tokenizer >>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") >>> # load first sample of English common_voice >>> dataset = load_dataset("common_voice", "en", split="train", streaming=True) >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000)) >>> dataset_iter = iter(dataset) >>> sample = next(dataset_iter) >>> # forward sample through model to get greedily predicted transcription ids >>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values >>> logits = model(input_values).logits[0] >>> pred_ids = torch.argmax(logits, axis=-1) >>> # retrieve word stamps (analogous commands for `output_char_offsets`) >>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True) >>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate >>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate >>> word_offsets = [ ... { ... "word": d["word"], ... "start_time": round(d["start_offset"] * time_offset, 2), ... "end_time": round(d["end_offset"] * time_offset, 2), ... } ... for d in outputs.word_offsets ... ] >>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer: >>> # https://huggingface.co/datasets/common_voice/viewer/en/train >>> word_offsets[:3] [{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES', 'start_time': 1.64, 'end_time': 1.9}, {'word': 'MILISANDRA', 'start_time': 2.26, 'end_time': 2.9}] ```""" # Convert inputs to python lists token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,) def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: """ Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary. Args: new_tokens (`List[str]`or `List[tokenizers.AddedToken]`): Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by checking if the tokenizer assign the index of the `unk_token` to them). special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the tokens should be added as special tokens. Returns: `int`: The number of tokens actually added to the vocabulary. Example: ```python # Let's see how to increase the vocabulary of Bert model and tokenizer tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"]) print("We have added", num_added_toks, "tokens") # Note: resize_token_embeddings expects to receive the full size of the new vocabulary, i.e. the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) ```""" new_tokens = [str(tok) for tok in new_tokens] tokens_to_add = [] for token in new_tokens: assert isinstance(token, str) if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case: token = token.lower() if ( token != self.unk_token and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and token not in tokens_to_add ): tokens_to_add.append(token) if self.verbose: logger.info(f"Adding {token} to the vocabulary") added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add)) added_tok_decoder = {v: k for k, v in added_tok_encoder.items()} self.added_tokens_encoder.update(added_tok_encoder) self.added_tokens_decoder.update(added_tok_decoder) # Make sure we don't split on any special tokens (even they were already in the vocab before) for token in tokens_to_add: if len(token) > 1: self._additional_special_tokens.append(AddedToken(token)) _insert_one_token_to_ordered_list(self.unique_no_split_tokens, token) self._create_trie(self.unique_no_split_tokens) return len(tokens_to_add) class Wav2Vec2Tokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2 tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the output when decoding. do_normalize (`bool`, *optional*, defaults to `False`): Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, *e.g.*, [wav2vec2-lv60](https://huggingface.co/models?search=lv60). return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`. <Tip> Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` should be passed. For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be passed for batched inference. </Tip> **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json" }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json" ), }, } model_input_names = ["input_values", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", do_lower_case=False, do_normalize=False, return_attention_mask=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, do_normalize=do_normalize, return_attention_mask=return_attention_mask, word_delimiter_token=word_delimiter_token, **kwargs, ) warnings.warn( "The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use" " `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.", FutureWarning, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} @property def word_delimiter_token(self) -> str: """ `str`: Padding token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING) def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences. Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrayr or a list of list of float values. """ is_batched = bool( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list))) ) # make sure input is in list format if is_batched and not isinstance(raw_speech[0], np.ndarray): raw_speech = [np.asarray(speech) for speech in raw_speech] elif not is_batched and not isinstance(raw_speech, np.ndarray): raw_speech = np.asarray(raw_speech) # always return batch if not is_batched: raw_speech = [raw_speech] # zero-mean and unit-variance normalization if self.do_normalize: raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech] # convert into correct format for padding encoded_inputs = BatchEncoding({"input_values": raw_speech}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=self.return_attention_mask, return_tensors=return_tensors, verbose=verbose, ) return padded_inputs @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ # group same tokens into non-repeating tokens in CTC style decoding grouped_tokens = [token_group[0] for token_group in groupby(tokens)] # filter self.pad_token which is used as CTC-blank token filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens)) # replace delimiter token string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip() if self.do_lower_case: string = string.lower() return string def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) text = self.convert_tokens_to_string(result) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,)

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Wav2Vec2.""" import json import os import sys import warnings from dataclasses import dataclass from itertools import groupby from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list from ...tokenization_utils_base import AddedToken, BatchEncoding from ...utils import ( ModelOutput, PaddingStrategy, TensorType, add_end_docstrings, is_flax_available, is_tf_available, is_torch_available, logging, to_py_obj, ) logger = logging.get_logger(__name__) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp # noqa: F401 VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json", }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer_config.json" ), }, } # Wav2Vec2 has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-base-960h": sys.maxsize} WAV2VEC2_KWARGS_DOCSTRING = r""" padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ ListOfDict = List[Dict[str, Union[int, str]]] @dataclass class Wav2Vec2CTCTokenizerOutput(ModelOutput): """ Output type of [` Wav2Vec2CTCTokenizer`], with transcription. Args: text (list of `str` or `str`): Decoded logits in text from. Usually the speech transcription. char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with produced text. word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets can be used to compute time stamps for each word. """ text: Union[List[str], str] char_offsets: Union[List[ListOfDict], ListOfDict] = None word_offsets: Union[List[ListOfDict], ListOfDict] = None class Wav2Vec2CTCTokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2CTC tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to accept lowercase input and lowercase the output when decoding. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", replace_word_delimiter_char=" ", do_lower_case=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, word_delimiter_token=word_delimiter_token, replace_word_delimiter_char=replace_word_delimiter_char, **kwargs, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.replace_word_delimiter_char = replace_word_delimiter_char with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} # make sure that tokens made of several # characters are not split at tokenization for token in self.encoder.keys(): if len(token) > 1: self.unique_no_split_tokens.append(token) self._create_trie(self.unique_no_split_tokens) @property def word_delimiter_token(self) -> str: """ `str`: Word delimiter token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _tokenize(self, text, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. """ if self.do_lower_case: text = text.upper() return list(text.replace(" ", self.word_delimiter_token)) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string( self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_char_offsets: bool = False, output_word_offsets: bool = False, ) -> Dict[str, Union[str, float]]: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ if len(tokens) == 0: return {"text": "", "char_offsets": [], "word_offsets": []} # group same tokens into non-repeating tokens in CTC style decoding if group_tokens: chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens))) else: chars = tokens char_repetitions = len(tokens) * [1] # filter self.pad_token which is used as CTC-blank token processed_chars = list(filter(lambda char: char != self.pad_token, chars)) # replace delimiter token processed_chars = [ self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars ] # retrieve offsets char_offsets = word_offsets = None if output_char_offsets or output_word_offsets: char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token) if len(char_offsets) != len(processed_chars): raise ValueError( f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}" " have to be of the same length, but are: " f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" f" {len(processed_chars)}" ) # set tokens to correct processed token for i, char in enumerate(processed_chars): char_offsets[i]["char"] = char # retrieve word offsets from character offsets word_offsets = None if output_word_offsets: word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char) # don't output chars if not set to True if not output_char_offsets: char_offsets = None # join to string join_char = " " if spaces_between_special_tokens else "" string = join_char.join(processed_chars).strip() if self.do_lower_case: string = string.lower() return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets} @staticmethod def _compute_offsets( char_repetitions: List[int], chars: List[str], ctc_token: int ) -> List[Dict[str, Union[str, int]]]: end_indices = np.asarray(char_repetitions).cumsum() start_indices = np.concatenate(([0], end_indices[:-1])) offsets = [ {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices) ] # filter out CTC token offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) return offsets @staticmethod def _get_word_offsets( offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " ) -> Dict[str, Union[str, float]]: word_offsets = [] last_state = "SPACE" word = "" start_offset = 0 end_offset = 0 for i, offset in enumerate(offsets): char = offset["char"] state = "SPACE" if char == word_delimiter_char else "WORD" if state == last_state: # If we are in the same state as before, we simply repeat what we've done before end_offset = offset["end_offset"] word += char else: # Switching state if state == "SPACE": # Finishing a word word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) else: # Starting a new word start_offset = offset["start_offset"] end_offset = offset["end_offset"] word = char last_state = state if last_state == "WORD": word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) return word_offsets def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): if is_split_into_words: text = " " + text return (text, kwargs) def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_word_offsets: Optional[bool] = False, output_char_offsets: Optional[bool] = False, ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) string_output = self.convert_tokens_to_string( result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens, output_word_offsets=output_word_offsets, output_char_offsets=output_char_offsets, ) text = string_output["text"] if clean_up_tokenization_spaces: text = self.clean_up_tokenization(text) if output_word_offsets or output_char_offsets: return Wav2Vec2CTCTokenizerOutput( text=text, char_offsets=string_output["char_offsets"], word_offsets=string_output["word_offsets"], ) else: return text # overwritten from `tokenization_utils_base.py` because tokenizer can output # `ModelOutput` which should not be a list for batched output and # because we need docs for `output_char_offsets` here def batch_decode( self, sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> List[str]: """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. """ batch_decoded = [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) for seq in sequences ] if output_char_offsets or output_word_offsets: # transform list of dicts to dict of lists return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]}) return batch_decoded # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` # and `output_word_offsets` here def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the example below to better understand how to make use of `output_char_offsets`. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the example below to better understand how to make use of `output_word_offsets`. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. Example: ```python >>> # Let's see how to retrieve time steps for a model >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC >>> from datasets import load_dataset >>> import datasets >>> import torch >>> # import model, feature extractor, tokenizer >>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") >>> # load first sample of English common_voice >>> dataset = load_dataset("common_voice", "en", split="train", streaming=True) >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000)) >>> dataset_iter = iter(dataset) >>> sample = next(dataset_iter) >>> # forward sample through model to get greedily predicted transcription ids >>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values >>> logits = model(input_values).logits[0] >>> pred_ids = torch.argmax(logits, axis=-1) >>> # retrieve word stamps (analogous commands for `output_char_offsets`) >>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True) >>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate >>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate >>> word_offsets = [ ... { ... "word": d["word"], ... "start_time": round(d["start_offset"] * time_offset, 2), ... "end_time": round(d["end_offset"] * time_offset, 2), ... } ... for d in outputs.word_offsets ... ] >>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer: >>> # https://huggingface.co/datasets/common_voice/viewer/en/train >>> word_offsets[:3] [{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES', 'start_time': 1.64, 'end_time': 1.9}, {'word': 'MILISANDRA', 'start_time': 2.26, 'end_time': 2.9}] ```""" # Convert inputs to python lists token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,) def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: """ Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary. Args: new_tokens (`List[str]`or `List[tokenizers.AddedToken]`): Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by checking if the tokenizer assign the index of the `unk_token` to them). special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the tokens should be added as special tokens. Returns: `int`: The number of tokens actually added to the vocabulary. Example: ```python # Let's see how to increase the vocabulary of Bert model and tokenizer tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"]) print("We have added", num_added_toks, "tokens") # Note: resize_token_embeddings expects to receive the full size of the new vocabulary, i.e. the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) ```""" new_tokens = [str(tok) for tok in new_tokens] tokens_to_add = [] for token in new_tokens: assert isinstance(token, str) if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case: token = token.lower() if ( token != self.unk_token and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and token not in tokens_to_add ): tokens_to_add.append(token) if self.verbose: logger.info(f"Adding {token} to the vocabulary") added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add)) added_tok_decoder = {v: k for k, v in added_tok_encoder.items()} self.added_tokens_encoder.update(added_tok_encoder) self.added_tokens_decoder.update(added_tok_decoder) # Make sure we don't split on any special tokens (even they were already in the vocab before) for token in tokens_to_add: if len(token) > 1: self._additional_special_tokens.append(AddedToken(token)) _insert_one_token_to_ordered_list(self.unique_no_split_tokens, token) self._create_trie(self.unique_no_split_tokens) return len(tokens_to_add) class Wav2Vec2Tokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2 tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the output when decoding. do_normalize (`bool`, *optional*, defaults to `False`): Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, *e.g.*, [wav2vec2-lv60](https://huggingface.co/models?search=lv60). return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`. <Tip> Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` should be passed. For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be passed for batched inference. </Tip> **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json" }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json" ), }, } model_input_names = ["input_values", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", do_lower_case=False, do_normalize=False, return_attention_mask=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, do_normalize=do_normalize, return_attention_mask=return_attention_mask, word_delimiter_token=word_delimiter_token, **kwargs, ) warnings.warn( "The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use" " `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.", FutureWarning, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} @property def word_delimiter_token(self) -> str: """ `str`: Padding token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING) def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences. Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrayr or a list of list of float values. """ is_batched = bool( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list))) ) # make sure input is in list format if is_batched and not isinstance(raw_speech[0], np.ndarray): raw_speech = [np.asarray(speech) for speech in raw_speech] elif not is_batched and not isinstance(raw_speech, np.ndarray): raw_speech = np.asarray(raw_speech) # always return batch if not is_batched: raw_speech = [raw_speech] # zero-mean and unit-variance normalization if self.do_normalize: raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech] # convert into correct format for padding encoded_inputs = BatchEncoding({"input_values": raw_speech}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=self.return_attention_mask, return_tensors=return_tensors, verbose=verbose, ) return padded_inputs @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ # group same tokens into non-repeating tokens in CTC style decoding grouped_tokens = [token_group[0] for token_group in groupby(tokens)] # filter self.pad_token which is used as CTC-blank token filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens)) # replace delimiter token string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip() if self.do_lower_case: string = string.lower() return string def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) text = self.convert_tokens_to_string(result) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/vit_mae/test_modeling_vit_mae.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ViTMAE model. """ import inspect import math import tempfile import unittest import numpy as np from transformers import ViTMAEConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import ViTMAEForPreTraining, ViTMAEModel from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import ViTFeatureExtractor class ViTMAEModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, mask_ratio=0.6, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.mask_ratio = mask_ratio self.scope = scope # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above # (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1))) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return ViTMAEConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, ) def create_and_check_model(self, config, pixel_values, labels): model = ViTMAEModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_pretraining(self, config, pixel_values, labels): model = ViTMAEForPreTraining(config) model.to(torch_device) model.eval() result = model(pixel_values) num_patches = (self.image_size // self.patch_size) ** 2 expected_num_channels = self.patch_size**2 * self.num_channels self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) # test greyscale images config.num_channels = 1 model = ViTMAEForPreTraining(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) expected_num_channels = self.patch_size**2 self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ViTMAEModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else () test_pruning = False test_torchscript = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = ViTMAEModelTester(self) self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ViTMAE does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) # overwrite from common since ViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict): # make masks reproducible np.random.seed(2) num_patches = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) pt_noise = torch.from_numpy(noise) # Add `noise` argument. # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument pt_inputs_dict["noise"] = pt_noise super().check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() # make random mask reproducible torch.manual_seed(2) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) out_2 = outputs[0].cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) # make random mask reproducible torch.manual_seed(2) with torch.no_grad(): after_outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # Make sure we don't have nans out_1 = after_outputs[0].cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_determinism(self): pass @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_save_load_fast_init_from_base(self): pass @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_save_load_fast_init_to_base(self): pass @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""") def test_model_outputs_equivalence(self): pass @slow def test_model_from_pretrained(self): for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ViTMAEModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ViTMAEModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None @slow def test_inference_for_pretraining(self): # make random mask reproducible across the PT and TF model np.random.seed(2) model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # prepare a noise vector that will be also used for testing the TF model # (this way we can ensure that the PT and TF models operate on the same inputs) vit_mae_config = ViTMAEConfig() num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2) noise = np.random.uniform(size=(1, num_patches)) # forward pass with torch.no_grad(): outputs = model(**inputs, noise=torch.from_numpy(noise).to(device=torch_device)) # verify the logits expected_shape = torch.Size((1, 196, 768)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] ) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice.to(torch_device), atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ViTMAE model. """ import inspect import math import tempfile import unittest import numpy as np from transformers import ViTMAEConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import ViTMAEForPreTraining, ViTMAEModel from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import ViTFeatureExtractor class ViTMAEModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, mask_ratio=0.6, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.mask_ratio = mask_ratio self.scope = scope # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above # (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1))) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return ViTMAEConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, ) def create_and_check_model(self, config, pixel_values, labels): model = ViTMAEModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_pretraining(self, config, pixel_values, labels): model = ViTMAEForPreTraining(config) model.to(torch_device) model.eval() result = model(pixel_values) num_patches = (self.image_size // self.patch_size) ** 2 expected_num_channels = self.patch_size**2 * self.num_channels self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) # test greyscale images config.num_channels = 1 model = ViTMAEForPreTraining(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) expected_num_channels = self.patch_size**2 self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ViTMAEModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else () test_pruning = False test_torchscript = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = ViTMAEModelTester(self) self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ViTMAE does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) # overwrite from common since ViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict): # make masks reproducible np.random.seed(2) num_patches = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) pt_noise = torch.from_numpy(noise) # Add `noise` argument. # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument pt_inputs_dict["noise"] = pt_noise super().check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() # make random mask reproducible torch.manual_seed(2) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) out_2 = outputs[0].cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) # make random mask reproducible torch.manual_seed(2) with torch.no_grad(): after_outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # Make sure we don't have nans out_1 = after_outputs[0].cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_determinism(self): pass @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_save_load_fast_init_from_base(self): pass @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_save_load_fast_init_to_base(self): pass @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""") def test_model_outputs_equivalence(self): pass @slow def test_model_from_pretrained(self): for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ViTMAEModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ViTMAEModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None @slow def test_inference_for_pretraining(self): # make random mask reproducible across the PT and TF model np.random.seed(2) model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # prepare a noise vector that will be also used for testing the TF model # (this way we can ensure that the PT and TF models operate on the same inputs) vit_mae_config = ViTMAEConfig() num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2) noise = np.random.uniform(size=(1, num_patches)) # forward pass with torch.no_grad(): outputs = model(**inputs, noise=torch.from_numpy(noise).to(device=torch_device)) # verify the logits expected_shape = torch.Size((1, 196, 768)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] ) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice.to(torch_device), atol=1e-4))

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/big_bird/tokenization_big_bird.py

# coding=utf-8 # Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for BigBird.""" import os import re from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model", "google/bigbird-roberta-large": ( "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model" ), "google/bigbird-base-trivia-itc": ( "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "google/bigbird-roberta-base": 4096, "google/bigbird-roberta-large": 4096, "google/bigbird-base-trivia-itc": 4096, } class BigBirdTokenizer(PreTrainedTokenizer): """ Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. bos_token (`str`, *optional*, defaults to `"<s>"`): The begin of sequence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] def __init__( self, vocab_file, unk_token="<unk>", bos_token="<s>", eos_token="</s>", pad_token="<pad>", sep_token="[SEP]", mask_token="[MASK]", cls_token="[CLS]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs ) -> None: bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, mask_token=mask_token, cls_token=cls_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) @property def vocab_size(self): return self.sp_model.get_piece_size() def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def _tokenize(self, text: str) -> List[str]: """Take as input a string and return a list of strings (tokens) for words/sub-words""" return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" token = self.sp_model.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" prev_is_special = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, spaces_between_special_tokens: bool = True, **kwargs ) -> str: self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False) filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) # To avoid mixing byte-level and unicode for byte-level BPT # we need to build string separately for added tokens and byte-level tokens # cf. https://github.com/huggingface/transformers/issues/1133 sub_texts = [] current_sub_text = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue if token in self.added_tokens_encoder: if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) current_sub_text = [] sub_texts.append(token) else: current_sub_text.append(token) if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) # Mimic the behavior of the Rust tokenizer: # No space before [MASK] and [SEP] if spaces_between_special_tokens: text = re.sub(r" (\[(MASK|SEP)\])", r"\1", " ".join(sub_texts)) else: text = "".join(sub_texts) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Big Bird sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

# coding=utf-8 # Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for BigBird.""" import os import re from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model", "google/bigbird-roberta-large": ( "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model" ), "google/bigbird-base-trivia-itc": ( "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "google/bigbird-roberta-base": 4096, "google/bigbird-roberta-large": 4096, "google/bigbird-base-trivia-itc": 4096, } class BigBirdTokenizer(PreTrainedTokenizer): """ Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. bos_token (`str`, *optional*, defaults to `"<s>"`): The begin of sequence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] def __init__( self, vocab_file, unk_token="<unk>", bos_token="<s>", eos_token="</s>", pad_token="<pad>", sep_token="[SEP]", mask_token="[MASK]", cls_token="[CLS]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs ) -> None: bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, mask_token=mask_token, cls_token=cls_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) @property def vocab_size(self): return self.sp_model.get_piece_size() def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def _tokenize(self, text: str) -> List[str]: """Take as input a string and return a list of strings (tokens) for words/sub-words""" return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" token = self.sp_model.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" prev_is_special = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, spaces_between_special_tokens: bool = True, **kwargs ) -> str: self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False) filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) # To avoid mixing byte-level and unicode for byte-level BPT # we need to build string separately for added tokens and byte-level tokens # cf. https://github.com/huggingface/transformers/issues/1133 sub_texts = [] current_sub_text = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue if token in self.added_tokens_encoder: if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) current_sub_text = [] sub_texts.append(token) else: current_sub_text.append(token) if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) # Mimic the behavior of the Rust tokenizer: # No space before [MASK] and [SEP] if spaces_between_special_tokens: text = re.sub(r" (\[(MASK|SEP)\])", r"\1", " ".join(sub_texts)) else: text = "".join(sub_texts) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Big Bird sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./docs/source/it/model_sharing.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Condividi un modello Gli ultimi due tutorial ti hanno mostrato come puoi fare fine-tuning di un modello con PyTorch, Keras e 🤗 Accelerate per configurazioni distribuite. Il prossimo passo è quello di condividere il tuo modello con la community! In Hugging Face, crediamo nella condivisione della conoscenza e delle risorse in modo da democratizzare l'intelligenza artificiale per chiunque. Ti incoraggiamo a considerare di condividere il tuo modello con la community per aiutare altre persone a risparmiare tempo e risorse. In questo tutorial, imparerai due metodi per la condivisione di un modello trained o fine-tuned nel [Model Hub](https://huggingface.co/models): - Condividi in modo programmatico i tuoi file nell'Hub. - Trascina i tuoi file nell'Hub mediante interfaccia grafica. <iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <Tip> Per condividere un modello con la community, hai bisogno di un account su [huggingface.co](https://huggingface.co/join). Puoi anche unirti ad un'organizzazione esistente o crearne una nuova. </Tip> ## Caratteristiche dei repository Ogni repository nel Model Hub si comporta come un tipico repository di GitHub. I nostri repository offrono il versionamento, la cronologia dei commit, e la possibilità di visualizzare le differenze. Il versionamento all'interno del Model Hub è basato su git e [git-lfs](https://git-lfs.github.com/). In altre parole, puoi trattare un modello come un unico repository, consentendo un maggiore controllo degli accessi e maggiore scalabilità. Il controllo delle versioni consente *revisions*, un metodo per appuntare una versione specifica di un modello con un hash di commit, un tag o un branch. Come risultato, puoi caricare una specifica versione di un modello con il parametro `revision`: ```py >>> model = AutoModel.from_pretrained( ... "julien-c/EsperBERTo-small", revision="v2.0.1" # nome di un tag, di un branch, o commit hash ... ) ``` Anche i file possono essere modificati facilmente in un repository ed è possibile visualizzare la cronologia dei commit e le differenze:  ## Configurazione Prima di condividere un modello nell'Hub, hai bisogno delle tue credenziali di Hugging Face. Se hai accesso ad un terminale, esegui il seguente comando nell'ambiente virtuale in cui è installata la libreria 🤗 Transformers. Questo memorizzerà il tuo token di accesso nella cartella cache di Hugging Face (di default `~/.cache/`): ```bash huggingface-cli login ``` Se stai usando un notebook come Jupyter o Colaboratory, assicurati di avere la libreria [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) installata. Questa libreria ti permette di interagire in maniera programmatica con l'Hub. ```bash pip install huggingface_hub ``` Utilizza `notebook_login` per accedere all'Hub, e segui il link [qui](https://huggingface.co/settings/token) per generare un token con cui effettuare il login: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Converti un modello per tutti i framework Per assicurarti che il tuo modello possa essere utilizzato da persone che lavorano con un framework differente, ti raccomandiamo di convertire e caricare il tuo modello sia con i checkpoint di PyTorch che con quelli di TensorFlow. Anche se è possibile caricare il modello da un framework diverso, se si salta questo passaggio, il caricamento sarà più lento perché 🤗 Transformers ha bisogno di convertire i checkpoint al momento. Convertire un checkpoint per un altro framework è semplice. Assicurati di avere PyTorch e TensorFlow installati (vedi [qui](installation) per le istruzioni d'installazione), e poi trova il modello specifico per il tuo compito nell'altro framework. <frameworkcontent> <pt> Specifica `from_tf=True` per convertire un checkpoint da TensorFlow a PyTorch: ```py >>> pt_model = DistilBertForSequenceClassification.from_pretrained( ... "path/verso/il-nome-magnifico-che-hai-scelto", from_tf=True ... ) >>> pt_model.save_pretrained("path/verso/il-nome-magnifico-che-hai-scelto") ``` </pt> <tf> Specifica `from_pt=True` per convertire un checkpoint da PyTorch a TensorFlow: ```py >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained( ... "path/verso/il-nome-magnifico-che-hai-scelto", from_pt=True ... ) ``` Poi puoi salvare il tuo nuovo modello in TensorFlow con il suo nuovo checkpoint: ```py >>> tf_model.save_pretrained("path/verso/il-nome-magnifico-che-hai-scelto") ``` </tf> <jax> Se un modello è disponibile in Flax, puoi anche convertire un checkpoint da PyTorch a Flax: ```py >>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( ... "path/verso/il-nome-magnifico-che-hai-scelto", from_pt=True ... ) ``` </jax> </frameworkcontent> ## Condividi un modello durante il training <frameworkcontent> <pt> <Youtube id="Z1-XMy-GNLQ"/> Condividere un modello nell'Hub è tanto semplice quanto aggiungere un parametro extra o un callback. Ricorda dal [tutorial sul fine-tuning](training), la classe [`TrainingArguments`] è dove specifichi gli iperparametri e le opzioni addizionali per l'allenamento. Una di queste opzioni di training include l'abilità di condividere direttamente un modello nell'Hub. Imposta `push_to_hub=True` in [`TrainingArguments`]: ```py >>> training_args = TrainingArguments(output_dir="il-mio-bellissimo-modello", push_to_hub=True) ``` Passa gli argomenti per il training come di consueto al [`Trainer`]: ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` Dopo aver effettuato il fine-tuning del tuo modello, chiama [`~transformers.Trainer.push_to_hub`] sul [`Trainer`] per condividere il modello allenato nell'Hub. 🤗 Transformers aggiungerà in modo automatico persino gli iperparametri, i risultati del training e le versioni del framework alla scheda del tuo modello (model card, in inglese)! ```py >>> trainer.push_to_hub() ``` </pt> <tf> Condividi un modello nell'Hub con [`PushToHubCallback`]. Nella funzione [`PushToHubCallback`], aggiungi: - Una directory di output per il tuo modello. - Un tokenizer. - L'`hub_model_id`, che è il tuo username sull'Hub e il nome del modello. ```py >>> from transformers.keras.callbacks import PushToHubCallback >>> push_to_hub_callback = PushToHubCallback( ... output_dir="./il_path_dove_salvare_il_tuo_modello", ... tokenizer=tokenizer, ... hub_model_id="il-tuo-username/il-mio-bellissimo-modello", ... ) ``` Aggiungi il callback a [`fit`](https://keras.io/api/models/model_training_apis/), e 🤗 Transformers caricherà il modello allenato nell'Hub: ```py >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) ``` </tf> </frameworkcontent> ## Utilizzare la funzione `push_to_hub` Puoi anche chiamare `push_to_hub` direttamente sul tuo modello per caricarlo nell'Hub. Specifica il nome del tuo modello in `push_to_hub`: ```py >>> pt_model.push_to_hub("il-mio-bellissimo-modello") ``` Questo crea un repository sotto il proprio username con il nome del modello `il-mio-bellissimo-modello`. Ora chiunque può caricare il tuo modello con la funzione `from_pretrained`: ```py >>> from transformers import AutoModel >>> model = AutoModel.from_pretrained("il-tuo-username/il-mio-bellissimo-modello") ``` Se fai parte di un'organizzazione e vuoi invece condividere un modello sotto il nome dell'organizzazione, aggiungi il parametro `organization`: ```py >>> pt_model.push_to_hub("il-mio-bellissimo-modello", organization="la-mia-fantastica-org") ``` La funzione `push_to_hub` può essere anche utilizzata per aggiungere altri file al repository del modello. Per esempio, aggiungi un tokenizer ad un repository di un modello: ```py >>> tokenizer.push_to_hub("il-mio-bellissimo-modello") ``` O magari potresti voler aggiungere la versione di TensorFlow del tuo modello PyTorch a cui hai fatto fine-tuning: ```py >>> tf_model.push_to_hub("il-mio-bellissimo-modello") ``` Ora quando navighi nel tuo profilo Hugging Face, dovresti vedere il tuo repository del modello appena creato. Premendo sulla scheda **Files** vengono visualizzati tutti i file caricati nel repository. Per maggiori dettagli su come creare e caricare file ad un repository, fai riferimento alla documentazione [qui](https://huggingface.co/docs/hub/how-to-upstream). ## Carica un modello utilizzando l'interfaccia web Chi preferisce un approccio senza codice può caricare un modello tramite l'interfaccia web dell'hub. Visita [huggingface.co/new](https://huggingface.co/new) per creare un nuovo repository:  Da qui, aggiungi alcune informazioni sul tuo modello: - Seleziona il/la **owner** del repository. Puoi essere te o qualunque organizzazione di cui fai parte. - Scegli un nome per il tuo modello, il quale sarà anche il nome del repository. - Scegli se il tuo modello è pubblico o privato. - Specifica la licenza utilizzata per il tuo modello. Ora premi sulla scheda **Files** e premi sul pulsante **Add file** per caricare un nuovo file al tuo repository. Trascina poi un file per caricarlo e aggiungere un messaggio di commit.  ## Aggiungi una scheda del modello Per assicurarti che chiunque possa comprendere le abilità, limitazioni, i potenziali bias e le considerazioni etiche del tuo modello, per favore aggiungi una scheda del modello (model card, in inglese) al tuo repository. La scheda del modello è definita nel file `README.md`. Puoi aggiungere una scheda del modello: * Creando manualmente e caricando un file `README.md`. * Premendo sul pulsante **Edit model card** nel repository del tuo modello. Dai un'occhiata alla [scheda del modello](https://huggingface.co/distilbert-base-uncased) di DistilBert per avere un buon esempio del tipo di informazioni che una scheda di un modello deve includere. Per maggiori dettagli legati ad altre opzioni che puoi controllare nel file `README.md`, come l'impatto ambientale o widget di esempio, fai riferimento alla documentazione [qui](https://huggingface.co/docs/hub/models-cards).

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Condividi un modello Gli ultimi due tutorial ti hanno mostrato come puoi fare fine-tuning di un modello con PyTorch, Keras e 🤗 Accelerate per configurazioni distribuite. Il prossimo passo è quello di condividere il tuo modello con la community! In Hugging Face, crediamo nella condivisione della conoscenza e delle risorse in modo da democratizzare l'intelligenza artificiale per chiunque. Ti incoraggiamo a considerare di condividere il tuo modello con la community per aiutare altre persone a risparmiare tempo e risorse. In questo tutorial, imparerai due metodi per la condivisione di un modello trained o fine-tuned nel [Model Hub](https://huggingface.co/models): - Condividi in modo programmatico i tuoi file nell'Hub. - Trascina i tuoi file nell'Hub mediante interfaccia grafica. <iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <Tip> Per condividere un modello con la community, hai bisogno di un account su [huggingface.co](https://huggingface.co/join). Puoi anche unirti ad un'organizzazione esistente o crearne una nuova. </Tip> ## Caratteristiche dei repository Ogni repository nel Model Hub si comporta come un tipico repository di GitHub. I nostri repository offrono il versionamento, la cronologia dei commit, e la possibilità di visualizzare le differenze. Il versionamento all'interno del Model Hub è basato su git e [git-lfs](https://git-lfs.github.com/). In altre parole, puoi trattare un modello come un unico repository, consentendo un maggiore controllo degli accessi e maggiore scalabilità. Il controllo delle versioni consente *revisions*, un metodo per appuntare una versione specifica di un modello con un hash di commit, un tag o un branch. Come risultato, puoi caricare una specifica versione di un modello con il parametro `revision`: ```py >>> model = AutoModel.from_pretrained( ... "julien-c/EsperBERTo-small", revision="v2.0.1" # nome di un tag, di un branch, o commit hash ... ) ``` Anche i file possono essere modificati facilmente in un repository ed è possibile visualizzare la cronologia dei commit e le differenze:  ## Configurazione Prima di condividere un modello nell'Hub, hai bisogno delle tue credenziali di Hugging Face. Se hai accesso ad un terminale, esegui il seguente comando nell'ambiente virtuale in cui è installata la libreria 🤗 Transformers. Questo memorizzerà il tuo token di accesso nella cartella cache di Hugging Face (di default `~/.cache/`): ```bash huggingface-cli login ``` Se stai usando un notebook come Jupyter o Colaboratory, assicurati di avere la libreria [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) installata. Questa libreria ti permette di interagire in maniera programmatica con l'Hub. ```bash pip install huggingface_hub ``` Utilizza `notebook_login` per accedere all'Hub, e segui il link [qui](https://huggingface.co/settings/token) per generare un token con cui effettuare il login: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Converti un modello per tutti i framework Per assicurarti che il tuo modello possa essere utilizzato da persone che lavorano con un framework differente, ti raccomandiamo di convertire e caricare il tuo modello sia con i checkpoint di PyTorch che con quelli di TensorFlow. Anche se è possibile caricare il modello da un framework diverso, se si salta questo passaggio, il caricamento sarà più lento perché 🤗 Transformers ha bisogno di convertire i checkpoint al momento. Convertire un checkpoint per un altro framework è semplice. Assicurati di avere PyTorch e TensorFlow installati (vedi [qui](installation) per le istruzioni d'installazione), e poi trova il modello specifico per il tuo compito nell'altro framework. <frameworkcontent> <pt> Specifica `from_tf=True` per convertire un checkpoint da TensorFlow a PyTorch: ```py >>> pt_model = DistilBertForSequenceClassification.from_pretrained( ... "path/verso/il-nome-magnifico-che-hai-scelto", from_tf=True ... ) >>> pt_model.save_pretrained("path/verso/il-nome-magnifico-che-hai-scelto") ``` </pt> <tf> Specifica `from_pt=True` per convertire un checkpoint da PyTorch a TensorFlow: ```py >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained( ... "path/verso/il-nome-magnifico-che-hai-scelto", from_pt=True ... ) ``` Poi puoi salvare il tuo nuovo modello in TensorFlow con il suo nuovo checkpoint: ```py >>> tf_model.save_pretrained("path/verso/il-nome-magnifico-che-hai-scelto") ``` </tf> <jax> Se un modello è disponibile in Flax, puoi anche convertire un checkpoint da PyTorch a Flax: ```py >>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( ... "path/verso/il-nome-magnifico-che-hai-scelto", from_pt=True ... ) ``` </jax> </frameworkcontent> ## Condividi un modello durante il training <frameworkcontent> <pt> <Youtube id="Z1-XMy-GNLQ"/> Condividere un modello nell'Hub è tanto semplice quanto aggiungere un parametro extra o un callback. Ricorda dal [tutorial sul fine-tuning](training), la classe [`TrainingArguments`] è dove specifichi gli iperparametri e le opzioni addizionali per l'allenamento. Una di queste opzioni di training include l'abilità di condividere direttamente un modello nell'Hub. Imposta `push_to_hub=True` in [`TrainingArguments`]: ```py >>> training_args = TrainingArguments(output_dir="il-mio-bellissimo-modello", push_to_hub=True) ``` Passa gli argomenti per il training come di consueto al [`Trainer`]: ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` Dopo aver effettuato il fine-tuning del tuo modello, chiama [`~transformers.Trainer.push_to_hub`] sul [`Trainer`] per condividere il modello allenato nell'Hub. 🤗 Transformers aggiungerà in modo automatico persino gli iperparametri, i risultati del training e le versioni del framework alla scheda del tuo modello (model card, in inglese)! ```py >>> trainer.push_to_hub() ``` </pt> <tf> Condividi un modello nell'Hub con [`PushToHubCallback`]. Nella funzione [`PushToHubCallback`], aggiungi: - Una directory di output per il tuo modello. - Un tokenizer. - L'`hub_model_id`, che è il tuo username sull'Hub e il nome del modello. ```py >>> from transformers.keras.callbacks import PushToHubCallback >>> push_to_hub_callback = PushToHubCallback( ... output_dir="./il_path_dove_salvare_il_tuo_modello", ... tokenizer=tokenizer, ... hub_model_id="il-tuo-username/il-mio-bellissimo-modello", ... ) ``` Aggiungi il callback a [`fit`](https://keras.io/api/models/model_training_apis/), e 🤗 Transformers caricherà il modello allenato nell'Hub: ```py >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) ``` </tf> </frameworkcontent> ## Utilizzare la funzione `push_to_hub` Puoi anche chiamare `push_to_hub` direttamente sul tuo modello per caricarlo nell'Hub. Specifica il nome del tuo modello in `push_to_hub`: ```py >>> pt_model.push_to_hub("il-mio-bellissimo-modello") ``` Questo crea un repository sotto il proprio username con il nome del modello `il-mio-bellissimo-modello`. Ora chiunque può caricare il tuo modello con la funzione `from_pretrained`: ```py >>> from transformers import AutoModel >>> model = AutoModel.from_pretrained("il-tuo-username/il-mio-bellissimo-modello") ``` Se fai parte di un'organizzazione e vuoi invece condividere un modello sotto il nome dell'organizzazione, aggiungi il parametro `organization`: ```py >>> pt_model.push_to_hub("il-mio-bellissimo-modello", organization="la-mia-fantastica-org") ``` La funzione `push_to_hub` può essere anche utilizzata per aggiungere altri file al repository del modello. Per esempio, aggiungi un tokenizer ad un repository di un modello: ```py >>> tokenizer.push_to_hub("il-mio-bellissimo-modello") ``` O magari potresti voler aggiungere la versione di TensorFlow del tuo modello PyTorch a cui hai fatto fine-tuning: ```py >>> tf_model.push_to_hub("il-mio-bellissimo-modello") ``` Ora quando navighi nel tuo profilo Hugging Face, dovresti vedere il tuo repository del modello appena creato. Premendo sulla scheda **Files** vengono visualizzati tutti i file caricati nel repository. Per maggiori dettagli su come creare e caricare file ad un repository, fai riferimento alla documentazione [qui](https://huggingface.co/docs/hub/how-to-upstream). ## Carica un modello utilizzando l'interfaccia web Chi preferisce un approccio senza codice può caricare un modello tramite l'interfaccia web dell'hub. Visita [huggingface.co/new](https://huggingface.co/new) per creare un nuovo repository:  Da qui, aggiungi alcune informazioni sul tuo modello: - Seleziona il/la **owner** del repository. Puoi essere te o qualunque organizzazione di cui fai parte. - Scegli un nome per il tuo modello, il quale sarà anche il nome del repository. - Scegli se il tuo modello è pubblico o privato. - Specifica la licenza utilizzata per il tuo modello. Ora premi sulla scheda **Files** e premi sul pulsante **Add file** per caricare un nuovo file al tuo repository. Trascina poi un file per caricarlo e aggiungere un messaggio di commit.  ## Aggiungi una scheda del modello Per assicurarti che chiunque possa comprendere le abilità, limitazioni, i potenziali bias e le considerazioni etiche del tuo modello, per favore aggiungi una scheda del modello (model card, in inglese) al tuo repository. La scheda del modello è definita nel file `README.md`. Puoi aggiungere una scheda del modello: * Creando manualmente e caricando un file `README.md`. * Premendo sul pulsante **Edit model card** nel repository del tuo modello. Dai un'occhiata alla [scheda del modello](https://huggingface.co/distilbert-base-uncased) di DistilBert per avere un buon esempio del tipo di informazioni che una scheda di un modello deve includere. Per maggiori dettagli legati ad altre opzioni che puoi controllare nel file `README.md`, come l'impatto ambientale o widget di esempio, fai riferimento alla documentazione [qui](https://huggingface.co/docs/hub/models-cards).

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/fnet/configuration_fnet.py

# coding=utf-8 # Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ FNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json", "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json" # See all FNet models at https://huggingface.co/models?filter=fnet } class FNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FNet [google/fnet-base](https://huggingface.co/google/fnet-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 4): The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. tpu_short_seq_length (`int`, *optional*, defaults to 512): The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or equal to 4096 tokens. Example: ```python >>> from transformers import FNetConfig, FNetModel >>> # Initializing a FNet fnet-base style configuration >>> configuration = FNetConfig() >>> # Initializing a model (with random weights) from the fnet-base style configuration >>> model = FNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "fnet" def __init__( self, vocab_size=32000, hidden_size=768, num_hidden_layers=12, intermediate_size=3072, hidden_act="gelu_new", hidden_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=4, initializer_range=0.02, layer_norm_eps=1e-12, use_tpu_fourier_optimizations=False, tpu_short_seq_length=512, pad_token_id=3, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations self.tpu_short_seq_length = tpu_short_seq_length

# coding=utf-8 # Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ FNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json", "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json" # See all FNet models at https://huggingface.co/models?filter=fnet } class FNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FNet [google/fnet-base](https://huggingface.co/google/fnet-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 4): The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. tpu_short_seq_length (`int`, *optional*, defaults to 512): The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or equal to 4096 tokens. Example: ```python >>> from transformers import FNetConfig, FNetModel >>> # Initializing a FNet fnet-base style configuration >>> configuration = FNetConfig() >>> # Initializing a model (with random weights) from the fnet-base style configuration >>> model = FNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "fnet" def __init__( self, vocab_size=32000, hidden_size=768, num_hidden_layers=12, intermediate_size=3072, hidden_act="gelu_new", hidden_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=4, initializer_range=0.02, layer_norm_eps=1e-12, use_tpu_fourier_optimizations=False, tpu_short_seq_length=512, pad_token_id=3, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations self.tpu_short_seq_length = tpu_short_seq_length

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/utils/test_logging.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import transformers.models.bart.tokenization_bart from huggingface_hub.utils import are_progress_bars_disabled from transformers import logging from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context from transformers.utils.logging import disable_progress_bar, enable_progress_bar class HfArgumentParserTest(unittest.TestCase): def test_set_level(self): logger = logging.get_logger() # the current default level is logging.WARNING level_origin = logging.get_verbosity() logging.set_verbosity_error() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) logging.set_verbosity_warning() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) logging.set_verbosity_info() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) logging.set_verbosity_debug() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) # restore to the original level logging.set_verbosity(level_origin) def test_integration(self): level_origin = logging.get_verbosity() logger = logging.get_logger("transformers.models.bart.tokenization_bart") msg = "Testing 1, 2, 3" # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`) if level_origin <= logging.WARNING: with CaptureLogger(logger) as cl: logger.warning(msg) self.assertEqual(cl.out, msg + "\n") # this is setting the level for all of `transformers.*` loggers logging.set_verbosity_error() # should not be able to log warnings with CaptureLogger(logger) as cl: logger.warning(msg) self.assertEqual(cl.out, "") # should be able to log warnings again logging.set_verbosity_warning() with CaptureLogger(logger) as cl: logger.warning(msg) self.assertEqual(cl.out, msg + "\n") # restore to the original level logging.set_verbosity(level_origin) @mockenv(TRANSFORMERS_VERBOSITY="error") def test_env_override(self): # reset for the env var to take effect, next time some logger call is made transformers.utils.logging._reset_library_root_logger() # this action activates the env var _ = logging.get_logger("transformers.models.bart.tokenization_bart") env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None) env_level = logging.log_levels[env_level_str] current_level = logging.get_verbosity() self.assertEqual( env_level, current_level, f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}", ) # restore to the original level os.environ["TRANSFORMERS_VERBOSITY"] = "" transformers.utils.logging._reset_library_root_logger() @mockenv(TRANSFORMERS_VERBOSITY="super-error") def test_env_invalid_override(self): # reset for the env var to take effect, next time some logger call is made transformers.utils.logging._reset_library_root_logger() logger = logging.logging.getLogger() with CaptureLogger(logger) as cl: # this action activates the env var logging.get_logger("transformers.models.bart.tokenization_bart") self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error", cl.out) # no need to restore as nothing was changed def test_advisory_warnings(self): # testing `logger.warning_advice()` logger = logging.get_logger("transformers.models.bart.tokenization_bart") msg = "Testing 1, 2, 3" with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"): # nothing should be logged as env var disables this method with CaptureLogger(logger) as cl: logger.warning_advice(msg) self.assertEqual(cl.out, "") with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""): # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset with CaptureLogger(logger) as cl: logger.warning_advice(msg) self.assertEqual(cl.out, msg + "\n") def test_set_progress_bar_enabled(): disable_progress_bar() assert are_progress_bars_disabled() enable_progress_bar() assert not are_progress_bars_disabled()

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import transformers.models.bart.tokenization_bart from huggingface_hub.utils import are_progress_bars_disabled from transformers import logging from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context from transformers.utils.logging import disable_progress_bar, enable_progress_bar class HfArgumentParserTest(unittest.TestCase): def test_set_level(self): logger = logging.get_logger() # the current default level is logging.WARNING level_origin = logging.get_verbosity() logging.set_verbosity_error() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) logging.set_verbosity_warning() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) logging.set_verbosity_info() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) logging.set_verbosity_debug() self.assertEqual(logger.getEffectiveLevel(), logging.get_verbosity()) # restore to the original level logging.set_verbosity(level_origin) def test_integration(self): level_origin = logging.get_verbosity() logger = logging.get_logger("transformers.models.bart.tokenization_bart") msg = "Testing 1, 2, 3" # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`) if level_origin <= logging.WARNING: with CaptureLogger(logger) as cl: logger.warning(msg) self.assertEqual(cl.out, msg + "\n") # this is setting the level for all of `transformers.*` loggers logging.set_verbosity_error() # should not be able to log warnings with CaptureLogger(logger) as cl: logger.warning(msg) self.assertEqual(cl.out, "") # should be able to log warnings again logging.set_verbosity_warning() with CaptureLogger(logger) as cl: logger.warning(msg) self.assertEqual(cl.out, msg + "\n") # restore to the original level logging.set_verbosity(level_origin) @mockenv(TRANSFORMERS_VERBOSITY="error") def test_env_override(self): # reset for the env var to take effect, next time some logger call is made transformers.utils.logging._reset_library_root_logger() # this action activates the env var _ = logging.get_logger("transformers.models.bart.tokenization_bart") env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None) env_level = logging.log_levels[env_level_str] current_level = logging.get_verbosity() self.assertEqual( env_level, current_level, f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}", ) # restore to the original level os.environ["TRANSFORMERS_VERBOSITY"] = "" transformers.utils.logging._reset_library_root_logger() @mockenv(TRANSFORMERS_VERBOSITY="super-error") def test_env_invalid_override(self): # reset for the env var to take effect, next time some logger call is made transformers.utils.logging._reset_library_root_logger() logger = logging.logging.getLogger() with CaptureLogger(logger) as cl: # this action activates the env var logging.get_logger("transformers.models.bart.tokenization_bart") self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error", cl.out) # no need to restore as nothing was changed def test_advisory_warnings(self): # testing `logger.warning_advice()` logger = logging.get_logger("transformers.models.bart.tokenization_bart") msg = "Testing 1, 2, 3" with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"): # nothing should be logged as env var disables this method with CaptureLogger(logger) as cl: logger.warning_advice(msg) self.assertEqual(cl.out, "") with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""): # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset with CaptureLogger(logger) as cl: logger.warning_advice(msg) self.assertEqual(cl.out, msg + "\n") def test_set_progress_bar_enabled(): disable_progress_bar() assert are_progress_bars_disabled() enable_progress_bar() assert not are_progress_bars_disabled()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ Pre-Training a 🤗 Wav2Vec2 model on unlabeled audio data """ import argparse import math import os from dataclasses import dataclass from pathlib import Path from typing import Dict, List, Optional, Union import datasets import torch from datasets import DatasetDict, concatenate_datasets, load_dataset from torch.utils.data.dataloader import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator from accelerate.logging import get_logger from huggingface_hub import Repository from transformers import ( AdamW, SchedulerType, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining, get_scheduler, is_wandb_available, set_seed, ) from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices from transformers.utils import get_full_repo_name, send_example_telemetry logger = get_logger(__name__) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_names", nargs="+", type=str, required=True, help="The configuration names of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_split_names", nargs="+", type=str, required=True, help="The names of the training data set splits to use (via the datasets library).", ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--preprocessing_only", action="store_true", help="Only run the preprocessing script to be cached for future use", ) parser.add_argument( "--cache_dir", type=str, default=None, help="Where do you want to store the pretrained models downloaded from huggingface.co", ) parser.add_argument( "--validation_split_percentage", type=int, default=1, help="Percentage of training data that should be used for validation if no validation is present in dataset.", ) parser.add_argument( "--logging_steps", type=int, default=500, help="Number of steps between each logging", ) parser.add_argument( "--saving_steps", type=int, default=500, help="Number of steps between each logging", ) parser.add_argument( "--audio_column_name", type=str, default="audio", help="Column in the dataset that contains speech file path. Defaults to 'audio'", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=True, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--train_cache_file_name", type=str, default=None, help="Path to the train cached file name", ) parser.add_argument( "--validation_cache_file_name", type=str, default=None, help="Path to the validation cached file name", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="If True, use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.") parser.add_argument( "--max_gumbel_temperature", type=float, default=2.0, help="Maximum temperature for gumbel softmax.", ) parser.add_argument( "--min_gumbel_temperature", type=float, default=0.5, help="Minimum temperature for gumbel softmax.", ) parser.add_argument( "--gumbel_temperature_decay", type=float, default=0.999995, help="Decay of gumbel temperature during training." ) parser.add_argument( "--max_duration_in_seconds", type=float, default=5.0, help="Filter out audio files that are longer than `max_duration_in_seconds` seconds", ) parser.add_argument( "--min_duration_in_seconds", type=float, default=3.0, help="Filter out audio files that are shorter than `min_duration_in_seconds` seconds", ) parser.add_argument( "--pad_to_multiple_of", type=int, default=None, help=( "If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the" " use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta)." ), ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="Beta1 for AdamW optimizer", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="Beta2 for AdamW optimizer", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-8, help="Epsilon for AdamW optimizer", ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") args = parser.parse_args() if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args @dataclass class DataCollatorForWav2Vec2Pretraining: """ Data collator that will dynamically pad the inputs received and prepare masked indices for self-supervised pretraining. Args: model (:class:`~transformers.Wav2Vec2ForPreTraining`): The Wav2Vec2 model used for pretraining. The data collator needs to have access to config and ``_get_feat_extract_output_lengths`` function for correct padding. feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`): The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ model: Wav2Vec2ForPreTraining feature_extractor: Wav2Vec2FeatureExtractor padding: Union[bool, str] = "longest" pad_to_multiple_of: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # reformat list to dict and set to pytorch format batch = self.feature_extractor.pad( features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) device = batch["input_values"].device batch_size = batch["input_values"].shape[0] mask_indices_seq_length = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]) # make sure masked sequence length is a Python scalar mask_indices_seq_length = int(mask_indices_seq_length) # make sure that no loss is computed on padded inputs if batch.get("attention_mask") is not None: # compute real output lengths according to convolution formula batch["sub_attention_mask"] = self.model._get_feature_vector_attention_mask( mask_indices_seq_length, batch["attention_mask"] ) features_shape = (batch_size, mask_indices_seq_length) # sample randomly masked indices mask_time_indices = _compute_mask_indices( features_shape, self.model.config.mask_time_prob, self.model.config.mask_time_length, attention_mask=batch.get("sub_attention_mask"), ) # sample negative indices sampled_negative_indices = _sample_negative_indices( features_shape, self.model.config.num_negatives, mask_time_indices=mask_time_indices, ) batch["mask_time_indices"] = torch.tensor(mask_time_indices, dtype=torch.long, device=device) batch["sampled_negative_indices"] = torch.tensor(sampled_negative_indices, dtype=torch.long, device=device) return batch def multiply_grads(params, c): """Multiplies grads by a constant *c*.""" for p in params: if p.grad is not None: if torch.is_tensor(c): c = c.to(p.grad.device) p.grad.data.mul_(c) def get_grad_norm(params, scale=1): """Compute grad norm given a gradient scale.""" total_norm = 0.0 for p in params: if p.grad is not None: param_norm = (p.grad.detach().data / scale).norm(2) total_norm += param_norm.item() ** 2 total_norm = total_norm**0.5 return total_norm def main(): # See all possible arguments in src/transformers/args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_wav2vec2_pretraining_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. accelerator = Accelerator() logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() # set up weights and biases if available if is_wandb_available(): import wandb wandb.init(project=args.output_dir.split("/")[-1]) else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub and not args.preprocessing_only: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # 1. Download and create train, validation dataset # We load all dataset configuration and datset split pairs passed in # ``args.dataset_config_names`` and ``args.dataset_split_names`` datasets_splits = [] for dataset_config_name, train_split_name in zip(args.dataset_config_names, args.dataset_split_names): # load dataset dataset_split = load_dataset( args.dataset_name, dataset_config_name, split=train_split_name, cache_dir=args.cache_dir, ) datasets_splits.append(dataset_split) # Next, we concatenate all configurations and splits into a single training dataset raw_datasets = DatasetDict() if len(datasets_splits) > 1: raw_datasets["train"] = concatenate_datasets(datasets_splits).shuffle(seed=args.seed) else: raw_datasets["train"] = datasets_splits[0] # Take ``args.validation_split_percentage`` from the training dataset for the validation_split_percentage num_validation_samples = raw_datasets["train"].num_rows * args.validation_split_percentage // 100 if num_validation_samples == 0: raise ValueError( "`args.validation_split_percentage` is less than a single sample " f"for {len(raw_datasets['train'])} training samples. Increase " "`args.num_validation_split_percentage`. " ) raw_datasets["validation"] = raw_datasets["train"].select(range(num_validation_samples)) raw_datasets["train"] = raw_datasets["train"].select(range(num_validation_samples, raw_datasets["train"].num_rows)) # 2. Now we preprocess the datasets including loading the audio, resampling and normalization # Thankfully, `datasets` takes care of automatically loading and resampling the audio, # so that we just need to set the correct target sampling rate and normalize the input # via the `feature_extractor` feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(args.model_name_or_path) # make sure that dataset decodes audio with correct sampling rate raw_datasets = raw_datasets.cast_column( args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) # only normalized-inputs-training is supported if not feature_extractor.do_normalize: raise ValueError( "Training is only supported for normalized inputs. Make sure ``feature_extractor.do_normalize == True``" ) # set max & min audio length in number of samples max_length = int(args.max_duration_in_seconds * feature_extractor.sampling_rate) min_length = int(args.min_duration_in_seconds * feature_extractor.sampling_rate) def prepare_dataset(batch): sample = batch[args.audio_column_name] inputs = feature_extractor( sample["array"], sampling_rate=sample["sampling_rate"], max_length=max_length, truncation=True ) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(inputs.input_values[0]) return batch # load via mapped files via path cache_file_names = None if args.train_cache_file_name is not None: cache_file_names = {"train": args.train_cache_file_name, "validation": args.validation_cache_file_name} # load audio files into numpy arrays with accelerator.main_process_first(): vectorized_datasets = raw_datasets.map( prepare_dataset, num_proc=args.preprocessing_num_workers, remove_columns=raw_datasets["train"].column_names, cache_file_names=cache_file_names, ) if min_length > 0.0: vectorized_datasets = vectorized_datasets.filter( lambda x: x > min_length, num_proc=args.preprocessing_num_workers, input_columns=["input_length"], ) vectorized_datasets = vectorized_datasets.remove_columns("input_length") # for large datasets it is advised to run the preprocessing on a # single machine first with ``args.preprocessing_only`` since there will mostly likely # be a timeout when running the script in distributed mode. # In a second step ``args.preprocessing_only`` can then be set to `False` to load the # cached dataset if args.preprocessing_only: return # 3. Load model config = Wav2Vec2Config.from_pretrained(args.model_name_or_path) # pretraining is only supported for "newer" stable layer norm architecture # apply_spec_augment has to be True, mask_feature_prob has to be 0.0 if not config.do_stable_layer_norm or config.feat_extract_norm != "layer": raise ValueError( "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and" " ``config.feat_extract_norm='layer'" ) # initialize random model model = Wav2Vec2ForPreTraining(config) # Activate gradient checkpointing if needed if args.gradient_checkpointing: model.gradient_checkpointing_enable() # 4. Define data collator, optimizer and scheduler data_collator = DataCollatorForWav2Vec2Pretraining( model=model, feature_extractor=feature_extractor, pad_to_multiple_of=args.pad_to_multiple_of ) train_dataloader = DataLoader( vectorized_datasets["train"], shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size, ) eval_dataloader = DataLoader( vectorized_datasets["validation"], collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer optimizer = AdamW( list(model.parameters()), lr=args.learning_rate, betas=[args.adam_beta1, args.adam_beta2], eps=args.adam_epsilon, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader ) # Scheduler and math around the number of training steps. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # 5. Train total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(vectorized_datasets['train'])}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") completed_steps = 0 starting_epoch = 0 # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 for epoch in range(starting_epoch, args.num_train_epochs): model.train() for step, batch in enumerate(train_dataloader): # compute num of losses num_losses = batch["mask_time_indices"].sum() sub_attention_mask = batch.pop("sub_attention_mask", None) sub_attention_mask = ( sub_attention_mask if sub_attention_mask is not None else torch.ones_like(batch["mask_time_indices"]) ) percent_masked = num_losses / sub_attention_mask.sum() # forward outputs = model(**batch) # divide loss by gradient accumulation steps since gradients # are accumulated for multiple backward passes in PyTorch loss = outputs.loss / args.gradient_accumulation_steps accelerator.backward(loss) # make sure that `num_losses` is summed for distributed training # and average gradients over losses of all devices if accelerator.state.num_processes > 1: num_losses = accelerator.gather_for_metrics(num_losses).sum() gradient_multiplier = accelerator.state.num_processes / num_losses multiply_grads(model.module.parameters(), gradient_multiplier) else: multiply_grads(model.parameters(), 1 / num_losses) # update step if (step + 1) % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: # compute grad norm for monitoring scale = ( accelerator.scaler._scale.item() if hasattr(accelerator, "scaler") and accelerator.scaler is not None else 1 ) if accelerator.state.num_processes > 1: grad_norm = get_grad_norm(model.module.parameters(), scale) else: grad_norm = get_grad_norm(model.parameters(), scale) # update parameters optimizer.step() optimizer.zero_grad() if not accelerator.optimizer_step_was_skipped: lr_scheduler.step() elif accelerator.is_local_main_process: progress_bar.write( f"Gradients have overflown - skipping update step... Updating gradient scale to {scale}..." ) # update gumbel temperature gumbel_temperature = max( args.max_gumbel_temperature * args.gumbel_temperature_decay**completed_steps, args.min_gumbel_temperature, ) if hasattr(model, "module"): model.module.set_gumbel_temperature(gumbel_temperature) else: model.set_gumbel_temperature(gumbel_temperature) progress_bar.update(1) completed_steps += 1 # 6. Log all results if (step + 1) % (args.gradient_accumulation_steps * args.logging_steps) == 0: loss.detach() outputs.contrastive_loss.detach() outputs.diversity_loss.detach() if accelerator.state.num_processes > 1: loss = accelerator.gather_for_metrics(loss).sum() outputs.contrastive_loss = accelerator.gather_for_metrics(outputs.contrastive_loss).sum() outputs.diversity_loss = accelerator.gather_for_metrics(outputs.diversity_loss).sum() percent_masked = accelerator.gather_for_metrics(percent_masked).sum() train_logs = { "loss": (loss * args.gradient_accumulation_steps) / num_losses, "constrast_loss": outputs.contrastive_loss / num_losses, "div_loss": outputs.diversity_loss / num_losses, "%_mask_idx": percent_masked / accelerator.num_processes, "ppl": outputs.codevector_perplexity, "lr": torch.tensor(optimizer.param_groups[0]["lr"]), "temp": torch.tensor(gumbel_temperature), "grad_norm": torch.tensor(grad_norm), } log_str = "" for k, v in train_logs.items(): log_str += "| {}: {:.3e}".format(k, v.item()) if accelerator.is_local_main_process: progress_bar.write(log_str) if is_wandb_available(): wandb.log(train_logs) # save model every `args.saving_steps` steps if (step + 1) % (args.gradient_accumulation_steps * args.saving_steps) == 0: if (args.push_to_hub and epoch < args.num_train_epochs - 1) or args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if (args.push_to_hub and epoch < args.num_train_epochs - 1) and accelerator.is_main_process: repo.push_to_hub( commit_message=f"Training in progress step {completed_steps}", blocking=False, auto_lfs_prune=True, ) # if completed steps > `args.max_train_steps` stop if completed_steps >= args.max_train_steps: break # 7. Validate! model.eval() # init logs val_logs = { "val_loss": 0, "val_contrastive_loss": 0, "val_diversity_loss": 0, "val_num_losses": 0, } for step, batch in enumerate(eval_dataloader): with torch.no_grad(): batch.pop("sub_attention_mask", None) outputs = model(**batch) val_logs["val_loss"] += outputs.loss val_logs["val_contrastive_loss"] += outputs.contrastive_loss val_logs["val_diversity_loss"] += outputs.diversity_loss val_logs["val_num_losses"] += batch["mask_time_indices"].sum() # sum over devices in multi-processing if accelerator.num_processes > 1: val_logs = {k: accelerator.gather_for_metrics(v).sum() for k, v in val_logs.items()} val_logs = {k: v / val_logs["val_num_losses"] for k, v in val_logs.items()} log_str = "" for k, v in val_logs.items(): log_str += "| {}: {:.3e}".format(k, v.item()) if accelerator.is_local_main_process: progress_bar.write(log_str) if is_wandb_available(): wandb.log(val_logs) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ Pre-Training a 🤗 Wav2Vec2 model on unlabeled audio data """ import argparse import math import os from dataclasses import dataclass from pathlib import Path from typing import Dict, List, Optional, Union import datasets import torch from datasets import DatasetDict, concatenate_datasets, load_dataset from torch.utils.data.dataloader import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator from accelerate.logging import get_logger from huggingface_hub import Repository from transformers import ( AdamW, SchedulerType, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining, get_scheduler, is_wandb_available, set_seed, ) from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices from transformers.utils import get_full_repo_name, send_example_telemetry logger = get_logger(__name__) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_names", nargs="+", type=str, required=True, help="The configuration names of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_split_names", nargs="+", type=str, required=True, help="The names of the training data set splits to use (via the datasets library).", ) parser.add_argument( "--preprocessing_num_workers", type=int, default=None, help="The number of processes to use for the preprocessing.", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--preprocessing_only", action="store_true", help="Only run the preprocessing script to be cached for future use", ) parser.add_argument( "--cache_dir", type=str, default=None, help="Where do you want to store the pretrained models downloaded from huggingface.co", ) parser.add_argument( "--validation_split_percentage", type=int, default=1, help="Percentage of training data that should be used for validation if no validation is present in dataset.", ) parser.add_argument( "--logging_steps", type=int, default=500, help="Number of steps between each logging", ) parser.add_argument( "--saving_steps", type=int, default=500, help="Number of steps between each logging", ) parser.add_argument( "--audio_column_name", type=str, default="audio", help="Column in the dataset that contains speech file path. Defaults to 'audio'", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=True, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--train_cache_file_name", type=str, default=None, help="Path to the train cached file name", ) parser.add_argument( "--validation_cache_file_name", type=str, default=None, help="Path to the validation cached file name", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="If True, use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.") parser.add_argument( "--max_gumbel_temperature", type=float, default=2.0, help="Maximum temperature for gumbel softmax.", ) parser.add_argument( "--min_gumbel_temperature", type=float, default=0.5, help="Minimum temperature for gumbel softmax.", ) parser.add_argument( "--gumbel_temperature_decay", type=float, default=0.999995, help="Decay of gumbel temperature during training." ) parser.add_argument( "--max_duration_in_seconds", type=float, default=5.0, help="Filter out audio files that are longer than `max_duration_in_seconds` seconds", ) parser.add_argument( "--min_duration_in_seconds", type=float, default=3.0, help="Filter out audio files that are shorter than `min_duration_in_seconds` seconds", ) parser.add_argument( "--pad_to_multiple_of", type=int, default=None, help=( "If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the" " use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta)." ), ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="Beta1 for AdamW optimizer", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="Beta2 for AdamW optimizer", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-8, help="Epsilon for AdamW optimizer", ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") args = parser.parse_args() if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args @dataclass class DataCollatorForWav2Vec2Pretraining: """ Data collator that will dynamically pad the inputs received and prepare masked indices for self-supervised pretraining. Args: model (:class:`~transformers.Wav2Vec2ForPreTraining`): The Wav2Vec2 model used for pretraining. The data collator needs to have access to config and ``_get_feat_extract_output_lengths`` function for correct padding. feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`): The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ model: Wav2Vec2ForPreTraining feature_extractor: Wav2Vec2FeatureExtractor padding: Union[bool, str] = "longest" pad_to_multiple_of: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # reformat list to dict and set to pytorch format batch = self.feature_extractor.pad( features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) device = batch["input_values"].device batch_size = batch["input_values"].shape[0] mask_indices_seq_length = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]) # make sure masked sequence length is a Python scalar mask_indices_seq_length = int(mask_indices_seq_length) # make sure that no loss is computed on padded inputs if batch.get("attention_mask") is not None: # compute real output lengths according to convolution formula batch["sub_attention_mask"] = self.model._get_feature_vector_attention_mask( mask_indices_seq_length, batch["attention_mask"] ) features_shape = (batch_size, mask_indices_seq_length) # sample randomly masked indices mask_time_indices = _compute_mask_indices( features_shape, self.model.config.mask_time_prob, self.model.config.mask_time_length, attention_mask=batch.get("sub_attention_mask"), ) # sample negative indices sampled_negative_indices = _sample_negative_indices( features_shape, self.model.config.num_negatives, mask_time_indices=mask_time_indices, ) batch["mask_time_indices"] = torch.tensor(mask_time_indices, dtype=torch.long, device=device) batch["sampled_negative_indices"] = torch.tensor(sampled_negative_indices, dtype=torch.long, device=device) return batch def multiply_grads(params, c): """Multiplies grads by a constant *c*.""" for p in params: if p.grad is not None: if torch.is_tensor(c): c = c.to(p.grad.device) p.grad.data.mul_(c) def get_grad_norm(params, scale=1): """Compute grad norm given a gradient scale.""" total_norm = 0.0 for p in params: if p.grad is not None: param_norm = (p.grad.detach().data / scale).norm(2) total_norm += param_norm.item() ** 2 total_norm = total_norm**0.5 return total_norm def main(): # See all possible arguments in src/transformers/args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_wav2vec2_pretraining_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. accelerator = Accelerator() logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() # set up weights and biases if available if is_wandb_available(): import wandb wandb.init(project=args.output_dir.split("/")[-1]) else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub and not args.preprocessing_only: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # 1. Download and create train, validation dataset # We load all dataset configuration and datset split pairs passed in # ``args.dataset_config_names`` and ``args.dataset_split_names`` datasets_splits = [] for dataset_config_name, train_split_name in zip(args.dataset_config_names, args.dataset_split_names): # load dataset dataset_split = load_dataset( args.dataset_name, dataset_config_name, split=train_split_name, cache_dir=args.cache_dir, ) datasets_splits.append(dataset_split) # Next, we concatenate all configurations and splits into a single training dataset raw_datasets = DatasetDict() if len(datasets_splits) > 1: raw_datasets["train"] = concatenate_datasets(datasets_splits).shuffle(seed=args.seed) else: raw_datasets["train"] = datasets_splits[0] # Take ``args.validation_split_percentage`` from the training dataset for the validation_split_percentage num_validation_samples = raw_datasets["train"].num_rows * args.validation_split_percentage // 100 if num_validation_samples == 0: raise ValueError( "`args.validation_split_percentage` is less than a single sample " f"for {len(raw_datasets['train'])} training samples. Increase " "`args.num_validation_split_percentage`. " ) raw_datasets["validation"] = raw_datasets["train"].select(range(num_validation_samples)) raw_datasets["train"] = raw_datasets["train"].select(range(num_validation_samples, raw_datasets["train"].num_rows)) # 2. Now we preprocess the datasets including loading the audio, resampling and normalization # Thankfully, `datasets` takes care of automatically loading and resampling the audio, # so that we just need to set the correct target sampling rate and normalize the input # via the `feature_extractor` feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(args.model_name_or_path) # make sure that dataset decodes audio with correct sampling rate raw_datasets = raw_datasets.cast_column( args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) # only normalized-inputs-training is supported if not feature_extractor.do_normalize: raise ValueError( "Training is only supported for normalized inputs. Make sure ``feature_extractor.do_normalize == True``" ) # set max & min audio length in number of samples max_length = int(args.max_duration_in_seconds * feature_extractor.sampling_rate) min_length = int(args.min_duration_in_seconds * feature_extractor.sampling_rate) def prepare_dataset(batch): sample = batch[args.audio_column_name] inputs = feature_extractor( sample["array"], sampling_rate=sample["sampling_rate"], max_length=max_length, truncation=True ) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(inputs.input_values[0]) return batch # load via mapped files via path cache_file_names = None if args.train_cache_file_name is not None: cache_file_names = {"train": args.train_cache_file_name, "validation": args.validation_cache_file_name} # load audio files into numpy arrays with accelerator.main_process_first(): vectorized_datasets = raw_datasets.map( prepare_dataset, num_proc=args.preprocessing_num_workers, remove_columns=raw_datasets["train"].column_names, cache_file_names=cache_file_names, ) if min_length > 0.0: vectorized_datasets = vectorized_datasets.filter( lambda x: x > min_length, num_proc=args.preprocessing_num_workers, input_columns=["input_length"], ) vectorized_datasets = vectorized_datasets.remove_columns("input_length") # for large datasets it is advised to run the preprocessing on a # single machine first with ``args.preprocessing_only`` since there will mostly likely # be a timeout when running the script in distributed mode. # In a second step ``args.preprocessing_only`` can then be set to `False` to load the # cached dataset if args.preprocessing_only: return # 3. Load model config = Wav2Vec2Config.from_pretrained(args.model_name_or_path) # pretraining is only supported for "newer" stable layer norm architecture # apply_spec_augment has to be True, mask_feature_prob has to be 0.0 if not config.do_stable_layer_norm or config.feat_extract_norm != "layer": raise ValueError( "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and" " ``config.feat_extract_norm='layer'" ) # initialize random model model = Wav2Vec2ForPreTraining(config) # Activate gradient checkpointing if needed if args.gradient_checkpointing: model.gradient_checkpointing_enable() # 4. Define data collator, optimizer and scheduler data_collator = DataCollatorForWav2Vec2Pretraining( model=model, feature_extractor=feature_extractor, pad_to_multiple_of=args.pad_to_multiple_of ) train_dataloader = DataLoader( vectorized_datasets["train"], shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size, ) eval_dataloader = DataLoader( vectorized_datasets["validation"], collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer optimizer = AdamW( list(model.parameters()), lr=args.learning_rate, betas=[args.adam_beta1, args.adam_beta2], eps=args.adam_epsilon, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader ) # Scheduler and math around the number of training steps. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # 5. Train total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(vectorized_datasets['train'])}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") completed_steps = 0 starting_epoch = 0 # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 for epoch in range(starting_epoch, args.num_train_epochs): model.train() for step, batch in enumerate(train_dataloader): # compute num of losses num_losses = batch["mask_time_indices"].sum() sub_attention_mask = batch.pop("sub_attention_mask", None) sub_attention_mask = ( sub_attention_mask if sub_attention_mask is not None else torch.ones_like(batch["mask_time_indices"]) ) percent_masked = num_losses / sub_attention_mask.sum() # forward outputs = model(**batch) # divide loss by gradient accumulation steps since gradients # are accumulated for multiple backward passes in PyTorch loss = outputs.loss / args.gradient_accumulation_steps accelerator.backward(loss) # make sure that `num_losses` is summed for distributed training # and average gradients over losses of all devices if accelerator.state.num_processes > 1: num_losses = accelerator.gather_for_metrics(num_losses).sum() gradient_multiplier = accelerator.state.num_processes / num_losses multiply_grads(model.module.parameters(), gradient_multiplier) else: multiply_grads(model.parameters(), 1 / num_losses) # update step if (step + 1) % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: # compute grad norm for monitoring scale = ( accelerator.scaler._scale.item() if hasattr(accelerator, "scaler") and accelerator.scaler is not None else 1 ) if accelerator.state.num_processes > 1: grad_norm = get_grad_norm(model.module.parameters(), scale) else: grad_norm = get_grad_norm(model.parameters(), scale) # update parameters optimizer.step() optimizer.zero_grad() if not accelerator.optimizer_step_was_skipped: lr_scheduler.step() elif accelerator.is_local_main_process: progress_bar.write( f"Gradients have overflown - skipping update step... Updating gradient scale to {scale}..." ) # update gumbel temperature gumbel_temperature = max( args.max_gumbel_temperature * args.gumbel_temperature_decay**completed_steps, args.min_gumbel_temperature, ) if hasattr(model, "module"): model.module.set_gumbel_temperature(gumbel_temperature) else: model.set_gumbel_temperature(gumbel_temperature) progress_bar.update(1) completed_steps += 1 # 6. Log all results if (step + 1) % (args.gradient_accumulation_steps * args.logging_steps) == 0: loss.detach() outputs.contrastive_loss.detach() outputs.diversity_loss.detach() if accelerator.state.num_processes > 1: loss = accelerator.gather_for_metrics(loss).sum() outputs.contrastive_loss = accelerator.gather_for_metrics(outputs.contrastive_loss).sum() outputs.diversity_loss = accelerator.gather_for_metrics(outputs.diversity_loss).sum() percent_masked = accelerator.gather_for_metrics(percent_masked).sum() train_logs = { "loss": (loss * args.gradient_accumulation_steps) / num_losses, "constrast_loss": outputs.contrastive_loss / num_losses, "div_loss": outputs.diversity_loss / num_losses, "%_mask_idx": percent_masked / accelerator.num_processes, "ppl": outputs.codevector_perplexity, "lr": torch.tensor(optimizer.param_groups[0]["lr"]), "temp": torch.tensor(gumbel_temperature), "grad_norm": torch.tensor(grad_norm), } log_str = "" for k, v in train_logs.items(): log_str += "| {}: {:.3e}".format(k, v.item()) if accelerator.is_local_main_process: progress_bar.write(log_str) if is_wandb_available(): wandb.log(train_logs) # save model every `args.saving_steps` steps if (step + 1) % (args.gradient_accumulation_steps * args.saving_steps) == 0: if (args.push_to_hub and epoch < args.num_train_epochs - 1) or args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if (args.push_to_hub and epoch < args.num_train_epochs - 1) and accelerator.is_main_process: repo.push_to_hub( commit_message=f"Training in progress step {completed_steps}", blocking=False, auto_lfs_prune=True, ) # if completed steps > `args.max_train_steps` stop if completed_steps >= args.max_train_steps: break # 7. Validate! model.eval() # init logs val_logs = { "val_loss": 0, "val_contrastive_loss": 0, "val_diversity_loss": 0, "val_num_losses": 0, } for step, batch in enumerate(eval_dataloader): with torch.no_grad(): batch.pop("sub_attention_mask", None) outputs = model(**batch) val_logs["val_loss"] += outputs.loss val_logs["val_contrastive_loss"] += outputs.contrastive_loss val_logs["val_diversity_loss"] += outputs.diversity_loss val_logs["val_num_losses"] += batch["mask_time_indices"].sum() # sum over devices in multi-processing if accelerator.num_processes > 1: val_logs = {k: accelerator.gather_for_metrics(v).sum() for k, v in val_logs.items()} val_logs = {k: v / val_logs["val_num_losses"] for k, v in val_logs.items()} log_str = "" for k, v in val_logs.items(): log_str += "| {}: {:.3e}".format(k, v.item()) if accelerator.is_local_main_process: progress_bar.write(log_str) if is_wandb_available(): wandb.log(val_logs) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) if __name__ == "__main__": main()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/camembert/test_modeling_camembert.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import CamembertModel @require_torch @require_sentencepiece @require_tokenizers class CamembertModelIntegrationTest(unittest.TestCase): @slow def test_output_embeds_base_model(self): model = CamembertModel.from_pretrained("camembert-base") model.to(torch_device) input_ids = torch.tensor( [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]], device=torch_device, dtype=torch.long, ) # J'aime le camembert ! output = model(input_ids)["last_hidden_state"] expected_shape = torch.Size((1, 10, 768)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]], device=torch_device, dtype=torch.float, ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import CamembertModel @require_torch @require_sentencepiece @require_tokenizers class CamembertModelIntegrationTest(unittest.TestCase): @slow def test_output_embeds_base_model(self): model = CamembertModel.from_pretrained("camembert-base") model.to(torch_device) input_ids = torch.tensor( [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]], device=torch_device, dtype=torch.long, ) # J'aime le camembert ! output = model(input_ids)["last_hidden_state"] expected_shape = torch.Size((1, 10, 768)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]], device=torch_device, dtype=torch.float, ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/legacy/seq2seq/train_mbart_cc25_enro.sh

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. python finetune_trainer.py \ --model_name_or_path=facebook/mbart-large-cc25 \ --data_dir $ENRO_DIR \ --output_dir mbart_cc25_enro --overwrite_output_dir \ --learning_rate=3e-5 \ --warmup_steps 500 \ --fp16 \ --label_smoothing 0.1 \ --adam_eps 1e-06 \ --src_lang en_XX --tgt_lang ro_RO \ --freeze_embeds \ --per_device_train_batch_size=4 --per_device_eval_batch_size=4 \ --max_source_length 128 --max_target_length 128 --val_max_target_length 128 --test_max_target_length 128\ --sortish_sampler \ --num_train_epochs 6 \ --save_steps 25000 --eval_steps 25000 --logging_steps 1000 \ --do_train --do_eval --do_predict \ --evaluation_strategy steps \ --predict_with_generate --logging_first_step \ --task translation \ "$@"

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. python finetune_trainer.py \ --model_name_or_path=facebook/mbart-large-cc25 \ --data_dir $ENRO_DIR \ --output_dir mbart_cc25_enro --overwrite_output_dir \ --learning_rate=3e-5 \ --warmup_steps 500 \ --fp16 \ --label_smoothing 0.1 \ --adam_eps 1e-06 \ --src_lang en_XX --tgt_lang ro_RO \ --freeze_embeds \ --per_device_train_batch_size=4 --per_device_eval_batch_size=4 \ --max_source_length 128 --max_target_length 128 --val_max_target_length 128 --test_max_target_length 128\ --sortish_sampler \ --num_train_epochs 6 \ --save_steps 25000 --eval_steps 25000 --logging_steps 1000 \ --do_train --do_eval --do_predict \ --evaluation_strategy steps \ --predict_with_generate --logging_first_step \ --task translation \ "$@"

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/vit_mae/test_modeling_tf_vit_mae.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow ViTMAE model. """ import copy import inspect import json import math import os import tempfile import unittest from importlib import import_module import numpy as np from transformers import ViTMAEConfig from transformers.file_utils import cached_property, is_tf_available, is_vision_available from transformers.testing_utils import require_tf, require_vision, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import TFViTMAEForPreTraining, TFViTMAEModel if is_vision_available(): from PIL import Image from transformers import ViTFeatureExtractor class TFViTMAEModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, mask_ratio=0.6, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.mask_ratio = mask_ratio self.scope = scope # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above # (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1))) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return ViTMAEConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, decoder_hidden_size=self.hidden_size, decoder_num_hidden_layers=self.num_hidden_layers, decoder_num_attention_heads=self.num_attention_heads, decoder_intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, ) def create_and_check_model(self, config, pixel_values, labels): model = TFViTMAEModel(config=config) result = model(pixel_values, training=False) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_pretraining(self, config, pixel_values, labels): model = TFViTMAEForPreTraining(config) result = model(pixel_values, training=False) # expected sequence length = num_patches num_patches = (self.image_size // self.patch_size) ** 2 expected_num_channels = self.patch_size**2 * self.num_channels self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) # test greyscale images config.num_channels = 1 model = TFViTMAEForPreTraining(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values, training=False) expected_num_channels = self.patch_size**2 self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, pixel_values, labels) = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_tf class TFViTMAEModelTest(TFModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else () test_pruning = False test_onnx = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = TFViTMAEModelTester(self) self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ViTMAE does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_keyword_and_dict_args(self): # make the mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs_dict = model(inputs, noise=noise) inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) outputs_keywords = model(**inputs_keywords, noise=noise) output_dict = outputs_dict[0].numpy() output_keywords = outputs_keywords[0].numpy() self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_numpy_arrays_inputs(self): # make the mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) def prepare_numpy_arrays(inputs_dict): inputs_np_dict = {} for k, v in inputs_dict.items(): if tf.is_tensor(v): inputs_np_dict[k] = v.numpy() else: inputs_np_dict[k] = np.array(k) return inputs_np_dict for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) inputs_np = prepare_numpy_arrays(inputs) output_for_dict_input = model(inputs_np, noise=noise) output_for_kw_input = model(**inputs_np, noise=noise) self.assert_outputs_same(output_for_dict_input, output_for_kw_input) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict): # make masks reproducible np.random.seed(2) num_patches = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) tf_noise = tf.constant(noise) # Add `noise` argument. # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument tf_inputs_dict["noise"] = tf_noise super().check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # overwrite from common since TFViTMAEForPretraining outputs loss along with # logits and mask indices. loss and mask indices are not suitable for integration # with other keras modules. def test_compile_tf_model(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy") for model_class in self.all_model_classes: # `pixel_values` implies that the input is an image inputs = tf.keras.Input( batch_shape=( 3, self.model_tester.num_channels, self.model_tester.image_size, self.model_tester.image_size, ), name="pixel_values", dtype="float32", ) # Prepare our model model = model_class(config) model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving. # Let's load it from the disk to be sure we can use pretrained weights with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) outputs_dict = model(inputs) hidden_states = outputs_dict[0] # `TFViTMAEForPreTraining` outputs are not recommended to be used for # downstream application. This is just to check if the outputs of # `TFViTMAEForPreTraining` can be integrated with other keras modules. if model_class.__name__ == "TFViTMAEForPreTraining": hidden_states = outputs_dict["logits"] # Add a dense layer on top to test integration with other keras modules outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states) # Compile extended model extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric]) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_keras_save_load(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_main_layer_classes = set( module_member for model_class in self.all_model_classes for module in (import_module(model_class.__module__),) for module_member_name in dir(module) if module_member_name.endswith("MainLayer") # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`. and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")] for module_member in (getattr(module, module_member_name),) if isinstance(module_member, type) and tf.keras.layers.Layer in module_member.__bases__ and getattr(module_member, "_keras_serializable", False) ) num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) noise = tf.convert_to_tensor(noise) inputs_dict.update({"noise": noise}) for main_layer_class in tf_main_layer_classes: main_layer = main_layer_class(config) symbolic_inputs = { name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items() } model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs)) outputs = model(inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "keras_model.h5") model.save(filepath) model = tf.keras.models.load_model( filepath, custom_objects={main_layer_class.__name__: main_layer_class} ) assert isinstance(model, tf.keras.Model) after_outputs = model(inputs_dict) self.assert_outputs_same(after_outputs, outputs) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test @slow def test_save_load(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) model_input = self._prepare_for_class(inputs_dict, model_class) outputs = model(model_input, noise=noise) if model_class.__name__ == "TFViTMAEModel": out_2 = outputs.last_hidden_state.numpy() out_2[np.isnan(out_2)] = 0 else: out_2 = outputs.logits.numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) after_outputs = model(model_input, noise=noise) if model_class.__name__ == "TFViTMAEModel": out_1 = after_outputs["last_hidden_state"].numpy() out_1[np.isnan(out_1)] = 0 else: out_1 = after_outputs["logits"].numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_save_load_config(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) model_inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(model_inputs, noise=noise) model_config = model.get_config() # make sure that returned config is jsonifiable, which is required by keras json.dumps(model_config) new_model = model_class.from_config(model.get_config()) # make sure it also accepts a normal config _ = model_class.from_config(model.config) _ = new_model(model_inputs) # Build model new_model.set_weights(model.get_weights()) after_outputs = new_model(model_inputs, noise=noise) self.assert_outputs_same(after_outputs, outputs) @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_determinism(self): pass @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""") def test_model_outputs_equivalence(self): pass @slow def test_model_from_pretrained(self): model = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224") self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_tf @require_vision class TFViTMAEModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None @slow def test_inference_for_pretraining(self): # make random mask reproducible across the PT and TF model np.random.seed(2) model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base") feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="tf") # prepare a noise vector that will be also used for testing the TF model # (this way we can ensure that the PT and TF models operate on the same inputs) vit_mae_config = ViTMAEConfig() num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2) noise = np.random.uniform(size=(1, num_patches)) # forward pass outputs = model(**inputs, noise=noise) # verify the logits expected_shape = tf.convert_to_tensor([1, 196, 768]) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.convert_to_tensor( [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] ) tf.debugging.assert_near(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow ViTMAE model. """ import copy import inspect import json import math import os import tempfile import unittest from importlib import import_module import numpy as np from transformers import ViTMAEConfig from transformers.file_utils import cached_property, is_tf_available, is_vision_available from transformers.testing_utils import require_tf, require_vision, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import TFViTMAEForPreTraining, TFViTMAEModel if is_vision_available(): from PIL import Image from transformers import ViTFeatureExtractor class TFViTMAEModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, mask_ratio=0.6, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.mask_ratio = mask_ratio self.scope = scope # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above # (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1))) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return ViTMAEConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, decoder_hidden_size=self.hidden_size, decoder_num_hidden_layers=self.num_hidden_layers, decoder_num_attention_heads=self.num_attention_heads, decoder_intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, ) def create_and_check_model(self, config, pixel_values, labels): model = TFViTMAEModel(config=config) result = model(pixel_values, training=False) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_pretraining(self, config, pixel_values, labels): model = TFViTMAEForPreTraining(config) result = model(pixel_values, training=False) # expected sequence length = num_patches num_patches = (self.image_size // self.patch_size) ** 2 expected_num_channels = self.patch_size**2 * self.num_channels self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) # test greyscale images config.num_channels = 1 model = TFViTMAEForPreTraining(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values, training=False) expected_num_channels = self.patch_size**2 self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, pixel_values, labels) = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_tf class TFViTMAEModelTest(TFModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else () test_pruning = False test_onnx = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = TFViTMAEModelTester(self) self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ViTMAE does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_keyword_and_dict_args(self): # make the mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs_dict = model(inputs, noise=noise) inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) outputs_keywords = model(**inputs_keywords, noise=noise) output_dict = outputs_dict[0].numpy() output_keywords = outputs_keywords[0].numpy() self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_numpy_arrays_inputs(self): # make the mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) def prepare_numpy_arrays(inputs_dict): inputs_np_dict = {} for k, v in inputs_dict.items(): if tf.is_tensor(v): inputs_np_dict[k] = v.numpy() else: inputs_np_dict[k] = np.array(k) return inputs_np_dict for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) inputs_np = prepare_numpy_arrays(inputs) output_for_dict_input = model(inputs_np, noise=noise) output_for_kw_input = model(**inputs_np, noise=noise) self.assert_outputs_same(output_for_dict_input, output_for_kw_input) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict): # make masks reproducible np.random.seed(2) num_patches = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) tf_noise = tf.constant(noise) # Add `noise` argument. # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument tf_inputs_dict["noise"] = tf_noise super().check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # overwrite from common since TFViTMAEForPretraining outputs loss along with # logits and mask indices. loss and mask indices are not suitable for integration # with other keras modules. def test_compile_tf_model(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy") for model_class in self.all_model_classes: # `pixel_values` implies that the input is an image inputs = tf.keras.Input( batch_shape=( 3, self.model_tester.num_channels, self.model_tester.image_size, self.model_tester.image_size, ), name="pixel_values", dtype="float32", ) # Prepare our model model = model_class(config) model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving. # Let's load it from the disk to be sure we can use pretrained weights with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) outputs_dict = model(inputs) hidden_states = outputs_dict[0] # `TFViTMAEForPreTraining` outputs are not recommended to be used for # downstream application. This is just to check if the outputs of # `TFViTMAEForPreTraining` can be integrated with other keras modules. if model_class.__name__ == "TFViTMAEForPreTraining": hidden_states = outputs_dict["logits"] # Add a dense layer on top to test integration with other keras modules outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states) # Compile extended model extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric]) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_keras_save_load(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_main_layer_classes = set( module_member for model_class in self.all_model_classes for module in (import_module(model_class.__module__),) for module_member_name in dir(module) if module_member_name.endswith("MainLayer") # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`. and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")] for module_member in (getattr(module, module_member_name),) if isinstance(module_member, type) and tf.keras.layers.Layer in module_member.__bases__ and getattr(module_member, "_keras_serializable", False) ) num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) noise = tf.convert_to_tensor(noise) inputs_dict.update({"noise": noise}) for main_layer_class in tf_main_layer_classes: main_layer = main_layer_class(config) symbolic_inputs = { name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items() } model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs)) outputs = model(inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "keras_model.h5") model.save(filepath) model = tf.keras.models.load_model( filepath, custom_objects={main_layer_class.__name__: main_layer_class} ) assert isinstance(model, tf.keras.Model) after_outputs = model(inputs_dict) self.assert_outputs_same(after_outputs, outputs) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test @slow def test_save_load(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) model_input = self._prepare_for_class(inputs_dict, model_class) outputs = model(model_input, noise=noise) if model_class.__name__ == "TFViTMAEModel": out_2 = outputs.last_hidden_state.numpy() out_2[np.isnan(out_2)] = 0 else: out_2 = outputs.logits.numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) after_outputs = model(model_input, noise=noise) if model_class.__name__ == "TFViTMAEModel": out_1 = after_outputs["last_hidden_state"].numpy() out_1[np.isnan(out_1)] = 0 else: out_1 = after_outputs["logits"].numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_save_load_config(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) model_inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(model_inputs, noise=noise) model_config = model.get_config() # make sure that returned config is jsonifiable, which is required by keras json.dumps(model_config) new_model = model_class.from_config(model.get_config()) # make sure it also accepts a normal config _ = model_class.from_config(model.config) _ = new_model(model_inputs) # Build model new_model.set_weights(model.get_weights()) after_outputs = new_model(model_inputs, noise=noise) self.assert_outputs_same(after_outputs, outputs) @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_determinism(self): pass @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""") def test_model_outputs_equivalence(self): pass @slow def test_model_from_pretrained(self): model = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224") self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_tf @require_vision class TFViTMAEModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None @slow def test_inference_for_pretraining(self): # make random mask reproducible across the PT and TF model np.random.seed(2) model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base") feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="tf") # prepare a noise vector that will be also used for testing the TF model # (this way we can ensure that the PT and TF models operate on the same inputs) vit_mae_config = ViTMAEConfig() num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2) noise = np.random.uniform(size=(1, num_patches)) # forward pass outputs = model(**inputs, noise=noise) # verify the logits expected_shape = tf.convert_to_tensor([1, 196, 768]) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.convert_to_tensor( [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] ) tf.debugging.assert_near(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./examples/research_projects/fsner/src/fsner/__init__.py

from .model import FSNERModel from .tokenizer_utils import FSNERTokenizerUtils __all__ = ["FSNERModel", "FSNERTokenizerUtils"]

from .model import FSNERModel from .tokenizer_utils import FSNERTokenizerUtils __all__ = ["FSNERModel", "FSNERTokenizerUtils"]

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./.git/hooks/applypatch-msg.sample

#!/bin/sh # # An example hook script to check the commit log message taken by # applypatch from an e-mail message. # # The hook should exit with non-zero status after issuing an # appropriate message if it wants to stop the commit. The hook is # allowed to edit the commit message file. # # To enable this hook, rename this file to "applypatch-msg". . git-sh-setup commitmsg="$(git rev-parse --git-path hooks/commit-msg)" test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"} :

#!/bin/sh # # An example hook script to check the commit log message taken by # applypatch from an e-mail message. # # The hook should exit with non-zero status after issuing an # appropriate message if it wants to stop the commit. The hook is # allowed to edit the commit message file. # # To enable this hook, rename this file to "applypatch-msg". . git-sh-setup commitmsg="$(git rev-parse --git-path hooks/commit-msg)" test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"} :

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/onnx/config.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import dataclasses import warnings from abc import ABC, abstractmethod from collections import OrderedDict from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union import numpy as np from packaging import version from ..utils import TensorType, is_torch_available, is_vision_available, logging from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size if TYPE_CHECKING: from ..configuration_utils import PretrainedConfig from ..feature_extraction_utils import FeatureExtractionMixin from ..image_processing_utils import ImageProcessingMixin from ..tokenization_utils_base import PreTrainedTokenizerBase if is_vision_available(): from PIL import Image logger = logging.get_logger(__name__) DEFAULT_ONNX_OPSET = 11 # 2 Gb EXTERNAL_DATA_FORMAT_SIZE_LIMIT = 2 * 1024 * 1024 * 1024 @dataclasses.dataclass class PatchingSpec: """ Data class that holds patching specifications. Args: o: Module / object where the op to patch is located name: Name of the op to monkey patch custom_op: Custom op that patches the original op orig_op: Original op that is being patched op_wrapper: Wrapper (optional) that wraps both the original and custom ops. It is useful for ops that are class or static methods for instance. """ o: Any name: str custom_op: Callable orig_op: Optional[Callable] = None op_wrapper: Optional[Callable] = None class OnnxConfig(ABC): """ Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format. """ default_fixed_batch = 2 default_fixed_sequence = 8 default_fixed_num_choices = 4 torch_onnx_minimum_version = version.parse("1.8") _tasks_to_common_outputs = { "causal-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "default": OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}}), "image-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "image-segmentation": OrderedDict( { "logits": {0: "batch", 1: "sequence"}, "pred_boxes": {0: "batch", 1: "sequence"}, "pred_masks": {0: "batch", 1: "sequence"}, } ), "masked-im": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "masked-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "multiple-choice": OrderedDict({"logits": {0: "batch"}}), "object-detection": OrderedDict( { "logits": {0: "batch", 1: "sequence"}, "pred_boxes": {0: "batch", 1: "sequence"}, } ), "question-answering": OrderedDict( { "start_logits": {0: "batch", 1: "sequence"}, "end_logits": {0: "batch", 1: "sequence"}, } ), "semantic-segmentation": OrderedDict({"logits": {0: "batch", 1: "num_labels", 2: "height", 3: "width"}}), "seq2seq-lm": OrderedDict({"logits": {0: "batch", 1: "decoder_sequence"}}), "sequence-classification": OrderedDict({"logits": {0: "batch"}}), "token-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "vision2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "speech2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), } def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None): self._config = config if task not in self._tasks_to_common_outputs: raise ValueError( f"{task} is not a supported task, supported tasks: {self._tasks_to_common_outputs.keys()}" ) self.task = task self._patching_specs = [] for spec in patching_specs if patching_specs is not None else []: final_spec = spec if spec.orig_op is None: final_spec = dataclasses.replace(spec, orig_op=getattr(spec.o, spec.name)) self._patching_specs.append(final_spec) @classmethod def from_model_config(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfig": """ Instantiate a OnnxConfig for a specific model Args: config: The model's configuration to use when exporting to ONNX Returns: OnnxConfig for this model """ return cls(config, task=task) @property @abstractmethod def inputs(self) -> Mapping[str, Mapping[int, str]]: """ Mapping containing the axis definition of the input tensors to provide to the model Returns: For each input: its name associated to the axes symbolic name and the axis position within the tensor """ raise NotImplementedError() @property def outputs(self) -> Mapping[str, Mapping[int, str]]: """ Mapping containing the axis definition of the output tensors to provide to the model Returns: For each output: its name associated to the axes symbolic name and the axis position within the tensor """ common_outputs = self._tasks_to_common_outputs[self.task] return copy.deepcopy(common_outputs) @property def values_override(self) -> Optional[Mapping[str, Any]]: """ Dictionary of keys to override in the model's config before exporting Returns: Dictionary with the keys (and their corresponding values) to override """ if hasattr(self._config, "use_cache"): return {"use_cache": False} return None @property def default_batch_size(self) -> int: """ The default batch size to use if no other indication Returns: Integer > 0 """ # Using 2 avoid ONNX making assumption about single sample batch return OnnxConfig.default_fixed_batch @property def default_sequence_length(self) -> int: """ The default sequence length to use if no other indication Returns: Integer > 0 """ return OnnxConfig.default_fixed_sequence @property def default_num_choices(self) -> int: """ The default number of choices to use if no other indication Returns: Integer > 0 """ return OnnxConfig.default_fixed_num_choices @property def default_onnx_opset(self) -> int: """ Which onnx opset to use when exporting the model Returns: Integer ONNX Opset version """ return DEFAULT_ONNX_OPSET @property def atol_for_validation(self) -> float: """ What absolute tolerance value to use during model conversion validation. Returns: Float absolute tolerance value. """ return 1e-5 @property def is_torch_support_available(self) -> bool: """ The minimum PyTorch version required to export the model. Returns: `bool`: Whether the installed version of PyTorch is compatible with the model. """ if is_torch_available(): from transformers.utils import torch_version return torch_version >= self.torch_onnx_minimum_version else: return False @staticmethod def use_external_data_format(num_parameters: int) -> bool: """ Flag indicating if the model requires using external data format Args: num_parameters: Number of parameter on the model Returns: True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise """ return ( compute_serialized_parameters_size(num_parameters, ParameterFormat.Float) >= EXTERNAL_DATA_FORMAT_SIZE_LIMIT ) def _generate_dummy_images( self, batch_size: int = 2, num_channels: int = 3, image_height: int = 40, image_width: int = 40 ): images = [] for _ in range(batch_size): data = np.random.rand(image_height, image_width, num_channels) * 255 images.append(Image.fromarray(data.astype("uint8")).convert("RGB")) return images def _generate_dummy_audio( self, batch_size: int = 2, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220 ): audio_data = [] for _ in range(batch_size): # time variable t = np.linspace(0, time_duration, int(time_duration * sampling_rate), endpoint=False) # generate pure sine wave at `frequency` Hz audio_data.append(0.5 * np.sin(2 * np.pi * frequency * t)) return audio_data def generate_dummy_inputs( self, preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"], batch_size: int = -1, seq_length: int = -1, num_choices: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, num_channels: int = 3, image_width: int = 40, image_height: int = 40, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220, tokenizer: "PreTrainedTokenizerBase" = None, ) -> Mapping[str, Any]: """ Generate inputs to provide to the ONNX exporter for the specific framework Args: preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]): The preprocessor associated with this model configuration. batch_size (`int`, *optional*, defaults to -1): The batch size to export the model for (-1 means dynamic axis). num_choices (`int`, *optional*, defaults to -1): The number of candidate answers provided for multiple choice task (-1 means dynamic axis). seq_length (`int`, *optional*, defaults to -1): The sequence length to export the model for (-1 means dynamic axis). is_pair (`bool`, *optional*, defaults to `False`): Indicate if the input is a pair (sentence 1, sentence 2) framework (`TensorType`, *optional*, defaults to `None`): The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for. num_channels (`int`, *optional*, defaults to 3): The number of channels of the generated images. image_width (`int`, *optional*, defaults to 40): The width of the generated images. image_height (`int`, *optional*, defaults to 40): The height of the generated images. sampling_rate (`int`, *optional* defaults to 22050) The sampling rate for audio data generation. time_duration (`float`, *optional* defaults to 5.0) Total seconds of sampling for audio data generation. frequency (`int`, *optional* defaults to 220) The desired natural frequency of generated audio. Returns: Mapping[str, Tensor] holding the kwargs to provide to the model's forward function """ from ..feature_extraction_utils import FeatureExtractionMixin from ..image_processing_utils import ImageProcessingMixin from ..tokenization_utils_base import PreTrainedTokenizerBase if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None: raise ValueError("You cannot provide both a tokenizer and a preprocessor to generate dummy inputs.") if tokenizer is not None: warnings.warn( "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use" " `preprocessor` instead.", FutureWarning, ) logger.warning("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.") preprocessor = tokenizer if isinstance(preprocessor, PreTrainedTokenizerBase): # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = preprocessor.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence input_token = ( preprocessor.unk_token if (preprocessor.unk_token is not None and len(preprocessor.unk_token) > 0) else "0" ) dummy_input = [" ".join([input_token]) * seq_length] * batch_size if self.task == "multiple-choice": # If dynamic axis (-1) we forward with a fixed dimension of 4 candidate answers to avoid optimizations # made by ONNX num_choices = compute_effective_axis_dimension( num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0 ) dummy_input = dummy_input * num_choices # The shape of the tokenized inputs values is [batch_size * num_choices, seq_length] tokenized_input = preprocessor(dummy_input, text_pair=dummy_input) # Unflatten the tokenized inputs values expanding it to the shape [batch_size, num_choices, seq_length] for k, v in tokenized_input.items(): tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)] return dict(tokenized_input.convert_to_tensors(tensor_type=framework)) return dict(preprocessor(dummy_input, return_tensors=framework)) elif isinstance(preprocessor, ImageProcessingMixin): if preprocessor.model_input_names[0] != "pixel_values": raise ValueError( f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects" f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}' ) # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) return dict(preprocessor(images=dummy_input, return_tensors=framework)) elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values": # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) return dict(preprocessor(images=dummy_input, return_tensors=framework)) elif ( isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "input_features" ): # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_input = self._generate_dummy_audio(batch_size, sampling_rate, time_duration, frequency) return dict(preprocessor(dummy_input, return_tensors=framework)) else: raise ValueError( "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." ) def generate_dummy_inputs_onnxruntime(self, reference_model_inputs: Mapping[str, Any]) -> Mapping[str, Any]: """ Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq models which have the encoder and decoder exported as separate ONNX files. Args: reference_model_inputs ([`Mapping[str, Tensor]`): Reference inputs for the model. Returns: `Mapping[str, Tensor]`: The mapping holding the kwargs to provide to the model's forward function """ return reference_model_inputs def patch_ops(self): for spec in self._patching_specs: custom_op = spec.custom_op if spec.op_wrapper is None else spec.op_wrapper(spec.custom_op) setattr(spec.o, spec.name, custom_op) def restore_ops(self): for spec in self._patching_specs: orig_op = spec.orig_op if spec.op_wrapper is None else spec.op_wrapper(spec.orig_op) setattr(spec.o, spec.name, orig_op) @classmethod def flatten_output_collection_property(cls, name: str, field: Iterable[Any]) -> Dict[str, Any]: """ Flatten any potential nested structure expanding the name of the field with the index of the element within the structure. Args: name: The name of the nested structure field: The structure to, potentially, be flattened Returns: (Dict[str, Any]): Outputs with flattened structure and key mapping this new structure. """ from itertools import chain return {f"{name}.{idx}": item for idx, item in enumerate(chain.from_iterable(field))} class OnnxConfigWithPast(OnnxConfig, ABC): def __init__( self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None, use_past: bool = False, ): super().__init__(config, task=task, patching_specs=patching_specs) self.use_past = use_past @classmethod def with_past(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfigWithPast": """ Instantiate a OnnxConfig with `use_past` attribute set to True Args: config: The underlying model's config to use when exporting to ONNX Returns: OnnxConfig with `.use_past = True` """ return cls(config, task=task, use_past=True) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: common_outputs = super().outputs if self.use_past: self.fill_with_past_key_values_(common_outputs, direction="outputs") return common_outputs @property def values_override(self) -> Optional[Mapping[str, Any]]: if hasattr(self._config, "use_cache"): return {"use_cache": self.use_past} return None @property def num_layers(self) -> int: """ The number of layers attribute retrieved from the model config. Override this for model configs where the number of layers attribute is not called `num_layers`. """ if not hasattr(self._config, "num_layers"): raise AttributeError( "could not find the number of layers attribute in the model configuration, override the num_layers" " property of the model OnnxConfig to solve this" ) return self._config.num_layers @property def num_attention_heads(self) -> int: """ The number of attention heads attribute retrieved from the model config. Override this for model configs where the number of attention heads attribute is not called `num_attention_heads`. """ if not hasattr(self._config, "num_attention_heads"): raise AttributeError( "could not find the number of attention heads attribute in the model configuration, override the" " num_attention_heads property of the model OnnxConfig to solve this" ) return self._config.num_attention_heads def generate_dummy_inputs( self, tokenizer: "PreTrainedTokenizerBase", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: # TODO: should we set seq_length = 1 when self.use_past = True? common_inputs = super().generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 shape = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) if "attention_mask" in common_inputs: mask_dtype = common_inputs["attention_mask"].dtype common_inputs["attention_mask"] = torch.cat( [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1, ) common_inputs["past_key_values"] = [] for _ in range(self.num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def fill_with_past_key_values_( self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str, inverted_values_shape: bool = False ): """ Fill the input_or_outputs mapping with past_key_values dynamic axes considering. Args: inputs_or_outputs: The mapping to fill. direction: either "inputs" or "outputs", it specifies whether input_or_outputs is the input mapping or the output mapping, this is important for axes naming. inverted_values_shape: If `True`, store values on dynamic axis 1, else on axis 2. """ if direction not in ["inputs", "outputs"]: raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given') name = "past_key_values" if direction == "inputs" else "present" for i in range(self.num_layers): inputs_or_outputs[f"{name}.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} if inverted_values_shape: inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 1: "past_sequence + sequence"} else: inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} def _flatten_past_key_values_(self, flattened_output, name, idx, t): flattened_output[f"{name}.{idx}.key"] = t[0] flattened_output[f"{name}.{idx}.value"] = t[1] def flatten_output_collection_property(self, name: str, field: Iterable[Any]) -> Dict[str, Any]: flattened_output = {} if name in ["present", "past_key_values"]: for idx, t in enumerate(field): self._flatten_past_key_values_(flattened_output, name, idx, t) else: flattened_output = super().flatten_output_collection_property(name, field) return flattened_output class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast): @property def outputs(self) -> Mapping[str, Mapping[int, str]]: common_outputs = super(OnnxConfigWithPast, self).outputs # Renaming the outputs axes properly. for name, axes_names in common_outputs.items(): sequence_name = "encoder_sequence" if "encoder" in name else "decoder_sequence" for axis_idx, name in axes_names.items(): if "sequence" in name: axes_names[axis_idx] = sequence_name # We reset the value as the order in common_outputs (OrderedDict) is lost otherwise else: axes_names[axis_idx] = name if self.use_past: self.fill_with_past_key_values_(common_outputs, direction="outputs") return common_outputs @property def num_layers(self) -> Tuple[int]: try: num_layers = super().num_layers num_layers = (num_layers, num_layers) except AttributeError: if hasattr(self._config, "encoder_layers") and hasattr(self._config, "decoder_layers"): num_layers = (self._config.encoder_layers, self._config.decoder_layers) else: raise AttributeError( "could not find the number of encoder and decoder layers attributes in the model configuration," " override the num_layers property of the model OnnxConfig to solve this" ) return num_layers @property def num_attention_heads(self) -> Tuple[int]: try: num_attention_heads = super().num_attention_heads num_attention_heads = (num_attention_heads, num_attention_heads) except AttributeError: if hasattr(self._config, "encoder_attention_heads") and hasattr(self._config, "decoder_attention_heads"): num_attention_heads = (self._config.encoder_attention_heads, self._config.decoder_attention_heads) else: raise AttributeError( "could not find the number of attention heads for the encoder and the decoder attributes in the" " model configuration, override the num_attention_heads property of the model OnnxConfig to solve" " this" ) return num_attention_heads def generate_dummy_inputs( self, tokenizer: "PreTrainedTokenizerBase", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: encoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) # Generate decoder inputs decoder_seq_length = seq_length if not self.use_past else 1 decoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=decoder_seq_length, is_pair=is_pair, framework=framework ) decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} common_inputs = dict(**encoder_inputs, **decoder_inputs) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch = common_inputs["input_ids"].shape[0] encoder_seq_length = common_inputs["input_ids"].shape[1] decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads encoder_shape = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) decoder_shape = ( batch, num_decoder_attention_heads, # Not using the same length for past_key_values decoder_seq_length + 3, self._config.hidden_size // num_decoder_attention_heads, ) common_inputs["past_key_values"] = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(min_num_layers): # For encoder-decoder models, past_key_values contains pre-computed values for both the encoder and the # decoder layers, hence a tuple of 4 tensors instead of 2 common_inputs["past_key_values"].append( ( torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape), ) ) # TODO: test this. shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(min_num_layers, max_num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str): if direction not in ["inputs", "outputs"]: raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given') name = "past_key_values" if direction == "inputs" else "present" # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" encoder_sequence = "past_encoder_sequence" decoder_sequence = "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence" for i in range(min_num_layers): inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence} inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence} inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence} inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence} for i in range(min_num_layers, max_num_layers): if remaining_side_name == "encoder": axes_info = {0: "batch", 2: encoder_sequence} else: axes_info = {0: "batch", 2: decoder_sequence} inputs_or_outputs[f"{name}.{i}.{remaining_side_name}.key"] = axes_info def _flatten_past_key_values_(self, flattened_output, name, idx, t): flattened_output[f"{name}.{idx}.decoder.key"] = t[0] flattened_output[f"{name}.{idx}.decoder.value"] = t[1] flattened_output[f"{name}.{idx}.encoder.key"] = t[2] flattened_output[f"{name}.{idx}.encoder.value"] = t[3]

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import dataclasses import warnings from abc import ABC, abstractmethod from collections import OrderedDict from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union import numpy as np from packaging import version from ..utils import TensorType, is_torch_available, is_vision_available, logging from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size if TYPE_CHECKING: from ..configuration_utils import PretrainedConfig from ..feature_extraction_utils import FeatureExtractionMixin from ..image_processing_utils import ImageProcessingMixin from ..tokenization_utils_base import PreTrainedTokenizerBase if is_vision_available(): from PIL import Image logger = logging.get_logger(__name__) DEFAULT_ONNX_OPSET = 11 # 2 Gb EXTERNAL_DATA_FORMAT_SIZE_LIMIT = 2 * 1024 * 1024 * 1024 @dataclasses.dataclass class PatchingSpec: """ Data class that holds patching specifications. Args: o: Module / object where the op to patch is located name: Name of the op to monkey patch custom_op: Custom op that patches the original op orig_op: Original op that is being patched op_wrapper: Wrapper (optional) that wraps both the original and custom ops. It is useful for ops that are class or static methods for instance. """ o: Any name: str custom_op: Callable orig_op: Optional[Callable] = None op_wrapper: Optional[Callable] = None class OnnxConfig(ABC): """ Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format. """ default_fixed_batch = 2 default_fixed_sequence = 8 default_fixed_num_choices = 4 torch_onnx_minimum_version = version.parse("1.8") _tasks_to_common_outputs = { "causal-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "default": OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}}), "image-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "image-segmentation": OrderedDict( { "logits": {0: "batch", 1: "sequence"}, "pred_boxes": {0: "batch", 1: "sequence"}, "pred_masks": {0: "batch", 1: "sequence"}, } ), "masked-im": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "masked-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "multiple-choice": OrderedDict({"logits": {0: "batch"}}), "object-detection": OrderedDict( { "logits": {0: "batch", 1: "sequence"}, "pred_boxes": {0: "batch", 1: "sequence"}, } ), "question-answering": OrderedDict( { "start_logits": {0: "batch", 1: "sequence"}, "end_logits": {0: "batch", 1: "sequence"}, } ), "semantic-segmentation": OrderedDict({"logits": {0: "batch", 1: "num_labels", 2: "height", 3: "width"}}), "seq2seq-lm": OrderedDict({"logits": {0: "batch", 1: "decoder_sequence"}}), "sequence-classification": OrderedDict({"logits": {0: "batch"}}), "token-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "vision2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), "speech2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}), } def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None): self._config = config if task not in self._tasks_to_common_outputs: raise ValueError( f"{task} is not a supported task, supported tasks: {self._tasks_to_common_outputs.keys()}" ) self.task = task self._patching_specs = [] for spec in patching_specs if patching_specs is not None else []: final_spec = spec if spec.orig_op is None: final_spec = dataclasses.replace(spec, orig_op=getattr(spec.o, spec.name)) self._patching_specs.append(final_spec) @classmethod def from_model_config(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfig": """ Instantiate a OnnxConfig for a specific model Args: config: The model's configuration to use when exporting to ONNX Returns: OnnxConfig for this model """ return cls(config, task=task) @property @abstractmethod def inputs(self) -> Mapping[str, Mapping[int, str]]: """ Mapping containing the axis definition of the input tensors to provide to the model Returns: For each input: its name associated to the axes symbolic name and the axis position within the tensor """ raise NotImplementedError() @property def outputs(self) -> Mapping[str, Mapping[int, str]]: """ Mapping containing the axis definition of the output tensors to provide to the model Returns: For each output: its name associated to the axes symbolic name and the axis position within the tensor """ common_outputs = self._tasks_to_common_outputs[self.task] return copy.deepcopy(common_outputs) @property def values_override(self) -> Optional[Mapping[str, Any]]: """ Dictionary of keys to override in the model's config before exporting Returns: Dictionary with the keys (and their corresponding values) to override """ if hasattr(self._config, "use_cache"): return {"use_cache": False} return None @property def default_batch_size(self) -> int: """ The default batch size to use if no other indication Returns: Integer > 0 """ # Using 2 avoid ONNX making assumption about single sample batch return OnnxConfig.default_fixed_batch @property def default_sequence_length(self) -> int: """ The default sequence length to use if no other indication Returns: Integer > 0 """ return OnnxConfig.default_fixed_sequence @property def default_num_choices(self) -> int: """ The default number of choices to use if no other indication Returns: Integer > 0 """ return OnnxConfig.default_fixed_num_choices @property def default_onnx_opset(self) -> int: """ Which onnx opset to use when exporting the model Returns: Integer ONNX Opset version """ return DEFAULT_ONNX_OPSET @property def atol_for_validation(self) -> float: """ What absolute tolerance value to use during model conversion validation. Returns: Float absolute tolerance value. """ return 1e-5 @property def is_torch_support_available(self) -> bool: """ The minimum PyTorch version required to export the model. Returns: `bool`: Whether the installed version of PyTorch is compatible with the model. """ if is_torch_available(): from transformers.utils import torch_version return torch_version >= self.torch_onnx_minimum_version else: return False @staticmethod def use_external_data_format(num_parameters: int) -> bool: """ Flag indicating if the model requires using external data format Args: num_parameters: Number of parameter on the model Returns: True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise """ return ( compute_serialized_parameters_size(num_parameters, ParameterFormat.Float) >= EXTERNAL_DATA_FORMAT_SIZE_LIMIT ) def _generate_dummy_images( self, batch_size: int = 2, num_channels: int = 3, image_height: int = 40, image_width: int = 40 ): images = [] for _ in range(batch_size): data = np.random.rand(image_height, image_width, num_channels) * 255 images.append(Image.fromarray(data.astype("uint8")).convert("RGB")) return images def _generate_dummy_audio( self, batch_size: int = 2, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220 ): audio_data = [] for _ in range(batch_size): # time variable t = np.linspace(0, time_duration, int(time_duration * sampling_rate), endpoint=False) # generate pure sine wave at `frequency` Hz audio_data.append(0.5 * np.sin(2 * np.pi * frequency * t)) return audio_data def generate_dummy_inputs( self, preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"], batch_size: int = -1, seq_length: int = -1, num_choices: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, num_channels: int = 3, image_width: int = 40, image_height: int = 40, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220, tokenizer: "PreTrainedTokenizerBase" = None, ) -> Mapping[str, Any]: """ Generate inputs to provide to the ONNX exporter for the specific framework Args: preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]): The preprocessor associated with this model configuration. batch_size (`int`, *optional*, defaults to -1): The batch size to export the model for (-1 means dynamic axis). num_choices (`int`, *optional*, defaults to -1): The number of candidate answers provided for multiple choice task (-1 means dynamic axis). seq_length (`int`, *optional*, defaults to -1): The sequence length to export the model for (-1 means dynamic axis). is_pair (`bool`, *optional*, defaults to `False`): Indicate if the input is a pair (sentence 1, sentence 2) framework (`TensorType`, *optional*, defaults to `None`): The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for. num_channels (`int`, *optional*, defaults to 3): The number of channels of the generated images. image_width (`int`, *optional*, defaults to 40): The width of the generated images. image_height (`int`, *optional*, defaults to 40): The height of the generated images. sampling_rate (`int`, *optional* defaults to 22050) The sampling rate for audio data generation. time_duration (`float`, *optional* defaults to 5.0) Total seconds of sampling for audio data generation. frequency (`int`, *optional* defaults to 220) The desired natural frequency of generated audio. Returns: Mapping[str, Tensor] holding the kwargs to provide to the model's forward function """ from ..feature_extraction_utils import FeatureExtractionMixin from ..image_processing_utils import ImageProcessingMixin from ..tokenization_utils_base import PreTrainedTokenizerBase if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None: raise ValueError("You cannot provide both a tokenizer and a preprocessor to generate dummy inputs.") if tokenizer is not None: warnings.warn( "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use" " `preprocessor` instead.", FutureWarning, ) logger.warning("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.") preprocessor = tokenizer if isinstance(preprocessor, PreTrainedTokenizerBase): # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = preprocessor.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence input_token = ( preprocessor.unk_token if (preprocessor.unk_token is not None and len(preprocessor.unk_token) > 0) else "0" ) dummy_input = [" ".join([input_token]) * seq_length] * batch_size if self.task == "multiple-choice": # If dynamic axis (-1) we forward with a fixed dimension of 4 candidate answers to avoid optimizations # made by ONNX num_choices = compute_effective_axis_dimension( num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0 ) dummy_input = dummy_input * num_choices # The shape of the tokenized inputs values is [batch_size * num_choices, seq_length] tokenized_input = preprocessor(dummy_input, text_pair=dummy_input) # Unflatten the tokenized inputs values expanding it to the shape [batch_size, num_choices, seq_length] for k, v in tokenized_input.items(): tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)] return dict(tokenized_input.convert_to_tensors(tensor_type=framework)) return dict(preprocessor(dummy_input, return_tensors=framework)) elif isinstance(preprocessor, ImageProcessingMixin): if preprocessor.model_input_names[0] != "pixel_values": raise ValueError( f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects" f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}' ) # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) return dict(preprocessor(images=dummy_input, return_tensors=framework)) elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values": # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) return dict(preprocessor(images=dummy_input, return_tensors=framework)) elif ( isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "input_features" ): # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_input = self._generate_dummy_audio(batch_size, sampling_rate, time_duration, frequency) return dict(preprocessor(dummy_input, return_tensors=framework)) else: raise ValueError( "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." ) def generate_dummy_inputs_onnxruntime(self, reference_model_inputs: Mapping[str, Any]) -> Mapping[str, Any]: """ Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq models which have the encoder and decoder exported as separate ONNX files. Args: reference_model_inputs ([`Mapping[str, Tensor]`): Reference inputs for the model. Returns: `Mapping[str, Tensor]`: The mapping holding the kwargs to provide to the model's forward function """ return reference_model_inputs def patch_ops(self): for spec in self._patching_specs: custom_op = spec.custom_op if spec.op_wrapper is None else spec.op_wrapper(spec.custom_op) setattr(spec.o, spec.name, custom_op) def restore_ops(self): for spec in self._patching_specs: orig_op = spec.orig_op if spec.op_wrapper is None else spec.op_wrapper(spec.orig_op) setattr(spec.o, spec.name, orig_op) @classmethod def flatten_output_collection_property(cls, name: str, field: Iterable[Any]) -> Dict[str, Any]: """ Flatten any potential nested structure expanding the name of the field with the index of the element within the structure. Args: name: The name of the nested structure field: The structure to, potentially, be flattened Returns: (Dict[str, Any]): Outputs with flattened structure and key mapping this new structure. """ from itertools import chain return {f"{name}.{idx}": item for idx, item in enumerate(chain.from_iterable(field))} class OnnxConfigWithPast(OnnxConfig, ABC): def __init__( self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None, use_past: bool = False, ): super().__init__(config, task=task, patching_specs=patching_specs) self.use_past = use_past @classmethod def with_past(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfigWithPast": """ Instantiate a OnnxConfig with `use_past` attribute set to True Args: config: The underlying model's config to use when exporting to ONNX Returns: OnnxConfig with `.use_past = True` """ return cls(config, task=task, use_past=True) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: common_outputs = super().outputs if self.use_past: self.fill_with_past_key_values_(common_outputs, direction="outputs") return common_outputs @property def values_override(self) -> Optional[Mapping[str, Any]]: if hasattr(self._config, "use_cache"): return {"use_cache": self.use_past} return None @property def num_layers(self) -> int: """ The number of layers attribute retrieved from the model config. Override this for model configs where the number of layers attribute is not called `num_layers`. """ if not hasattr(self._config, "num_layers"): raise AttributeError( "could not find the number of layers attribute in the model configuration, override the num_layers" " property of the model OnnxConfig to solve this" ) return self._config.num_layers @property def num_attention_heads(self) -> int: """ The number of attention heads attribute retrieved from the model config. Override this for model configs where the number of attention heads attribute is not called `num_attention_heads`. """ if not hasattr(self._config, "num_attention_heads"): raise AttributeError( "could not find the number of attention heads attribute in the model configuration, override the" " num_attention_heads property of the model OnnxConfig to solve this" ) return self._config.num_attention_heads def generate_dummy_inputs( self, tokenizer: "PreTrainedTokenizerBase", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: # TODO: should we set seq_length = 1 when self.use_past = True? common_inputs = super().generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 shape = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) if "attention_mask" in common_inputs: mask_dtype = common_inputs["attention_mask"].dtype common_inputs["attention_mask"] = torch.cat( [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1, ) common_inputs["past_key_values"] = [] for _ in range(self.num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def fill_with_past_key_values_( self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str, inverted_values_shape: bool = False ): """ Fill the input_or_outputs mapping with past_key_values dynamic axes considering. Args: inputs_or_outputs: The mapping to fill. direction: either "inputs" or "outputs", it specifies whether input_or_outputs is the input mapping or the output mapping, this is important for axes naming. inverted_values_shape: If `True`, store values on dynamic axis 1, else on axis 2. """ if direction not in ["inputs", "outputs"]: raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given') name = "past_key_values" if direction == "inputs" else "present" for i in range(self.num_layers): inputs_or_outputs[f"{name}.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} if inverted_values_shape: inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 1: "past_sequence + sequence"} else: inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} def _flatten_past_key_values_(self, flattened_output, name, idx, t): flattened_output[f"{name}.{idx}.key"] = t[0] flattened_output[f"{name}.{idx}.value"] = t[1] def flatten_output_collection_property(self, name: str, field: Iterable[Any]) -> Dict[str, Any]: flattened_output = {} if name in ["present", "past_key_values"]: for idx, t in enumerate(field): self._flatten_past_key_values_(flattened_output, name, idx, t) else: flattened_output = super().flatten_output_collection_property(name, field) return flattened_output class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast): @property def outputs(self) -> Mapping[str, Mapping[int, str]]: common_outputs = super(OnnxConfigWithPast, self).outputs # Renaming the outputs axes properly. for name, axes_names in common_outputs.items(): sequence_name = "encoder_sequence" if "encoder" in name else "decoder_sequence" for axis_idx, name in axes_names.items(): if "sequence" in name: axes_names[axis_idx] = sequence_name # We reset the value as the order in common_outputs (OrderedDict) is lost otherwise else: axes_names[axis_idx] = name if self.use_past: self.fill_with_past_key_values_(common_outputs, direction="outputs") return common_outputs @property def num_layers(self) -> Tuple[int]: try: num_layers = super().num_layers num_layers = (num_layers, num_layers) except AttributeError: if hasattr(self._config, "encoder_layers") and hasattr(self._config, "decoder_layers"): num_layers = (self._config.encoder_layers, self._config.decoder_layers) else: raise AttributeError( "could not find the number of encoder and decoder layers attributes in the model configuration," " override the num_layers property of the model OnnxConfig to solve this" ) return num_layers @property def num_attention_heads(self) -> Tuple[int]: try: num_attention_heads = super().num_attention_heads num_attention_heads = (num_attention_heads, num_attention_heads) except AttributeError: if hasattr(self._config, "encoder_attention_heads") and hasattr(self._config, "decoder_attention_heads"): num_attention_heads = (self._config.encoder_attention_heads, self._config.decoder_attention_heads) else: raise AttributeError( "could not find the number of attention heads for the encoder and the decoder attributes in the" " model configuration, override the num_attention_heads property of the model OnnxConfig to solve" " this" ) return num_attention_heads def generate_dummy_inputs( self, tokenizer: "PreTrainedTokenizerBase", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: encoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) # Generate decoder inputs decoder_seq_length = seq_length if not self.use_past else 1 decoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=decoder_seq_length, is_pair=is_pair, framework=framework ) decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} common_inputs = dict(**encoder_inputs, **decoder_inputs) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch = common_inputs["input_ids"].shape[0] encoder_seq_length = common_inputs["input_ids"].shape[1] decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads encoder_shape = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) decoder_shape = ( batch, num_decoder_attention_heads, # Not using the same length for past_key_values decoder_seq_length + 3, self._config.hidden_size // num_decoder_attention_heads, ) common_inputs["past_key_values"] = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(min_num_layers): # For encoder-decoder models, past_key_values contains pre-computed values for both the encoder and the # decoder layers, hence a tuple of 4 tensors instead of 2 common_inputs["past_key_values"].append( ( torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape), ) ) # TODO: test this. shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(min_num_layers, max_num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str): if direction not in ["inputs", "outputs"]: raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given') name = "past_key_values" if direction == "inputs" else "present" # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" encoder_sequence = "past_encoder_sequence" decoder_sequence = "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence" for i in range(min_num_layers): inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence} inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence} inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence} inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence} for i in range(min_num_layers, max_num_layers): if remaining_side_name == "encoder": axes_info = {0: "batch", 2: encoder_sequence} else: axes_info = {0: "batch", 2: decoder_sequence} inputs_or_outputs[f"{name}.{i}.{remaining_side_name}.key"] = axes_info def _flatten_past_key_values_(self, flattened_output, name, idx, t): flattened_output[f"{name}.{idx}.decoder.key"] = t[0] flattened_output[f"{name}.{idx}.decoder.value"] = t[1] flattened_output[f"{name}.{idx}.encoder.key"] = t[2] flattened_output[f"{name}.{idx}.encoder.value"] = t[3]

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/deit/test_feature_extraction_deit.py

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import DeiTFeatureExtractor class DeiTFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 20, "width": 20} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_feat_extract_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } @require_torch @require_vision class DeiTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = DeiTFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = DeiTFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "do_center_crop")) self.assertTrue(hasattr(feature_extractor, "center_crop")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL images image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), )

# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import DeiTFeatureExtractor class DeiTFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 20, "width": 20} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_feat_extract_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } @require_torch @require_vision class DeiTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = DeiTFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = DeiTFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "do_center_crop")) self.assertTrue(hasattr(feature_extractor, "center_crop")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL images image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/auto/modeling_flax_auto.py

# coding=utf-8 # Copyright 2018 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class.""" from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES logger = logging.get_logger(__name__) FLAX_MODEL_MAPPING_NAMES = OrderedDict( [ # Base model mapping ("albert", "FlaxAlbertModel"), ("bart", "FlaxBartModel"), ("beit", "FlaxBeitModel"), ("bert", "FlaxBertModel"), ("big_bird", "FlaxBigBirdModel"), ("blenderbot", "FlaxBlenderbotModel"), ("blenderbot-small", "FlaxBlenderbotSmallModel"), ("clip", "FlaxCLIPModel"), ("distilbert", "FlaxDistilBertModel"), ("electra", "FlaxElectraModel"), ("gpt2", "FlaxGPT2Model"), ("gpt_neo", "FlaxGPTNeoModel"), ("gptj", "FlaxGPTJModel"), ("longt5", "FlaxLongT5Model"), ("marian", "FlaxMarianModel"), ("mbart", "FlaxMBartModel"), ("mt5", "FlaxMT5Model"), ("opt", "FlaxOPTModel"), ("pegasus", "FlaxPegasusModel"), ("roberta", "FlaxRobertaModel"), ("roformer", "FlaxRoFormerModel"), ("t5", "FlaxT5Model"), ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"), ("vit", "FlaxViTModel"), ("wav2vec2", "FlaxWav2Vec2Model"), ("xglm", "FlaxXGLMModel"), ("xlm-roberta", "FlaxXLMRobertaModel"), ] ) FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( [ # Model for pre-training mapping ("albert", "FlaxAlbertForPreTraining"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForPreTraining"), ("big_bird", "FlaxBigBirdForPreTraining"), ("electra", "FlaxElectraForPreTraining"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("t5", "FlaxT5ForConditionalGeneration"), ("wav2vec2", "FlaxWav2Vec2ForPreTraining"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict( [ # Model for Masked LM mapping ("albert", "FlaxAlbertForMaskedLM"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForMaskedLM"), ("big_bird", "FlaxBigBirdForMaskedLM"), ("distilbert", "FlaxDistilBertForMaskedLM"), ("electra", "FlaxElectraForMaskedLM"), ("mbart", "FlaxMBartForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Seq2Seq Causal LM mapping ("bart", "FlaxBartForConditionalGeneration"), ("blenderbot", "FlaxBlenderbotForConditionalGeneration"), ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"), ("encoder-decoder", "FlaxEncoderDecoderModel"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("marian", "FlaxMarianMTModel"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("pegasus", "FlaxPegasusForConditionalGeneration"), ("t5", "FlaxT5ForConditionalGeneration"), ] ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Image-classsification ("beit", "FlaxBeitForImageClassification"), ("vit", "FlaxViTForImageClassification"), ] ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"), ] ) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Causal LM mapping ("bart", "FlaxBartForCausalLM"), ("bert", "FlaxBertForCausalLM"), ("big_bird", "FlaxBigBirdForCausalLM"), ("electra", "FlaxElectraForCausalLM"), ("gpt2", "FlaxGPT2LMHeadModel"), ("gpt_neo", "FlaxGPTNeoForCausalLM"), ("gptj", "FlaxGPTJForCausalLM"), ("opt", "FlaxOPTForCausalLM"), ("roberta", "FlaxRobertaForCausalLM"), ("xglm", "FlaxXGLMForCausalLM"), ] ) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Sequence Classification mapping ("albert", "FlaxAlbertForSequenceClassification"), ("bart", "FlaxBartForSequenceClassification"), ("bert", "FlaxBertForSequenceClassification"), ("big_bird", "FlaxBigBirdForSequenceClassification"), ("distilbert", "FlaxDistilBertForSequenceClassification"), ("electra", "FlaxElectraForSequenceClassification"), ("mbart", "FlaxMBartForSequenceClassification"), ("roberta", "FlaxRobertaForSequenceClassification"), ("roformer", "FlaxRoFormerForSequenceClassification"), ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"), ] ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Question Answering mapping ("albert", "FlaxAlbertForQuestionAnswering"), ("bart", "FlaxBartForQuestionAnswering"), ("bert", "FlaxBertForQuestionAnswering"), ("big_bird", "FlaxBigBirdForQuestionAnswering"), ("distilbert", "FlaxDistilBertForQuestionAnswering"), ("electra", "FlaxElectraForQuestionAnswering"), ("mbart", "FlaxMBartForQuestionAnswering"), ("roberta", "FlaxRobertaForQuestionAnswering"), ("roformer", "FlaxRoFormerForQuestionAnswering"), ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"), ] ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Token Classification mapping ("albert", "FlaxAlbertForTokenClassification"), ("bert", "FlaxBertForTokenClassification"), ("big_bird", "FlaxBigBirdForTokenClassification"), ("distilbert", "FlaxDistilBertForTokenClassification"), ("electra", "FlaxElectraForTokenClassification"), ("roberta", "FlaxRobertaForTokenClassification"), ("roformer", "FlaxRoFormerForTokenClassification"), ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"), ] ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict( [ # Model for Multiple Choice mapping ("albert", "FlaxAlbertForMultipleChoice"), ("bert", "FlaxBertForMultipleChoice"), ("big_bird", "FlaxBigBirdForMultipleChoice"), ("distilbert", "FlaxDistilBertForMultipleChoice"), ("electra", "FlaxElectraForMultipleChoice"), ("roberta", "FlaxRobertaForMultipleChoice"), ("roformer", "FlaxRoFormerForMultipleChoice"), ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"), ] ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict( [ ("bert", "FlaxBertForNextSentencePrediction"), ] ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"), ] ) FLAX_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES) FLAX_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES) FLAX_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES ) class FlaxAutoModel(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_MAPPING FlaxAutoModel = auto_class_update(FlaxAutoModel) class FlaxAutoModelForPreTraining(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING FlaxAutoModelForPreTraining = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining") class FlaxAutoModelForCausalLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING FlaxAutoModelForCausalLM = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling") class FlaxAutoModelForMaskedLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING FlaxAutoModelForMaskedLM = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling") class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING FlaxAutoModelForSeq2SeqLM = auto_class_update( FlaxAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base" ) class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING FlaxAutoModelForSequenceClassification = auto_class_update( FlaxAutoModelForSequenceClassification, head_doc="sequence classification" ) class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING FlaxAutoModelForQuestionAnswering = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering") class FlaxAutoModelForTokenClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING FlaxAutoModelForTokenClassification = auto_class_update( FlaxAutoModelForTokenClassification, head_doc="token classification" ) class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING FlaxAutoModelForMultipleChoice = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice") class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING FlaxAutoModelForNextSentencePrediction = auto_class_update( FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction" ) class FlaxAutoModelForImageClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING FlaxAutoModelForImageClassification = auto_class_update( FlaxAutoModelForImageClassification, head_doc="image classification" ) class FlaxAutoModelForVision2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING FlaxAutoModelForVision2Seq = auto_class_update(FlaxAutoModelForVision2Seq, head_doc="vision-to-text modeling") class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING FlaxAutoModelForSpeechSeq2Seq = auto_class_update( FlaxAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" )

# coding=utf-8 # Copyright 2018 The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class.""" from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES logger = logging.get_logger(__name__) FLAX_MODEL_MAPPING_NAMES = OrderedDict( [ # Base model mapping ("albert", "FlaxAlbertModel"), ("bart", "FlaxBartModel"), ("beit", "FlaxBeitModel"), ("bert", "FlaxBertModel"), ("big_bird", "FlaxBigBirdModel"), ("blenderbot", "FlaxBlenderbotModel"), ("blenderbot-small", "FlaxBlenderbotSmallModel"), ("clip", "FlaxCLIPModel"), ("distilbert", "FlaxDistilBertModel"), ("electra", "FlaxElectraModel"), ("gpt2", "FlaxGPT2Model"), ("gpt_neo", "FlaxGPTNeoModel"), ("gptj", "FlaxGPTJModel"), ("longt5", "FlaxLongT5Model"), ("marian", "FlaxMarianModel"), ("mbart", "FlaxMBartModel"), ("mt5", "FlaxMT5Model"), ("opt", "FlaxOPTModel"), ("pegasus", "FlaxPegasusModel"), ("roberta", "FlaxRobertaModel"), ("roformer", "FlaxRoFormerModel"), ("t5", "FlaxT5Model"), ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"), ("vit", "FlaxViTModel"), ("wav2vec2", "FlaxWav2Vec2Model"), ("xglm", "FlaxXGLMModel"), ("xlm-roberta", "FlaxXLMRobertaModel"), ] ) FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( [ # Model for pre-training mapping ("albert", "FlaxAlbertForPreTraining"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForPreTraining"), ("big_bird", "FlaxBigBirdForPreTraining"), ("electra", "FlaxElectraForPreTraining"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("t5", "FlaxT5ForConditionalGeneration"), ("wav2vec2", "FlaxWav2Vec2ForPreTraining"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict( [ # Model for Masked LM mapping ("albert", "FlaxAlbertForMaskedLM"), ("bart", "FlaxBartForConditionalGeneration"), ("bert", "FlaxBertForMaskedLM"), ("big_bird", "FlaxBigBirdForMaskedLM"), ("distilbert", "FlaxDistilBertForMaskedLM"), ("electra", "FlaxElectraForMaskedLM"), ("mbart", "FlaxMBartForConditionalGeneration"), ("roberta", "FlaxRobertaForMaskedLM"), ("roformer", "FlaxRoFormerForMaskedLM"), ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"), ] ) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Seq2Seq Causal LM mapping ("bart", "FlaxBartForConditionalGeneration"), ("blenderbot", "FlaxBlenderbotForConditionalGeneration"), ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"), ("encoder-decoder", "FlaxEncoderDecoderModel"), ("longt5", "FlaxLongT5ForConditionalGeneration"), ("marian", "FlaxMarianMTModel"), ("mbart", "FlaxMBartForConditionalGeneration"), ("mt5", "FlaxMT5ForConditionalGeneration"), ("pegasus", "FlaxPegasusForConditionalGeneration"), ("t5", "FlaxT5ForConditionalGeneration"), ] ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Image-classsification ("beit", "FlaxBeitForImageClassification"), ("vit", "FlaxViTForImageClassification"), ] ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"), ] ) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Causal LM mapping ("bart", "FlaxBartForCausalLM"), ("bert", "FlaxBertForCausalLM"), ("big_bird", "FlaxBigBirdForCausalLM"), ("electra", "FlaxElectraForCausalLM"), ("gpt2", "FlaxGPT2LMHeadModel"), ("gpt_neo", "FlaxGPTNeoForCausalLM"), ("gptj", "FlaxGPTJForCausalLM"), ("opt", "FlaxOPTForCausalLM"), ("roberta", "FlaxRobertaForCausalLM"), ("xglm", "FlaxXGLMForCausalLM"), ] ) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Sequence Classification mapping ("albert", "FlaxAlbertForSequenceClassification"), ("bart", "FlaxBartForSequenceClassification"), ("bert", "FlaxBertForSequenceClassification"), ("big_bird", "FlaxBigBirdForSequenceClassification"), ("distilbert", "FlaxDistilBertForSequenceClassification"), ("electra", "FlaxElectraForSequenceClassification"), ("mbart", "FlaxMBartForSequenceClassification"), ("roberta", "FlaxRobertaForSequenceClassification"), ("roformer", "FlaxRoFormerForSequenceClassification"), ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"), ] ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Question Answering mapping ("albert", "FlaxAlbertForQuestionAnswering"), ("bart", "FlaxBartForQuestionAnswering"), ("bert", "FlaxBertForQuestionAnswering"), ("big_bird", "FlaxBigBirdForQuestionAnswering"), ("distilbert", "FlaxDistilBertForQuestionAnswering"), ("electra", "FlaxElectraForQuestionAnswering"), ("mbart", "FlaxMBartForQuestionAnswering"), ("roberta", "FlaxRobertaForQuestionAnswering"), ("roformer", "FlaxRoFormerForQuestionAnswering"), ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"), ] ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Token Classification mapping ("albert", "FlaxAlbertForTokenClassification"), ("bert", "FlaxBertForTokenClassification"), ("big_bird", "FlaxBigBirdForTokenClassification"), ("distilbert", "FlaxDistilBertForTokenClassification"), ("electra", "FlaxElectraForTokenClassification"), ("roberta", "FlaxRobertaForTokenClassification"), ("roformer", "FlaxRoFormerForTokenClassification"), ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"), ] ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict( [ # Model for Multiple Choice mapping ("albert", "FlaxAlbertForMultipleChoice"), ("bert", "FlaxBertForMultipleChoice"), ("big_bird", "FlaxBigBirdForMultipleChoice"), ("distilbert", "FlaxDistilBertForMultipleChoice"), ("electra", "FlaxElectraForMultipleChoice"), ("roberta", "FlaxRobertaForMultipleChoice"), ("roformer", "FlaxRoFormerForMultipleChoice"), ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"), ] ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict( [ ("bert", "FlaxBertForNextSentencePrediction"), ] ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"), ] ) FLAX_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES) FLAX_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES) FLAX_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES ) FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES) FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES ) FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES ) FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES ) FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES ) FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES ) class FlaxAutoModel(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_MAPPING FlaxAutoModel = auto_class_update(FlaxAutoModel) class FlaxAutoModelForPreTraining(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING FlaxAutoModelForPreTraining = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining") class FlaxAutoModelForCausalLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING FlaxAutoModelForCausalLM = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling") class FlaxAutoModelForMaskedLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING FlaxAutoModelForMaskedLM = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling") class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING FlaxAutoModelForSeq2SeqLM = auto_class_update( FlaxAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base" ) class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING FlaxAutoModelForSequenceClassification = auto_class_update( FlaxAutoModelForSequenceClassification, head_doc="sequence classification" ) class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING FlaxAutoModelForQuestionAnswering = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering") class FlaxAutoModelForTokenClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING FlaxAutoModelForTokenClassification = auto_class_update( FlaxAutoModelForTokenClassification, head_doc="token classification" ) class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING FlaxAutoModelForMultipleChoice = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice") class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING FlaxAutoModelForNextSentencePrediction = auto_class_update( FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction" ) class FlaxAutoModelForImageClassification(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING FlaxAutoModelForImageClassification = auto_class_update( FlaxAutoModelForImageClassification, head_doc="image classification" ) class FlaxAutoModelForVision2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING FlaxAutoModelForVision2Seq = auto_class_update(FlaxAutoModelForVision2Seq, head_doc="vision-to-text modeling") class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass): _model_mapping = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING FlaxAutoModelForSpeechSeq2Seq = auto_class_update( FlaxAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/mbart/test_tokenization_mbart.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, ) from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): from transformers.models.mbart.modeling_mbart import shift_tokens_right EN_CODE = 250004 RO_CODE = 250020 @require_sentencepiece @require_tokenizers class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = MBartTokenizer rust_tokenizer_class = MBartTokenizerFast test_rust_tokenizer = True test_sentencepiece = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.save_pretrained(self.tmpdirname) def test_full_tokenizer(self): tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4] # ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^ ], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ], ) # overwrite from test_tokenization_common to speed up test def test_save_pretrained(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files + the tokenizer.json file for the fast one self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f) self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key)) # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id")) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=True tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=False tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it saved the tokenizer.json file self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) @require_torch @require_sentencepiece @require_tokenizers class MBartEnroIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/mbart-large-en-ro" src_text = [ " UN Chief Says There Is No Military Solution in Syria", """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""", ] tgt_text = [ "Şeful ONU declară că nu există o soluţie militară în Siria", "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei" ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor' " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.", ] expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE] @classmethod def setUpClass(cls): cls.tokenizer: MBartTokenizer = MBartTokenizer.from_pretrained( cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO" ) cls.pad_token_id = 1 return cls def check_language_codes(self): self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020) def test_enro_tokenizer_batch_encode_plus(self): ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0] self.assertListEqual(self.expected_src_tokens, ids) def test_enro_tokenizer_decode_ignores_language_codes(self): self.assertIn(RO_CODE, self.tokenizer.all_special_ids) generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2] result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) self.assertEqual(result, expected_romanian) self.assertNotIn(self.tokenizer.eos_token, result) def test_enro_tokenizer_truncation(self): src_text = ["this is gunna be a long sentence " * 20] assert isinstance(src_text[0], str) desired_max_length = 10 ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0] self.assertEqual(ids[-2], 2) self.assertEqual(ids[-1], EN_CODE) self.assertEqual(len(ids), desired_max_length) def test_mask_token(self): self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250026, 250001]) def test_special_tokens_unaffacted_by_save_load(self): tmpdirname = tempfile.mkdtemp() original_special_tokens = self.tokenizer.fairseq_tokens_to_ids self.tokenizer.save_pretrained(tmpdirname) new_tok = MBartTokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens) @require_torch def test_batch_fairseq_parity(self): batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt") batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id) # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE] assert batch.decoder_input_ids[1][0].tolist() == RO_CODE assert batch.decoder_input_ids[1][-1] == 2 assert batch.labels[1][-2:].tolist() == [2, RO_CODE] @require_torch def test_enro_tokenizer_prepare_batch(self): batch = self.tokenizer( self.src_text, text_target=self.tgt_text, padding=True, truncation=True, max_length=len(self.expected_src_tokens), return_tensors="pt", ) batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id) self.assertIsInstance(batch, BatchEncoding) self.assertEqual((2, 14), batch.input_ids.shape) self.assertEqual((2, 14), batch.attention_mask.shape) result = batch.input_ids.tolist()[0] self.assertListEqual(self.expected_src_tokens, result) self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS # Test that special tokens are reset self.assertEqual(self.tokenizer.prefix_tokens, []) self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE]) def test_seq2seq_max_length(self): batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt") targets = self.tokenizer( text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt" ) labels = targets["input_ids"] batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.decoder_input_ids.shape[1], 10) @require_torch def test_tokenizer_translation(self): inputs = self.tokenizer._build_translation_inputs( "A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR" ) self.assertEqual( nested_simplify(inputs), { # A, test, EOS, en_XX "input_ids": [[62, 3034, 2, 250004]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 250001, }, )

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, ) from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): from transformers.models.mbart.modeling_mbart import shift_tokens_right EN_CODE = 250004 RO_CODE = 250020 @require_sentencepiece @require_tokenizers class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = MBartTokenizer rust_tokenizer_class = MBartTokenizerFast test_rust_tokenizer = True test_sentencepiece = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.save_pretrained(self.tmpdirname) def test_full_tokenizer(self): tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4] # ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^ ], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ], ) # overwrite from test_tokenization_common to speed up test def test_save_pretrained(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files + the tokenizer.json file for the fast one self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f) self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key)) # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id")) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=True tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=False tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it saved the tokenizer.json file self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) @require_torch @require_sentencepiece @require_tokenizers class MBartEnroIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/mbart-large-en-ro" src_text = [ " UN Chief Says There Is No Military Solution in Syria", """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""", ] tgt_text = [ "Şeful ONU declară că nu există o soluţie militară în Siria", "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei" ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor' " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.", ] expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE] @classmethod def setUpClass(cls): cls.tokenizer: MBartTokenizer = MBartTokenizer.from_pretrained( cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO" ) cls.pad_token_id = 1 return cls def check_language_codes(self): self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020) def test_enro_tokenizer_batch_encode_plus(self): ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0] self.assertListEqual(self.expected_src_tokens, ids) def test_enro_tokenizer_decode_ignores_language_codes(self): self.assertIn(RO_CODE, self.tokenizer.all_special_ids) generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2] result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) self.assertEqual(result, expected_romanian) self.assertNotIn(self.tokenizer.eos_token, result) def test_enro_tokenizer_truncation(self): src_text = ["this is gunna be a long sentence " * 20] assert isinstance(src_text[0], str) desired_max_length = 10 ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0] self.assertEqual(ids[-2], 2) self.assertEqual(ids[-1], EN_CODE) self.assertEqual(len(ids), desired_max_length) def test_mask_token(self): self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250026, 250001]) def test_special_tokens_unaffacted_by_save_load(self): tmpdirname = tempfile.mkdtemp() original_special_tokens = self.tokenizer.fairseq_tokens_to_ids self.tokenizer.save_pretrained(tmpdirname) new_tok = MBartTokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens) @require_torch def test_batch_fairseq_parity(self): batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt") batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id) # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE] assert batch.decoder_input_ids[1][0].tolist() == RO_CODE assert batch.decoder_input_ids[1][-1] == 2 assert batch.labels[1][-2:].tolist() == [2, RO_CODE] @require_torch def test_enro_tokenizer_prepare_batch(self): batch = self.tokenizer( self.src_text, text_target=self.tgt_text, padding=True, truncation=True, max_length=len(self.expected_src_tokens), return_tensors="pt", ) batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id) self.assertIsInstance(batch, BatchEncoding) self.assertEqual((2, 14), batch.input_ids.shape) self.assertEqual((2, 14), batch.attention_mask.shape) result = batch.input_ids.tolist()[0] self.assertListEqual(self.expected_src_tokens, result) self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS # Test that special tokens are reset self.assertEqual(self.tokenizer.prefix_tokens, []) self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE]) def test_seq2seq_max_length(self): batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt") targets = self.tokenizer( text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt" ) labels = targets["input_ids"] batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.decoder_input_ids.shape[1], 10) @require_torch def test_tokenizer_translation(self): inputs = self.tokenizer._build_translation_inputs( "A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR" ) self.assertEqual( nested_simplify(inputs), { # A, test, EOS, en_XX "input_ids": [[62, 3034, 2, 250004]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 250001, }, )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/flava/modeling_flava.py

# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch FLAVA model.""" import collections import math from collections import OrderedDict from dataclasses import dataclass from typing import Any, Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from transformers.utils.doc import add_code_sample_docstrings from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_flava import ( FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/flava-full" # Codebook docstring _CHECKPOINT_FOR_CODEBOOK_DOC = "facebook/flava-image-codebook" _FEAT_EXTRACTOR_FOR_DOC = "FlavaFeatureExtractor" _CONFIG_CLASS_FOR_IMAGE_MODEL_DOC = "FlavaImageConfig" _CONFIG_CLASS_FOR_TEXT_MODEL_DOC = "FlavaTextConfig" _CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC = "FlavaMultimodalConfig" _TOKENIZER_FOR_DOC = "BertTokenizer" _EXPECTED_IMAGE_OUTPUT_SHAPE = [1, 197, 768] FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/flava-full", # See all flava models at https://huggingface.co/models?filter=flava ] FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST = ["facebook/flava-image-codebook"] LOGIT_SCALE_CLAMP_MIN = 0 LOGIT_SCALE_CLAMP_MAX = 4.6052 FlavaPossibleConfigs = Union[FlavaTextConfig, FlavaImageConfig, FlavaMultimodalConfig] @dataclass class FlavaModelOutput(ModelOutput): """ Output from FlavaModel containing embeddings and outputs from individual encoders. Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. """ image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_output", "image_output", "multimodal_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class FlavaLosses(ModelOutput): """Class representing pretraining losses from FLAVA model Args: mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.: Masked Image Modeling loss as used in BeIT calculated only for unimodal image data. mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.: Masked Language Modeling loss as used in BERT calculated only for unimodal text data. itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.: Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on masked pairs in FLAVA. global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.: Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text data. This is calculated on unmasked images and texts. mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.: Masked Multimodal Modeling loss's image component calculated on paired image-text data. mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.: Masked Multimodal Modeling loss's text component calculated on paired image-text data. """ mim: Optional[torch.FloatTensor] = None mlm: Optional[torch.FloatTensor] = None itm: Optional[torch.FloatTensor] = None global_contrastive: Optional[torch.FloatTensor] = None mmm_image: Optional[torch.FloatTensor] = None mmm_text: Optional[torch.FloatTensor] = None def all_none(self) -> bool: all_none = True for v in self.values(): if v is not None: all_none = False break return all_none @dataclass class FlavaForPreTrainingOutput(ModelOutput): """ Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders. Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True): Total loss calculated for this model. loss_info (`FlavaLosses`): Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on the keys. image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present): The output of the [`FlavaTextModel`]. multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_masked_output (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present): The output of the [`FlavaMultimodalModel`]. mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not): The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not): The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present): The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA. mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's `image_projection` and `text_projection` layers respectively. This represents the image-text similarity scores. This is calculated on unmasked images and texts. contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and texts. """ loss: Optional[torch.FloatTensor] = None loss_info: FlavaLosses = None image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None image_masked_embeddings: Optional[torch.FloatTensor] = None image_masked_output: Optional[BaseModelOutputWithPooling] = None text_masked_embeddings: Optional[torch.FloatTensor] = None text_masked_output: Optional[BaseModelOutputWithPooling] = None multimodal_masked_embeddings: Optional[torch.FloatTensor] = None multimodal_masked_output: Optional[BaseModelOutputWithPooling] = None mim_logits: Optional[torch.FloatTensor] = None mlm_logits: Optional[torch.FloatTensor] = None itm_logits: Optional[torch.FloatTensor] = None contrastive_logits_per_image: Optional[torch.FloatTensor] = None contrastive_logits_per_text: Optional[torch.FloatTensor] = None mmm_image_logits: Optional[torch.FloatTensor] = None mmm_text_logits: Optional[torch.FloatTensor] = None def to_tuple(self) -> Tuple[Any]: transformer_outputs = [ "text_output", "image_output", "multimodal_output", "text_masked_output", "image_masked_output", "multimodal_masked_output", ] return tuple(self[k] if k not in transformer_outputs else getattr(self, k).to_tuple() for k in self.keys()) # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class FlavaImageEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None: super().__init__() use_mask_token = use_mask_token or config.mask_token self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = PatchEmbeddings( image_size=config.image_size, patch_size=config.patch_size, num_channels=config.num_channels, embed_dim=config.hidden_size, ) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/image_transformer.py#L174 """ npatch = embeddings.shape[1] - 1 num_pos = self.position_embeddings.shape[1] - 1 if npatch == num_pos and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] num_h_patches = height // self.config.patch_size num_w_patches = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1 patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(math.sqrt(num_pos)), int(math.sqrt(num_pos)), dim).permute(0, 3, 1, 2), scale_factor=(num_h_patches / math.sqrt(num_pos), num_w_patches / math.sqrt(num_pos)), mode="bicubic", align_corners=False, ) if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]: raise ValueError( f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the " f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})" ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) batch_size, seq_len, _ = embeddings.size() if bool_masked_pos is not None: mask_tokens = self.mask_token.expand(batch_size, seq_len, -1) # B X H X W = B X HW if bool_masked_pos.dim() == 3: bool_masked_pos = bool_masked_pos.view(bool_masked_pos.size(0), -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class PatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__( self, image_size: int = 224, patch_size: Union[int, Tuple[int, int]] = 16, num_channels: int = 3, embed_dim: int = 768, ): super().__init__() if not isinstance(image_size, collections.abc.Iterable): image_size = (image_size, image_size) if not isinstance(patch_size, collections.abc.Iterable): patch_size = (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) x = self.projection(pixel_values).flatten(2).transpose(1, 2) return x class FlavaTextEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, ): input_shape = input_ids.size() seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class FlavaSelfAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class FlavaSelfOutput(nn.Module): """ The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class FlavaAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.attention = FlavaSelfAttention(config) self.output = FlavaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention( hidden_states, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class FlavaIntermediate(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act # Copied from transformers.models.vit.modeling_vit.ViTIntermediate.forward def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class FlavaOutput(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # Copied from transformers.models.vit.modeling_vit.ViTOutput.forward def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class FlavaLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = FlavaAttention(config) self.intermediate = FlavaIntermediate(config) self.output = FlavaOutput(config) # TODO: Check fp32 layer norm possiblity self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class FlavaEncoder(nn.Module): def __init__(self, config: FlavaConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions ) class FlavaPooler(nn.Module): def __init__(self, config: FlavaPossibleConfigs): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output FLAVA_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`{config}`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ FLAVA_INPUTS_DOCSTRING_COMMON = r""" attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ FLAVA_IMAGE_INPUTS_DOCSTRING_BASE = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`FlavaFeatureExtractor`]. See [`FlavaFeatureExtractor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. """ FLAVA_IMAGE_INPUTS_DOCSTRING = FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_TEXT_INPUTS_DOCSTRING_BASE = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) """ FLAVA_TEXT_INPUTS_DOCSTRING = FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_MULTIMODAL_INPUTS_DOCSTRING = ( r""" Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`): The concatenated hidden states of unimodal encoders. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_MODEL_INPUTS_DOCSTRING_BASE = r""" Args: skip_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used. """ FLAVA_MODEL_INPUTS_DOCSTRING = ( FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON + FLAVA_MODEL_INPUTS_DOCSTRING_BASE ) FLAVA_PRETRAINING_INPUTS_DOCSTRING = ( r""" Args: input_ids_masked (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task to be used with MLM. Indices can be obtained using [`BertTokenizer`] along with [`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) """ + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + r""" image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*): Mask to avoid performing attention on padding token indices specifically for images. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) skip_unmasked_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked multimodal embeddings or outputs as of now. mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*): Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction). Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., text_config.vocab_size - 1]`. mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*): Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ..., image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are generated automatically using the image codebook assigned to the model. By default, it uses [`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels. itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*): Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match. The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well. return_loss (`bool`, *optional*, default to None): Whether to return calculated loss or not. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_PRETRAINING_START_DOCSTRING_EXTRA = r""" Parameters: image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will be initialized using the image_codebook_config defined in the config first as the first parameter. """ class FlavaPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FlavaConfig base_model_prefix = "flava" supports_gradient_checkpointing = True def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: FlavaEncoder, value: bool = False) -> None: if isinstance(module, FlavaEncoder): module.gradient_checkpointing = value @add_start_docstrings( "The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaImageConfig"), ) class FlavaImageModel(FlavaPreTrainedModel): config_class = FlavaImageConfig # This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.image_model" main_input_name = "pixel_values" def __init__(self, config: FlavaImageConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaImageEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> nn.Module: return self.embeddings.patch_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.patch_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC, modality="vision", expected_output=_EXPECTED_IMAGE_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaTextConfig"), ) class FlavaTextModel(FlavaPreTrainedModel): config_class = FlavaTextConfig # This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.text_model" def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaTextEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> PatchEmbeddings: return self.embeddings.word_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify input_ids") input_shape = input_ids.size() if attention_mask is None: attention_mask = torch.ones(input_shape, device=input_ids.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, input_shape, input_ids.device ) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaMultimodalConfig"), ) class FlavaMultimodalModel(FlavaPreTrainedModel): config_class = FlavaMultimodalConfig # This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.multimodal_model" main_input_name = "hidden_states" def __init__(self, config: FlavaMultimodalConfig, add_pooling_layer=True): super().__init__(config) self.config = config self.use_cls_token = self.config.use_cls_token if self.use_cls_token: self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward( FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC, ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, seq_length, _ = hidden_states.size() if self.use_cls_token: cls_tokens = self.cls_token.expand(batch_size, -1, -1) hidden_states = torch.cat((cls_tokens, hidden_states), dim=1) seq_length += 1 if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, (batch_size, seq_length), hidden_states.device ) encoder_outputs = self.encoder( hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaConfig"), ) class FlavaModel(FlavaPreTrainedModel): config_class = FlavaConfig def __init__(self, config: FlavaConfig): super().__init__(config) if not isinstance(config.text_config, FlavaTextConfig): raise ValueError( "config.text_config is expected to be of type FlavaTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.image_config, FlavaImageConfig): raise ValueError( "config.image_config is expected to be of type FlavaImageConfig but is of type" f" {type(config.image_config)}." ) if not isinstance(config.multimodal_config, FlavaMultimodalConfig): raise ValueError( "config.multimodal_config is expected to be of type FlavaMultimodalConfig but " + f"is of type {type(config.multimodal_config)}." ) text_config = config.text_config image_config = config.image_config multimodal_config = config.multimodal_config self.projection_dim = config.projection_dim self.text_hidden_size = text_config.hidden_size self.image_hidden_size = image_config.hidden_size self.mm_hidden_size = multimodal_config.hidden_size self.text_model = FlavaTextModel(text_config) self.image_model = FlavaImageModel(image_config) self.multimodal_model = FlavaMultimodalModel(multimodal_config) self.image_projection = nn.Linear(self.image_hidden_size, self.projection_dim) self.text_projection = nn.Linear(self.text_hidden_size, self.projection_dim) self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value) self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size) self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlavaTextModel`]. Examples: ```python >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[0] # last_hidden_state text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) def get_image_features( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlavaImageModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) image_outputs = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) pooled_output = image_outputs[0] # last_hidden_state image_features = self.image_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward( FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_multimodal_encoder: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, ) -> Union[Tuple, FlavaOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True) >>> outputs = model(**inputs) >>> logits_per_image = outputs.contrastive_logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ``` """ return_dict = return_dict if return_dict is not None else self.config.return_dict if not output_hidden_states: raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`") image_embeddings = None image_states = None image_mm_projection = None image_output = None if pixel_values is not None: image_output = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=image_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeddings, image_states = image_output[0], image_output[2] # Note that these states don't use final layernorm in the transformer model image_mm_projection = self.image_to_mm_projection(image_states[-1]) text_embeddings = None text_states = None text_mm_projection = None text_output = None if input_ids is not None: text_output = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_embeddings, text_states = text_output[0], text_output[2] # Note that these states don't use final layernorm in the transformer model text_mm_projection = self.text_to_mm_projection(text_states[-1]) multimodal_embeddings = None multimodal_output = None if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder: multimodal_input = torch.cat([image_mm_projection, text_mm_projection], dim=1) multimodal_output = self.multimodal_model(multimodal_input, return_dict=return_dict) multimodal_embeddings = multimodal_output[0] if not return_dict: return ( image_embeddings, image_output, text_embeddings, text_output, multimodal_embeddings, multimodal_output, ) return FlavaModelOutput( image_embeddings=image_embeddings, image_output=image_output, text_embeddings=text_embeddings, text_output=text_output, multimodal_embeddings=multimodal_embeddings, multimodal_output=multimodal_output, ) class FlavaImageCodebookResPath(nn.Module): def __init__(self, in_size: int, out_size: int, **kwargs): super().__init__() hid_size = out_size // 4 path = OrderedDict() path["relu_1"] = nn.ReLU() path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1) path["relu_2"] = nn.ReLU() path["conv_2"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_3"] = nn.ReLU() path["conv_3"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_4"] = nn.ReLU() path["conv_4"] = nn.Conv2d(hid_size, out_size, kernel_size=1, padding=0) self.path = nn.Sequential(path) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.path(x) class FlavaImageCodebookBlock(nn.Module): def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs): super().__init__() self.post_gain = 1 / (num_layers**2) if in_size != out_size: self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0) else: self.id_path = nn.Identity() self.res_path = FlavaImageCodebookResPath(in_size, out_size) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.id_path(x) + self.post_gain * self.res_path(x) class FlavaImageCodebookLayerGroup(nn.Module): def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True): super().__init__() blocks = OrderedDict() for i in range(num_blocks): if i == 0: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(in_size, out_size, num_layers) else: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(out_size, out_size, num_layers) if use_pool: blocks["pool"] = nn.MaxPool2d(kernel_size=2) self.group = nn.Sequential(blocks) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.group(x) # Inspired by DALLE Encoder in https://github.com/openai/DALL-E/blob/5be4b236bc3ade6943662354117a0e83752cc322/dall_e/encoder.py#L42 @add_start_docstrings( """ The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use `get_codebook_indices` to get image tokens for an image. """, FLAVA_START_DOCSTRING.format(config="FlavaImageCodebookConfig"), ) class FlavaImageCodebook(FlavaPreTrainedModel): base_model_prefix = "" config_class = FlavaImageCodebookConfig main_input_name = "pixel_values" supports_gradient_checkpointing = False def __init__( self, config: FlavaImageCodebookConfig, **kwargs: Any, ): super().__init__(config) self.config = config self.num_groups = config.num_groups self.input_channels = config.input_channels self.num_blocks_per_group = config.num_blocks_per_group self.hidden_size = config.hidden_size self.vocab_size = config.vocab_size num_layers = self.num_groups * self.num_blocks_per_group output_blocks = OrderedDict() output_blocks["relu"] = nn.ReLU() output_blocks["conv"] = nn.Conv2d(8 * self.hidden_size, self.vocab_size, kernel_size=1, padding=0) blocks = OrderedDict() blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3) blocks["group_1"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size ) blocks["group_2"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 2 * self.hidden_size ) blocks["group_3"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 2 * self.hidden_size, 4 * self.hidden_size ) blocks["group_4"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 4 * self.hidden_size, 8 * self.hidden_size, use_pool=False ) blocks["output"] = nn.Sequential(output_blocks) self.blocks = nn.Sequential(blocks) self.post_init() if self.config.freeze: for param in self.parameters(): param.requires_grad = False def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model.get_codebook_indices(**inputs) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) z_logits = self.blocks(pixel_values) return torch.argmax(z_logits, axis=1) def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor: z_logits = self.blocks(pixel_values) return nn.Softmax(dim=1)(z_logits) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model(**inputs) >>> print(outputs.shape) (1, 196) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) if len(pixel_values.shape) != 4: raise ValueError(f"input shape {pixel_values.shape} is not 4d") if pixel_values.shape[1] != self.input_channels: raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}") return self.blocks(pixel_values) class FlavaPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class FlavaMaskedPredictionHead(nn.Module): def __init__(self, config, weight=None): super().__init__() self.config = config self.transform = FlavaPredictionHeadTransform(config) self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) if weight is not None: self.decoder.weight = weight # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, x): x = self.transform(x) x = self.decoder(x) return x class FlavaITMHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.pooler = FlavaPooler(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, x): x = self.pooler(x) x = self.seq_relationship(x) return x class FlavaGlobalContrastiveHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.global_backprop_contrastive = config.global_backprop_contrastive def forward(self, image_embeddings, text_embeddings, logit_scale): temperature = torch.exp(logit_scale) if not torch.distributed.is_available() or not torch.distributed.is_initialized(): labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device) image_embeddings_all = [image_embeddings] text_embeddings_all = [text_embeddings] else: local_batch_size = image_embeddings.size(0) world_size = torch.distributed.get_world_size() if self.global_backprop_contrastive: image_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(image_embeddings) text_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(text_embeddings) else: image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)] text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)] torch.distributed.all_gather(image_embeddings_all, image_embeddings) torch.distributed.all_gather(text_embeddings_all, text_embeddings) labels = local_batch_size * torch.distributed.get_rank() + torch.arange( local_batch_size, device=image_embeddings.device ) image_embeddings_all = torch.cat(image_embeddings_all) text_embeddings_all = torch.cat(text_embeddings_all) logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature return logits_per_image, logits_per_text, labels @add_start_docstrings( """ The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs. """, FLAVA_START_DOCSTRING.format(config="FlavaConfig") + FLAVA_PRETRAINING_START_DOCSTRING_EXTRA, ) class FlavaForPreTraining(FlavaPreTrainedModel): # Those are linked to xxx.bias _keys_to_ignore_on_load_missing = [ "mmm_text_head.decoder.bias", "mmm_image_head.decoder.bias", "mlm_head.decoder.bias", "mim_head.decoder.bias", ] def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None): super().__init__(config) self.flava = FlavaModel(config) self.image_codebook = image_codebook if self.image_codebook is None and config.init_codebook: self.image_codebook = FlavaImageCodebook(config.image_codebook_config) # Levarage text and image encoder configs to create the masked # head since it has the right vocab self.mim_head = FlavaMaskedPredictionHead(config.image_config) self.mlm_head = FlavaMaskedPredictionHead(config.text_config) self.itm_head = FlavaITMHead(config) self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config) self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config) self.global_contrastive_head = FlavaGlobalContrastiveHead(config) self.image_vocab_size = config.image_config.vocab_size self.text_vocab_size = config.text_config.vocab_size self.mlm_weight = config.mlm_weight self.mim_weight = config.mim_weight self.global_contrastive_weight = config.global_contrastive_weight self.ce_ignore_index = config.ce_ignore_index self.itm_weight = config.itm_weight self.mmm_image_weight = config.mmm_image_weight self.mmm_text_weight = config.mmm_text_weight self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder self.post_init() def _resize_to_2d(self, x: torch.Tensor): if x.dim() > 2: x = x.view(x.size(0), -1) return x @add_start_docstrings_to_model_forward( FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches") ) @replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, input_ids_masked: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, codebook_pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_unmasked_multimodal_encoder: bool = None, mlm_labels: Optional[torch.Tensor] = None, mim_labels: Optional[torch.Tensor] = None, itm_labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, return_loss: Optional[bool] = None, ): """ Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaForPreTraining, FlavaProcessor >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> text = ["a photo of a cat"] >>> inputs = processor( ... images=[image], ... text=text, ... return_masks=True, ... return_codebook_pixels=True, ... padding=True, ... max_length=77, ... return_tensors="pt", ... ) >>> output = model(**inputs) ``` Return: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict return_loss = return_loss if return_loss is not None else self.config.return_loss skip_unmasked_multimodal_encoder = ( skip_unmasked_multimodal_encoder if skip_unmasked_multimodal_encoder is not None else self.skip_unmasked_multimodal_encoder ) if input_ids_masked is None and input_ids is not None: logger.warning( "`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to" " `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if" " you are doing inference on unmasked text..." ) input_ids_masked = input_ids flava_output = self.flava( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, image_attention_mask=image_attention_mask, # Don't need unmasked multimodal embedding for anything so skip it # NOTE: ITM uses masked version skip_multimodal_encoder=skip_unmasked_multimodal_encoder, output_attentions=output_attentions, output_hidden_states=output_hidden_states, # Pass true to have deterministic outputs return_dict=True, ) flava_masked_output = self.flava( input_ids=input_ids_masked, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, image_attention_mask=image_attention_mask, bool_masked_pos=bool_masked_pos, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, ) pos_mask = None image_embeddings = flava_output.image_embeddings text_embeddings = flava_output.text_embeddings image_masked_embeddings = flava_masked_output.image_embeddings text_masked_embeddings = flava_masked_output.text_embeddings multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None itm_logits = logits_per_image = logits_per_text = None # Calculate mim_labels if necessary from the image_codebook if image_masked_embeddings is not None or multimodal_masked_embeddings is not None: if mim_labels is None and return_loss: if self.image_codebook is None: raise RuntimeError( "`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` " " have been passed. Reinstantiate the model with `init_codebook` set to True or " "pass in your custom `mim_labels`" ) if codebook_pixel_values is None: raise ValueError( "`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. " "Call `FlavaProcessor` with `return_codebook_pixels` set to True" ) mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values) # Unimodal MIM Loss # If multimodal embeddings are present, we will calculate MMM loss if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_image = image_masked_embeddings if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :] masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mim_logits = self.mim_head(sequence_for_image) if return_loss: mim_loss = nn.functional.cross_entropy( mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mim_loss *= self.mim_weight else: mim_logits = self.mim_head(sequence_for_image) # Unimodal MLM Loss if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_text = text_masked_embeddings if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :] masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mlm_logits = self.mlm_head(sequence_for_text) if return_loss: mlm_loss = nn.functional.cross_entropy( mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mlm_loss *= self.mlm_weight else: mlm_logits = self.mlm_head(sequence_for_text) # ITM Loss if self.itm_weight > 0 and multimodal_masked_embeddings is not None: itm_logits = self.itm_head(multimodal_masked_embeddings) if itm_labels is not None: pos_pairs = itm_labels.ne(0) pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True])) if return_loss: itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels) itm_loss *= self.itm_weight if multimodal_masked_embeddings is not None: multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask] if mlm_labels is not None: mlm_labels = mlm_labels[pos_mask] if mim_labels is not None: mim_labels = mim_labels[pos_mask] # MMM Image Loss if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0: sequence_for_image = multimodal_masked_embeddings end_index = image_masked_embeddings.size(1) - 1 sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :] if pos_mask is not None: sequence_for_image = sequence_for_image[pos_mask] if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mmm_image_logits = self.mmm_image_head(sequence_for_image) if return_loss: mmm_image_loss = nn.functional.cross_entropy( mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mmm_image_loss *= self.mmm_image_weight else: mmm_image_logits = self.mmm_image_head(sequence_for_image) # MMM Text Loss if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0: sequence_for_text = multimodal_masked_embeddings sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :] if pos_mask is not None: sequence_for_text = sequence_for_text[pos_mask] if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mmm_text_logits = self.mmm_text_head(sequence_for_text) if return_loss: mmm_text_loss = nn.functional.cross_entropy( mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mmm_text_loss *= self.mmm_text_weight else: mmm_text_logits = self.mmm_text_head(sequence_for_text) # Global Contrastive Loss if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0: text_embedding = self.flava.text_projection(text_embeddings[:, 0, :]) text_embedding = nn.functional.normalize(text_embedding, dim=-1) image_embedding = self.flava.image_projection(image_embeddings[:, 0, :]) image_embedding = nn.functional.normalize(image_embedding, dim=-1) self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX) logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head( image_embedding, text_embedding, self.flava.logit_scale ) # Apply ITM negative mask if any if pos_mask is not None: logits_per_image = logits_per_image[pos_mask] logits_per_text = logits_per_text[pos_mask] gc_labels = gc_labels[pos_mask] if return_loss: gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels) gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels) gc_loss = (gc_loss_image + gc_loss_text) / 2 gc_loss *= self.global_contrastive_weight flava_losses = FlavaLosses( mim=mim_loss, mlm=mlm_loss, itm=itm_loss, global_contrastive=gc_loss, mmm_image=mmm_image_loss, mmm_text=mmm_text_loss, ) if return_loss and not flava_losses.all_none(): total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values()) if not return_dict: output = ( image_embeddings, flava_output.image_output.to_tuple() if flava_output.image_output is not None else None, text_embeddings, flava_output.text_output.to_tuple() if flava_output.text_output is not None else None, flava_output.multimodal_embeddings, flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None, image_masked_embeddings, flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None, text_masked_embeddings, flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None, multimodal_masked_embeddings, flava_masked_output.multimodal_output.to_tuple() if flava_masked_output.multimodal_output is not None else None, mim_logits, mlm_logits, itm_logits, logits_per_image, logits_per_image, mmm_image_logits, mmm_text_logits, ) if return_loss and not flava_losses.all_none(): output = ( total_loss, flava_losses, ) + output # Filter None as transformer by default won't handle it return tuple(x for x in output if x is None) return FlavaForPreTrainingOutput( loss=total_loss, loss_info=flava_losses, image_embeddings=image_embeddings, image_output=flava_output.image_output, text_embeddings=text_embeddings, text_output=flava_output.text_output, multimodal_embeddings=flava_output.multimodal_embeddings, multimodal_output=flava_output.multimodal_output, image_masked_embeddings=image_masked_embeddings, image_masked_output=flava_masked_output.image_output, text_masked_embeddings=text_masked_embeddings, text_masked_output=flava_masked_output.text_output, multimodal_masked_embeddings=multimodal_masked_embeddings, multimodal_masked_output=flava_masked_output.multimodal_output, mim_logits=mim_logits, mlm_logits=mlm_logits, itm_logits=itm_logits, contrastive_logits_per_image=logits_per_image, contrastive_logits_per_text=logits_per_text, mmm_image_logits=mmm_image_logits, mmm_text_logits=mmm_text_logits, )

# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch FLAVA model.""" import collections import math from collections import OrderedDict from dataclasses import dataclass from typing import Any, Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from transformers.utils.doc import add_code_sample_docstrings from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_flava import ( FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/flava-full" # Codebook docstring _CHECKPOINT_FOR_CODEBOOK_DOC = "facebook/flava-image-codebook" _FEAT_EXTRACTOR_FOR_DOC = "FlavaFeatureExtractor" _CONFIG_CLASS_FOR_IMAGE_MODEL_DOC = "FlavaImageConfig" _CONFIG_CLASS_FOR_TEXT_MODEL_DOC = "FlavaTextConfig" _CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC = "FlavaMultimodalConfig" _TOKENIZER_FOR_DOC = "BertTokenizer" _EXPECTED_IMAGE_OUTPUT_SHAPE = [1, 197, 768] FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/flava-full", # See all flava models at https://huggingface.co/models?filter=flava ] FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST = ["facebook/flava-image-codebook"] LOGIT_SCALE_CLAMP_MIN = 0 LOGIT_SCALE_CLAMP_MAX = 4.6052 FlavaPossibleConfigs = Union[FlavaTextConfig, FlavaImageConfig, FlavaMultimodalConfig] @dataclass class FlavaModelOutput(ModelOutput): """ Output from FlavaModel containing embeddings and outputs from individual encoders. Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. """ image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_output", "image_output", "multimodal_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class FlavaLosses(ModelOutput): """Class representing pretraining losses from FLAVA model Args: mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.: Masked Image Modeling loss as used in BeIT calculated only for unimodal image data. mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.: Masked Language Modeling loss as used in BERT calculated only for unimodal text data. itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.: Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on masked pairs in FLAVA. global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.: Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text data. This is calculated on unmasked images and texts. mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.: Masked Multimodal Modeling loss's image component calculated on paired image-text data. mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.: Masked Multimodal Modeling loss's text component calculated on paired image-text data. """ mim: Optional[torch.FloatTensor] = None mlm: Optional[torch.FloatTensor] = None itm: Optional[torch.FloatTensor] = None global_contrastive: Optional[torch.FloatTensor] = None mmm_image: Optional[torch.FloatTensor] = None mmm_text: Optional[torch.FloatTensor] = None def all_none(self) -> bool: all_none = True for v in self.values(): if v is not None: all_none = False break return all_none @dataclass class FlavaForPreTrainingOutput(ModelOutput): """ Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders. Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True): Total loss calculated for this model. loss_info (`FlavaLosses`): Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on the keys. image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present): The output of the [`FlavaTextModel`]. multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_masked_output (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present): The output of the [`FlavaMultimodalModel`]. mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not): The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not): The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present): The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA. mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's `image_projection` and `text_projection` layers respectively. This represents the image-text similarity scores. This is calculated on unmasked images and texts. contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and texts. """ loss: Optional[torch.FloatTensor] = None loss_info: FlavaLosses = None image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None image_masked_embeddings: Optional[torch.FloatTensor] = None image_masked_output: Optional[BaseModelOutputWithPooling] = None text_masked_embeddings: Optional[torch.FloatTensor] = None text_masked_output: Optional[BaseModelOutputWithPooling] = None multimodal_masked_embeddings: Optional[torch.FloatTensor] = None multimodal_masked_output: Optional[BaseModelOutputWithPooling] = None mim_logits: Optional[torch.FloatTensor] = None mlm_logits: Optional[torch.FloatTensor] = None itm_logits: Optional[torch.FloatTensor] = None contrastive_logits_per_image: Optional[torch.FloatTensor] = None contrastive_logits_per_text: Optional[torch.FloatTensor] = None mmm_image_logits: Optional[torch.FloatTensor] = None mmm_text_logits: Optional[torch.FloatTensor] = None def to_tuple(self) -> Tuple[Any]: transformer_outputs = [ "text_output", "image_output", "multimodal_output", "text_masked_output", "image_masked_output", "multimodal_masked_output", ] return tuple(self[k] if k not in transformer_outputs else getattr(self, k).to_tuple() for k in self.keys()) # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class FlavaImageEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None: super().__init__() use_mask_token = use_mask_token or config.mask_token self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = PatchEmbeddings( image_size=config.image_size, patch_size=config.patch_size, num_channels=config.num_channels, embed_dim=config.hidden_size, ) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/image_transformer.py#L174 """ npatch = embeddings.shape[1] - 1 num_pos = self.position_embeddings.shape[1] - 1 if npatch == num_pos and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] num_h_patches = height // self.config.patch_size num_w_patches = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1 patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(math.sqrt(num_pos)), int(math.sqrt(num_pos)), dim).permute(0, 3, 1, 2), scale_factor=(num_h_patches / math.sqrt(num_pos), num_w_patches / math.sqrt(num_pos)), mode="bicubic", align_corners=False, ) if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]: raise ValueError( f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the " f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})" ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) batch_size, seq_len, _ = embeddings.size() if bool_masked_pos is not None: mask_tokens = self.mask_token.expand(batch_size, seq_len, -1) # B X H X W = B X HW if bool_masked_pos.dim() == 3: bool_masked_pos = bool_masked_pos.view(bool_masked_pos.size(0), -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class PatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__( self, image_size: int = 224, patch_size: Union[int, Tuple[int, int]] = 16, num_channels: int = 3, embed_dim: int = 768, ): super().__init__() if not isinstance(image_size, collections.abc.Iterable): image_size = (image_size, image_size) if not isinstance(patch_size, collections.abc.Iterable): patch_size = (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) x = self.projection(pixel_values).flatten(2).transpose(1, 2) return x class FlavaTextEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, ): input_shape = input_ids.size() seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class FlavaSelfAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class FlavaSelfOutput(nn.Module): """ The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class FlavaAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.attention = FlavaSelfAttention(config) self.output = FlavaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention( hidden_states, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class FlavaIntermediate(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act # Copied from transformers.models.vit.modeling_vit.ViTIntermediate.forward def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class FlavaOutput(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # Copied from transformers.models.vit.modeling_vit.ViTOutput.forward def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class FlavaLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = FlavaAttention(config) self.intermediate = FlavaIntermediate(config) self.output = FlavaOutput(config) # TODO: Check fp32 layer norm possiblity self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class FlavaEncoder(nn.Module): def __init__(self, config: FlavaConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions ) class FlavaPooler(nn.Module): def __init__(self, config: FlavaPossibleConfigs): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output FLAVA_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`{config}`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ FLAVA_INPUTS_DOCSTRING_COMMON = r""" attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ FLAVA_IMAGE_INPUTS_DOCSTRING_BASE = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`FlavaFeatureExtractor`]. See [`FlavaFeatureExtractor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. """ FLAVA_IMAGE_INPUTS_DOCSTRING = FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_TEXT_INPUTS_DOCSTRING_BASE = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) """ FLAVA_TEXT_INPUTS_DOCSTRING = FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_MULTIMODAL_INPUTS_DOCSTRING = ( r""" Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`): The concatenated hidden states of unimodal encoders. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_MODEL_INPUTS_DOCSTRING_BASE = r""" Args: skip_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used. """ FLAVA_MODEL_INPUTS_DOCSTRING = ( FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON + FLAVA_MODEL_INPUTS_DOCSTRING_BASE ) FLAVA_PRETRAINING_INPUTS_DOCSTRING = ( r""" Args: input_ids_masked (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task to be used with MLM. Indices can be obtained using [`BertTokenizer`] along with [`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) """ + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + r""" image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*): Mask to avoid performing attention on padding token indices specifically for images. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) skip_unmasked_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked multimodal embeddings or outputs as of now. mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*): Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction). Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., text_config.vocab_size - 1]`. mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*): Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ..., image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are generated automatically using the image codebook assigned to the model. By default, it uses [`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels. itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*): Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match. The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well. return_loss (`bool`, *optional*, default to None): Whether to return calculated loss or not. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_PRETRAINING_START_DOCSTRING_EXTRA = r""" Parameters: image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will be initialized using the image_codebook_config defined in the config first as the first parameter. """ class FlavaPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FlavaConfig base_model_prefix = "flava" supports_gradient_checkpointing = True def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: FlavaEncoder, value: bool = False) -> None: if isinstance(module, FlavaEncoder): module.gradient_checkpointing = value @add_start_docstrings( "The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaImageConfig"), ) class FlavaImageModel(FlavaPreTrainedModel): config_class = FlavaImageConfig # This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.image_model" main_input_name = "pixel_values" def __init__(self, config: FlavaImageConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaImageEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> nn.Module: return self.embeddings.patch_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.patch_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC, modality="vision", expected_output=_EXPECTED_IMAGE_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaTextConfig"), ) class FlavaTextModel(FlavaPreTrainedModel): config_class = FlavaTextConfig # This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.text_model" def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaTextEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> PatchEmbeddings: return self.embeddings.word_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify input_ids") input_shape = input_ids.size() if attention_mask is None: attention_mask = torch.ones(input_shape, device=input_ids.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, input_shape, input_ids.device ) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaMultimodalConfig"), ) class FlavaMultimodalModel(FlavaPreTrainedModel): config_class = FlavaMultimodalConfig # This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.multimodal_model" main_input_name = "hidden_states" def __init__(self, config: FlavaMultimodalConfig, add_pooling_layer=True): super().__init__(config) self.config = config self.use_cls_token = self.config.use_cls_token if self.use_cls_token: self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward( FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC, ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, seq_length, _ = hidden_states.size() if self.use_cls_token: cls_tokens = self.cls_token.expand(batch_size, -1, -1) hidden_states = torch.cat((cls_tokens, hidden_states), dim=1) seq_length += 1 if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, (batch_size, seq_length), hidden_states.device ) encoder_outputs = self.encoder( hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaConfig"), ) class FlavaModel(FlavaPreTrainedModel): config_class = FlavaConfig def __init__(self, config: FlavaConfig): super().__init__(config) if not isinstance(config.text_config, FlavaTextConfig): raise ValueError( "config.text_config is expected to be of type FlavaTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.image_config, FlavaImageConfig): raise ValueError( "config.image_config is expected to be of type FlavaImageConfig but is of type" f" {type(config.image_config)}." ) if not isinstance(config.multimodal_config, FlavaMultimodalConfig): raise ValueError( "config.multimodal_config is expected to be of type FlavaMultimodalConfig but " + f"is of type {type(config.multimodal_config)}." ) text_config = config.text_config image_config = config.image_config multimodal_config = config.multimodal_config self.projection_dim = config.projection_dim self.text_hidden_size = text_config.hidden_size self.image_hidden_size = image_config.hidden_size self.mm_hidden_size = multimodal_config.hidden_size self.text_model = FlavaTextModel(text_config) self.image_model = FlavaImageModel(image_config) self.multimodal_model = FlavaMultimodalModel(multimodal_config) self.image_projection = nn.Linear(self.image_hidden_size, self.projection_dim) self.text_projection = nn.Linear(self.text_hidden_size, self.projection_dim) self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value) self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size) self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlavaTextModel`]. Examples: ```python >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[0] # last_hidden_state text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) def get_image_features( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlavaImageModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) image_outputs = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) pooled_output = image_outputs[0] # last_hidden_state image_features = self.image_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward( FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_multimodal_encoder: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, ) -> Union[Tuple, FlavaOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True) >>> outputs = model(**inputs) >>> logits_per_image = outputs.contrastive_logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ``` """ return_dict = return_dict if return_dict is not None else self.config.return_dict if not output_hidden_states: raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`") image_embeddings = None image_states = None image_mm_projection = None image_output = None if pixel_values is not None: image_output = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=image_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeddings, image_states = image_output[0], image_output[2] # Note that these states don't use final layernorm in the transformer model image_mm_projection = self.image_to_mm_projection(image_states[-1]) text_embeddings = None text_states = None text_mm_projection = None text_output = None if input_ids is not None: text_output = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_embeddings, text_states = text_output[0], text_output[2] # Note that these states don't use final layernorm in the transformer model text_mm_projection = self.text_to_mm_projection(text_states[-1]) multimodal_embeddings = None multimodal_output = None if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder: multimodal_input = torch.cat([image_mm_projection, text_mm_projection], dim=1) multimodal_output = self.multimodal_model(multimodal_input, return_dict=return_dict) multimodal_embeddings = multimodal_output[0] if not return_dict: return ( image_embeddings, image_output, text_embeddings, text_output, multimodal_embeddings, multimodal_output, ) return FlavaModelOutput( image_embeddings=image_embeddings, image_output=image_output, text_embeddings=text_embeddings, text_output=text_output, multimodal_embeddings=multimodal_embeddings, multimodal_output=multimodal_output, ) class FlavaImageCodebookResPath(nn.Module): def __init__(self, in_size: int, out_size: int, **kwargs): super().__init__() hid_size = out_size // 4 path = OrderedDict() path["relu_1"] = nn.ReLU() path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1) path["relu_2"] = nn.ReLU() path["conv_2"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_3"] = nn.ReLU() path["conv_3"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_4"] = nn.ReLU() path["conv_4"] = nn.Conv2d(hid_size, out_size, kernel_size=1, padding=0) self.path = nn.Sequential(path) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.path(x) class FlavaImageCodebookBlock(nn.Module): def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs): super().__init__() self.post_gain = 1 / (num_layers**2) if in_size != out_size: self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0) else: self.id_path = nn.Identity() self.res_path = FlavaImageCodebookResPath(in_size, out_size) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.id_path(x) + self.post_gain * self.res_path(x) class FlavaImageCodebookLayerGroup(nn.Module): def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True): super().__init__() blocks = OrderedDict() for i in range(num_blocks): if i == 0: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(in_size, out_size, num_layers) else: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(out_size, out_size, num_layers) if use_pool: blocks["pool"] = nn.MaxPool2d(kernel_size=2) self.group = nn.Sequential(blocks) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.group(x) # Inspired by DALLE Encoder in https://github.com/openai/DALL-E/blob/5be4b236bc3ade6943662354117a0e83752cc322/dall_e/encoder.py#L42 @add_start_docstrings( """ The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use `get_codebook_indices` to get image tokens for an image. """, FLAVA_START_DOCSTRING.format(config="FlavaImageCodebookConfig"), ) class FlavaImageCodebook(FlavaPreTrainedModel): base_model_prefix = "" config_class = FlavaImageCodebookConfig main_input_name = "pixel_values" supports_gradient_checkpointing = False def __init__( self, config: FlavaImageCodebookConfig, **kwargs: Any, ): super().__init__(config) self.config = config self.num_groups = config.num_groups self.input_channels = config.input_channels self.num_blocks_per_group = config.num_blocks_per_group self.hidden_size = config.hidden_size self.vocab_size = config.vocab_size num_layers = self.num_groups * self.num_blocks_per_group output_blocks = OrderedDict() output_blocks["relu"] = nn.ReLU() output_blocks["conv"] = nn.Conv2d(8 * self.hidden_size, self.vocab_size, kernel_size=1, padding=0) blocks = OrderedDict() blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3) blocks["group_1"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size ) blocks["group_2"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 2 * self.hidden_size ) blocks["group_3"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 2 * self.hidden_size, 4 * self.hidden_size ) blocks["group_4"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 4 * self.hidden_size, 8 * self.hidden_size, use_pool=False ) blocks["output"] = nn.Sequential(output_blocks) self.blocks = nn.Sequential(blocks) self.post_init() if self.config.freeze: for param in self.parameters(): param.requires_grad = False def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model.get_codebook_indices(**inputs) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) z_logits = self.blocks(pixel_values) return torch.argmax(z_logits, axis=1) def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor: z_logits = self.blocks(pixel_values) return nn.Softmax(dim=1)(z_logits) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model(**inputs) >>> print(outputs.shape) (1, 196) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) if len(pixel_values.shape) != 4: raise ValueError(f"input shape {pixel_values.shape} is not 4d") if pixel_values.shape[1] != self.input_channels: raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}") return self.blocks(pixel_values) class FlavaPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class FlavaMaskedPredictionHead(nn.Module): def __init__(self, config, weight=None): super().__init__() self.config = config self.transform = FlavaPredictionHeadTransform(config) self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) if weight is not None: self.decoder.weight = weight # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, x): x = self.transform(x) x = self.decoder(x) return x class FlavaITMHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.pooler = FlavaPooler(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, x): x = self.pooler(x) x = self.seq_relationship(x) return x class FlavaGlobalContrastiveHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.global_backprop_contrastive = config.global_backprop_contrastive def forward(self, image_embeddings, text_embeddings, logit_scale): temperature = torch.exp(logit_scale) if not torch.distributed.is_available() or not torch.distributed.is_initialized(): labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device) image_embeddings_all = [image_embeddings] text_embeddings_all = [text_embeddings] else: local_batch_size = image_embeddings.size(0) world_size = torch.distributed.get_world_size() if self.global_backprop_contrastive: image_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(image_embeddings) text_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(text_embeddings) else: image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)] text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)] torch.distributed.all_gather(image_embeddings_all, image_embeddings) torch.distributed.all_gather(text_embeddings_all, text_embeddings) labels = local_batch_size * torch.distributed.get_rank() + torch.arange( local_batch_size, device=image_embeddings.device ) image_embeddings_all = torch.cat(image_embeddings_all) text_embeddings_all = torch.cat(text_embeddings_all) logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature return logits_per_image, logits_per_text, labels @add_start_docstrings( """ The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs. """, FLAVA_START_DOCSTRING.format(config="FlavaConfig") + FLAVA_PRETRAINING_START_DOCSTRING_EXTRA, ) class FlavaForPreTraining(FlavaPreTrainedModel): # Those are linked to xxx.bias _keys_to_ignore_on_load_missing = [ "mmm_text_head.decoder.bias", "mmm_image_head.decoder.bias", "mlm_head.decoder.bias", "mim_head.decoder.bias", ] def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None): super().__init__(config) self.flava = FlavaModel(config) self.image_codebook = image_codebook if self.image_codebook is None and config.init_codebook: self.image_codebook = FlavaImageCodebook(config.image_codebook_config) # Levarage text and image encoder configs to create the masked # head since it has the right vocab self.mim_head = FlavaMaskedPredictionHead(config.image_config) self.mlm_head = FlavaMaskedPredictionHead(config.text_config) self.itm_head = FlavaITMHead(config) self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config) self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config) self.global_contrastive_head = FlavaGlobalContrastiveHead(config) self.image_vocab_size = config.image_config.vocab_size self.text_vocab_size = config.text_config.vocab_size self.mlm_weight = config.mlm_weight self.mim_weight = config.mim_weight self.global_contrastive_weight = config.global_contrastive_weight self.ce_ignore_index = config.ce_ignore_index self.itm_weight = config.itm_weight self.mmm_image_weight = config.mmm_image_weight self.mmm_text_weight = config.mmm_text_weight self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder self.post_init() def _resize_to_2d(self, x: torch.Tensor): if x.dim() > 2: x = x.view(x.size(0), -1) return x @add_start_docstrings_to_model_forward( FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches") ) @replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, input_ids_masked: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, codebook_pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_unmasked_multimodal_encoder: bool = None, mlm_labels: Optional[torch.Tensor] = None, mim_labels: Optional[torch.Tensor] = None, itm_labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, return_loss: Optional[bool] = None, ): """ Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaForPreTraining, FlavaProcessor >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> text = ["a photo of a cat"] >>> inputs = processor( ... images=[image], ... text=text, ... return_masks=True, ... return_codebook_pixels=True, ... padding=True, ... max_length=77, ... return_tensors="pt", ... ) >>> output = model(**inputs) ``` Return: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict return_loss = return_loss if return_loss is not None else self.config.return_loss skip_unmasked_multimodal_encoder = ( skip_unmasked_multimodal_encoder if skip_unmasked_multimodal_encoder is not None else self.skip_unmasked_multimodal_encoder ) if input_ids_masked is None and input_ids is not None: logger.warning( "`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to" " `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if" " you are doing inference on unmasked text..." ) input_ids_masked = input_ids flava_output = self.flava( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, image_attention_mask=image_attention_mask, # Don't need unmasked multimodal embedding for anything so skip it # NOTE: ITM uses masked version skip_multimodal_encoder=skip_unmasked_multimodal_encoder, output_attentions=output_attentions, output_hidden_states=output_hidden_states, # Pass true to have deterministic outputs return_dict=True, ) flava_masked_output = self.flava( input_ids=input_ids_masked, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, image_attention_mask=image_attention_mask, bool_masked_pos=bool_masked_pos, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, ) pos_mask = None image_embeddings = flava_output.image_embeddings text_embeddings = flava_output.text_embeddings image_masked_embeddings = flava_masked_output.image_embeddings text_masked_embeddings = flava_masked_output.text_embeddings multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None itm_logits = logits_per_image = logits_per_text = None # Calculate mim_labels if necessary from the image_codebook if image_masked_embeddings is not None or multimodal_masked_embeddings is not None: if mim_labels is None and return_loss: if self.image_codebook is None: raise RuntimeError( "`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` " " have been passed. Reinstantiate the model with `init_codebook` set to True or " "pass in your custom `mim_labels`" ) if codebook_pixel_values is None: raise ValueError( "`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. " "Call `FlavaProcessor` with `return_codebook_pixels` set to True" ) mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values) # Unimodal MIM Loss # If multimodal embeddings are present, we will calculate MMM loss if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_image = image_masked_embeddings if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :] masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mim_logits = self.mim_head(sequence_for_image) if return_loss: mim_loss = nn.functional.cross_entropy( mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mim_loss *= self.mim_weight else: mim_logits = self.mim_head(sequence_for_image) # Unimodal MLM Loss if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_text = text_masked_embeddings if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :] masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mlm_logits = self.mlm_head(sequence_for_text) if return_loss: mlm_loss = nn.functional.cross_entropy( mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mlm_loss *= self.mlm_weight else: mlm_logits = self.mlm_head(sequence_for_text) # ITM Loss if self.itm_weight > 0 and multimodal_masked_embeddings is not None: itm_logits = self.itm_head(multimodal_masked_embeddings) if itm_labels is not None: pos_pairs = itm_labels.ne(0) pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True])) if return_loss: itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels) itm_loss *= self.itm_weight if multimodal_masked_embeddings is not None: multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask] if mlm_labels is not None: mlm_labels = mlm_labels[pos_mask] if mim_labels is not None: mim_labels = mim_labels[pos_mask] # MMM Image Loss if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0: sequence_for_image = multimodal_masked_embeddings end_index = image_masked_embeddings.size(1) - 1 sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :] if pos_mask is not None: sequence_for_image = sequence_for_image[pos_mask] if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mmm_image_logits = self.mmm_image_head(sequence_for_image) if return_loss: mmm_image_loss = nn.functional.cross_entropy( mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mmm_image_loss *= self.mmm_image_weight else: mmm_image_logits = self.mmm_image_head(sequence_for_image) # MMM Text Loss if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0: sequence_for_text = multimodal_masked_embeddings sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :] if pos_mask is not None: sequence_for_text = sequence_for_text[pos_mask] if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mmm_text_logits = self.mmm_text_head(sequence_for_text) if return_loss: mmm_text_loss = nn.functional.cross_entropy( mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mmm_text_loss *= self.mmm_text_weight else: mmm_text_logits = self.mmm_text_head(sequence_for_text) # Global Contrastive Loss if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0: text_embedding = self.flava.text_projection(text_embeddings[:, 0, :]) text_embedding = nn.functional.normalize(text_embedding, dim=-1) image_embedding = self.flava.image_projection(image_embeddings[:, 0, :]) image_embedding = nn.functional.normalize(image_embedding, dim=-1) self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX) logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head( image_embedding, text_embedding, self.flava.logit_scale ) # Apply ITM negative mask if any if pos_mask is not None: logits_per_image = logits_per_image[pos_mask] logits_per_text = logits_per_text[pos_mask] gc_labels = gc_labels[pos_mask] if return_loss: gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels) gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels) gc_loss = (gc_loss_image + gc_loss_text) / 2 gc_loss *= self.global_contrastive_weight flava_losses = FlavaLosses( mim=mim_loss, mlm=mlm_loss, itm=itm_loss, global_contrastive=gc_loss, mmm_image=mmm_image_loss, mmm_text=mmm_text_loss, ) if return_loss and not flava_losses.all_none(): total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values()) if not return_dict: output = ( image_embeddings, flava_output.image_output.to_tuple() if flava_output.image_output is not None else None, text_embeddings, flava_output.text_output.to_tuple() if flava_output.text_output is not None else None, flava_output.multimodal_embeddings, flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None, image_masked_embeddings, flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None, text_masked_embeddings, flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None, multimodal_masked_embeddings, flava_masked_output.multimodal_output.to_tuple() if flava_masked_output.multimodal_output is not None else None, mim_logits, mlm_logits, itm_logits, logits_per_image, logits_per_image, mmm_image_logits, mmm_text_logits, ) if return_loss and not flava_losses.all_none(): output = ( total_loss, flava_losses, ) + output # Filter None as transformer by default won't handle it return tuple(x for x in output if x is None) return FlavaForPreTrainingOutput( loss=total_loss, loss_info=flava_losses, image_embeddings=image_embeddings, image_output=flava_output.image_output, text_embeddings=text_embeddings, text_output=flava_output.text_output, multimodal_embeddings=flava_output.multimodal_embeddings, multimodal_output=flava_output.multimodal_output, image_masked_embeddings=image_masked_embeddings, image_masked_output=flava_masked_output.image_output, text_masked_embeddings=text_masked_embeddings, text_masked_output=flava_masked_output.text_output, multimodal_masked_embeddings=multimodal_masked_embeddings, multimodal_masked_output=flava_masked_output.multimodal_output, mim_logits=mim_logits, mlm_logits=mlm_logits, itm_logits=itm_logits, contrastive_logits_per_image=logits_per_image, contrastive_logits_per_text=logits_per_text, mmm_image_logits=mmm_image_logits, mmm_text_logits=mmm_text_logits, )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/retribert/modeling_retribert.py

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RetriBERT model """ import math from typing import Optional import torch import torch.utils.checkpoint as checkpoint from torch import nn from ...modeling_utils import PreTrainedModel from ...utils import add_start_docstrings, logging from ..bert.modeling_bert import BertModel from .configuration_retribert import RetriBertConfig logger = logging.get_logger(__name__) RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "yjernite/retribert-base-uncased", # See all RetriBert models at https://huggingface.co/models?filter=retribert ] # INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL # class RetriBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RetriBertConfig load_tf_weights = None base_model_prefix = "retribert" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) RETRIBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( """Bert Based model to embed queries or document for document retrieval.""", RETRIBERT_START_DOCSTRING, ) class RetriBertModel(RetriBertPreTrainedModel): def __init__(self, config: RetriBertConfig) -> None: super().__init__(config) self.projection_dim = config.projection_dim self.bert_query = BertModel(config) self.bert_doc = None if config.share_encoders else BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.ce_loss = nn.CrossEntropyLoss(reduction="mean") # Initialize weights and apply final processing self.post_init() def embed_sentences_checkpointed( self, input_ids, attention_mask, sent_encoder, checkpoint_batch_size=-1, ): # reproduces BERT forward pass with checkpointing if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: return sent_encoder(input_ids, attention_mask=attention_mask)[1] else: # prepare implicit variables device = input_ids.device input_shape = input_ids.size() token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) head_mask = [None] * sent_encoder.config.num_hidden_layers extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask( attention_mask, input_shape ) # define function for checkpointing def partial_encode(*inputs): encoder_outputs = sent_encoder.encoder( inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) sequence_output = encoder_outputs[0] pooled_output = sent_encoder.pooler(sequence_output) return pooled_output # run embedding layer on everything at once embedding_output = sent_encoder.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None ) # run encoding and pooling on one mini-batch at a time pooled_output_list = [] for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) pooled_output_list.append(pooled_output) return torch.cat(pooled_output_list, dim=0) def embed_questions( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): q_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query, checkpoint_batch_size, ) return self.project_query(q_reps) def embed_answers( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): a_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query if self.bert_doc is None else self.bert_doc, checkpoint_batch_size, ) return self.project_doc(a_reps) def forward( self, input_ids_query: torch.LongTensor, attention_mask_query: Optional[torch.FloatTensor], input_ids_doc: torch.LongTensor, attention_mask_doc: Optional[torch.FloatTensor], checkpoint_batch_size: int = -1, ) -> torch.FloatTensor: r""" Args: input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the queries in a batch. Indices can be obtained using [`RetriBertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask_query (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the documents in a batch. attention_mask_doc (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on documents padding token indices. checkpoint_batch_size (`int`, *optional*, defaults to `-1`): If greater than 0, uses gradient checkpointing to only compute sequence representation on `checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to all document representations in the batch. Return: `torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its corresponding document and each document to its corresponding query in the batch """ device = input_ids_query.device q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size) a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size) compare_scores = torch.mm(q_reps, a_reps.t()) loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RetriBERT model """ import math from typing import Optional import torch import torch.utils.checkpoint as checkpoint from torch import nn from ...modeling_utils import PreTrainedModel from ...utils import add_start_docstrings, logging from ..bert.modeling_bert import BertModel from .configuration_retribert import RetriBertConfig logger = logging.get_logger(__name__) RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "yjernite/retribert-base-uncased", # See all RetriBert models at https://huggingface.co/models?filter=retribert ] # INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL # class RetriBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RetriBertConfig load_tf_weights = None base_model_prefix = "retribert" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) RETRIBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( """Bert Based model to embed queries or document for document retrieval.""", RETRIBERT_START_DOCSTRING, ) class RetriBertModel(RetriBertPreTrainedModel): def __init__(self, config: RetriBertConfig) -> None: super().__init__(config) self.projection_dim = config.projection_dim self.bert_query = BertModel(config) self.bert_doc = None if config.share_encoders else BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.ce_loss = nn.CrossEntropyLoss(reduction="mean") # Initialize weights and apply final processing self.post_init() def embed_sentences_checkpointed( self, input_ids, attention_mask, sent_encoder, checkpoint_batch_size=-1, ): # reproduces BERT forward pass with checkpointing if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: return sent_encoder(input_ids, attention_mask=attention_mask)[1] else: # prepare implicit variables device = input_ids.device input_shape = input_ids.size() token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) head_mask = [None] * sent_encoder.config.num_hidden_layers extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask( attention_mask, input_shape ) # define function for checkpointing def partial_encode(*inputs): encoder_outputs = sent_encoder.encoder( inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) sequence_output = encoder_outputs[0] pooled_output = sent_encoder.pooler(sequence_output) return pooled_output # run embedding layer on everything at once embedding_output = sent_encoder.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None ) # run encoding and pooling on one mini-batch at a time pooled_output_list = [] for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) pooled_output_list.append(pooled_output) return torch.cat(pooled_output_list, dim=0) def embed_questions( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): q_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query, checkpoint_batch_size, ) return self.project_query(q_reps) def embed_answers( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): a_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query if self.bert_doc is None else self.bert_doc, checkpoint_batch_size, ) return self.project_doc(a_reps) def forward( self, input_ids_query: torch.LongTensor, attention_mask_query: Optional[torch.FloatTensor], input_ids_doc: torch.LongTensor, attention_mask_doc: Optional[torch.FloatTensor], checkpoint_batch_size: int = -1, ) -> torch.FloatTensor: r""" Args: input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the queries in a batch. Indices can be obtained using [`RetriBertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask_query (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the documents in a batch. attention_mask_doc (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on documents padding token indices. checkpoint_batch_size (`int`, *optional*, defaults to `-1`): If greater than 0, uses gradient checkpointing to only compute sequence representation on `checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to all document representations in the batch. Return: `torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its corresponding document and each document to its corresponding query in the batch """ device = input_ids_query.device q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size) a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size) compare_scores = torch.mm(q_reps, a_reps.t()) loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./docs/source/en/model_doc/bigbird_pegasus.mdx

<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # BigBirdPegasus ## Overview The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and random attention approximates full attention, while being computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context, BigBird has shown improved performance on various long document NLP tasks, such as question answering and summarization, compared to BERT or RoBERTa. The abstract from the paper is the following: *Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.* Tips: - For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird). - BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using **original_full** is advised as there is no benefit in using **block_sparse** attention. - The code currently uses window size of 3 blocks and 2 global blocks. - Sequence length must be divisible by block size. - Current implementation supports only **ITC**. - Current implementation doesn't support **num_random_blocks = 0**. - BigBirdPegasus uses the [PegasusTokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pegasus/tokenization_pegasus.py). - BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. The original code can be found [here](https://github.com/google-research/bigbird). ## BigBirdPegasusConfig [[autodoc]] BigBirdPegasusConfig - all ## BigBirdPegasusModel [[autodoc]] BigBirdPegasusModel - forward ## BigBirdPegasusForConditionalGeneration [[autodoc]] BigBirdPegasusForConditionalGeneration - forward ## BigBirdPegasusForSequenceClassification [[autodoc]] BigBirdPegasusForSequenceClassification - forward ## BigBirdPegasusForQuestionAnswering [[autodoc]] BigBirdPegasusForQuestionAnswering - forward ## BigBirdPegasusForCausalLM [[autodoc]] BigBirdPegasusForCausalLM - forward

<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # BigBirdPegasus ## Overview The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and random attention approximates full attention, while being computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context, BigBird has shown improved performance on various long document NLP tasks, such as question answering and summarization, compared to BERT or RoBERTa. The abstract from the paper is the following: *Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.* Tips: - For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird). - BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using **original_full** is advised as there is no benefit in using **block_sparse** attention. - The code currently uses window size of 3 blocks and 2 global blocks. - Sequence length must be divisible by block size. - Current implementation supports only **ITC**. - Current implementation doesn't support **num_random_blocks = 0**. - BigBirdPegasus uses the [PegasusTokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pegasus/tokenization_pegasus.py). - BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. The original code can be found [here](https://github.com/google-research/bigbird). ## BigBirdPegasusConfig [[autodoc]] BigBirdPegasusConfig - all ## BigBirdPegasusModel [[autodoc]] BigBirdPegasusModel - forward ## BigBirdPegasusForConditionalGeneration [[autodoc]] BigBirdPegasusForConditionalGeneration - forward ## BigBirdPegasusForSequenceClassification [[autodoc]] BigBirdPegasusForSequenceClassification - forward ## BigBirdPegasusForQuestionAnswering [[autodoc]] BigBirdPegasusForQuestionAnswering - forward ## BigBirdPegasusForCausalLM [[autodoc]] BigBirdPegasusForCausalLM - forward

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/layoutlmv2/__init__.py

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/t5/test_tokenization_t5.py

# coding=utf-8 # Copyright 2018 Google T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import tempfile import unittest from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, T5Tokenizer, T5TokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available, is_torch_available from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): FRAMEWORK = "pt" elif is_tf_available(): FRAMEWORK = "tf" else: FRAMEWORK = "jax" @require_sentencepiece @require_tokenizers class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = T5Tokenizer rust_tokenizer_class = T5TokenizerFast test_rust_tokenizer = True test_sentencepiece = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = T5Tokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "<s>" token_id = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<unk>") self.assertEqual(vocab_keys[1], "<s>") self.assertEqual(vocab_keys[-1], "<pad>") self.assertEqual(len(vocab_keys), 1_101) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_100) def test_full_tokenizer(self): tokenizer = T5Tokenizer(SAMPLE_VOCAB) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382]) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ], ) @cached_property def t5_base_tokenizer(self): return T5Tokenizer.from_pretrained("t5-base") @cached_property def t5_base_tokenizer_fast(self): return T5TokenizerFast.from_pretrained("t5-base") def get_tokenizer(self, **kwargs) -> T5Tokenizer: return self.tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs) def get_rust_tokenizer(self, **kwargs) -> T5TokenizerFast: return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer() sequence = "I was born in 92000, and this is falsé." tokens = tokenizer.tokenize(sequence) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) rust_tokenizer = self.get_rust_tokenizer() ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) def test_eos_treatment(self): tokenizer = self.t5_base_tokenizer batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]) batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""]) self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"]) def test_prepare_batch(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id] batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) self.assertIsInstance(batch, BatchEncoding) if FRAMEWORK != "jax": result = list(batch.input_ids.numpy()[0]) else: result = list(batch.input_ids.tolist()[0]) self.assertListEqual(expected_src_tokens, result) self.assertEqual((2, 9), batch.input_ids.shape) self.assertEqual((2, 9), batch.attention_mask.shape) def test_empty_target_text(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) # check if input_ids are returned and no decoder_input_ids self.assertIn("input_ids", batch) self.assertIn("attention_mask", batch) self.assertNotIn("decoder_input_ids", batch) self.assertNotIn("decoder_attention_mask", batch) def test_max_length(self): tokenizer = self.t5_base_tokenizer tgt_text = [ "Summary of the text.", "Another summary.", ] targets = tokenizer( text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK ) self.assertEqual(32, targets["input_ids"].shape[1]) def test_outputs_not_longer_than_maxlen(self): tokenizer = self.t5_base_tokenizer batch = tokenizer( ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK ) self.assertIsInstance(batch, BatchEncoding) # Since T5 does NOT have a max input length, # this test should be changed to the following in Transformers v5: # self.assertEqual(batch.input_ids.shape, (2, 8001)) self.assertEqual(batch.input_ids.shape, (2, 512)) def test_eos_in_input(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization. </s>"] tgt_text = ["Summary of the text. </s>"] expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1] expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1] batch = tokenizer(src_text, text_target=tgt_text) self.assertEqual(expected_src_tokens, batch["input_ids"][0]) self.assertEqual(expected_tgt_tokens, batch["labels"][0]) def test_token_type_ids(self): src_text_1 = ["A first paragraph for summarization."] src_text_2 = ["A second paragraph for summarization."] fast_token_type_ids = self.t5_base_tokenizer_fast( src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True ).token_type_ids slow_token_type_ids = self.t5_base_tokenizer( src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True ).token_type_ids self.assertEqual(slow_token_type_ids, fast_token_type_ids) self.assertEqual(len(slow_token_type_ids[0]), 18) def test_fast_and_slow_same_result(self): src_text = "<pad> Today is <unk> nice day </s>" tgt_ids = [0, 1960, 19, 2, 1245, 239, 1] tgt_text = "<pad> Today is<unk> nice day</s>" fast_ids = self.t5_base_tokenizer_fast(src_text, add_special_tokens=False).input_ids slow_ids = self.t5_base_tokenizer(src_text, add_special_tokens=False).input_ids self.assertEqual(tgt_ids, fast_ids) self.assertEqual(tgt_ids, slow_ids) fast_text = self.t5_base_tokenizer_fast.decode(fast_ids) slow_text = self.t5_base_tokenizer.decode(fast_ids) self.assertEqual(tgt_text, fast_text) self.assertEqual(tgt_text, slow_text) def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) p_output = tokenizer_p.encode("Hey this is a <special> token") r_output = tokenizer_r.encode("Hey this is a <special> token") cr_output = tokenizer_cr.encode("Hey this is a <special> token") special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0] self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in r_output) self.assertTrue(special_token_id in cr_output) def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self): tokenizer_list = [] if self.test_slow_tokenizer: tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())) if self.test_rust_tokenizer: tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())) for tokenizer_class, tokenizer_utils in tokenizer_list: with tempfile.TemporaryDirectory() as tmp_dir: tokenizer_utils.save_pretrained(tmp_dir) with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file: special_tokens_map = json.load(json_file) with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file: tokenizer_config = json.load(json_file) added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)] special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [ "an_additional_special_token" ] tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [ "an_additional_special_token" ] with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile: json.dump(special_tokens_map, outfile) with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile: json.dump(tokenizer_config, outfile) # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and # "special_tokens_map.json" files tokenizer_without_change_in_init = tokenizer_class.from_pretrained( tmp_dir, ) self.assertIn( "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens ) # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab self.assertEqual( ["an_additional_special_token"], tokenizer_without_change_in_init.convert_ids_to_tokens( tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]) ), ) # Now we test that we can change the value of additional_special_tokens in the from_pretrained new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)] tokenizer = tokenizer_class.from_pretrained( tmp_dir, additional_special_tokens=new_added_tokens, ) self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens) self.assertEqual( ["a_new_additional_special_token"], tokenizer.convert_ids_to_tokens( tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]) ), ) # overwritten from `test_tokenization_common` since T5 has no max length def test_pretrained_model_lists(self): # We should have at least one default checkpoint for each tokenizer # We should specify the max input length as well (used in some part to list the pretrained checkpoints) self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1) self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1) @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="t5-base", revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b", )

# coding=utf-8 # Copyright 2018 Google T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import tempfile import unittest from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, T5Tokenizer, T5TokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available, is_torch_available from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): FRAMEWORK = "pt" elif is_tf_available(): FRAMEWORK = "tf" else: FRAMEWORK = "jax" @require_sentencepiece @require_tokenizers class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = T5Tokenizer rust_tokenizer_class = T5TokenizerFast test_rust_tokenizer = True test_sentencepiece = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = T5Tokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "<s>" token_id = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<unk>") self.assertEqual(vocab_keys[1], "<s>") self.assertEqual(vocab_keys[-1], "<pad>") self.assertEqual(len(vocab_keys), 1_101) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_100) def test_full_tokenizer(self): tokenizer = T5Tokenizer(SAMPLE_VOCAB) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382]) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ], ) @cached_property def t5_base_tokenizer(self): return T5Tokenizer.from_pretrained("t5-base") @cached_property def t5_base_tokenizer_fast(self): return T5TokenizerFast.from_pretrained("t5-base") def get_tokenizer(self, **kwargs) -> T5Tokenizer: return self.tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs) def get_rust_tokenizer(self, **kwargs) -> T5TokenizerFast: return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer() sequence = "I was born in 92000, and this is falsé." tokens = tokenizer.tokenize(sequence) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) rust_tokenizer = self.get_rust_tokenizer() ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) def test_eos_treatment(self): tokenizer = self.t5_base_tokenizer batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]) batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""]) self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"]) def test_prepare_batch(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id] batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) self.assertIsInstance(batch, BatchEncoding) if FRAMEWORK != "jax": result = list(batch.input_ids.numpy()[0]) else: result = list(batch.input_ids.tolist()[0]) self.assertListEqual(expected_src_tokens, result) self.assertEqual((2, 9), batch.input_ids.shape) self.assertEqual((2, 9), batch.attention_mask.shape) def test_empty_target_text(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) # check if input_ids are returned and no decoder_input_ids self.assertIn("input_ids", batch) self.assertIn("attention_mask", batch) self.assertNotIn("decoder_input_ids", batch) self.assertNotIn("decoder_attention_mask", batch) def test_max_length(self): tokenizer = self.t5_base_tokenizer tgt_text = [ "Summary of the text.", "Another summary.", ] targets = tokenizer( text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK ) self.assertEqual(32, targets["input_ids"].shape[1]) def test_outputs_not_longer_than_maxlen(self): tokenizer = self.t5_base_tokenizer batch = tokenizer( ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK ) self.assertIsInstance(batch, BatchEncoding) # Since T5 does NOT have a max input length, # this test should be changed to the following in Transformers v5: # self.assertEqual(batch.input_ids.shape, (2, 8001)) self.assertEqual(batch.input_ids.shape, (2, 512)) def test_eos_in_input(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization. </s>"] tgt_text = ["Summary of the text. </s>"] expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1] expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1] batch = tokenizer(src_text, text_target=tgt_text) self.assertEqual(expected_src_tokens, batch["input_ids"][0]) self.assertEqual(expected_tgt_tokens, batch["labels"][0]) def test_token_type_ids(self): src_text_1 = ["A first paragraph for summarization."] src_text_2 = ["A second paragraph for summarization."] fast_token_type_ids = self.t5_base_tokenizer_fast( src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True ).token_type_ids slow_token_type_ids = self.t5_base_tokenizer( src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True ).token_type_ids self.assertEqual(slow_token_type_ids, fast_token_type_ids) self.assertEqual(len(slow_token_type_ids[0]), 18) def test_fast_and_slow_same_result(self): src_text = "<pad> Today is <unk> nice day </s>" tgt_ids = [0, 1960, 19, 2, 1245, 239, 1] tgt_text = "<pad> Today is<unk> nice day</s>" fast_ids = self.t5_base_tokenizer_fast(src_text, add_special_tokens=False).input_ids slow_ids = self.t5_base_tokenizer(src_text, add_special_tokens=False).input_ids self.assertEqual(tgt_ids, fast_ids) self.assertEqual(tgt_ids, slow_ids) fast_text = self.t5_base_tokenizer_fast.decode(fast_ids) slow_text = self.t5_base_tokenizer.decode(fast_ids) self.assertEqual(tgt_text, fast_text) self.assertEqual(tgt_text, slow_text) def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) p_output = tokenizer_p.encode("Hey this is a <special> token") r_output = tokenizer_r.encode("Hey this is a <special> token") cr_output = tokenizer_cr.encode("Hey this is a <special> token") special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0] self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in r_output) self.assertTrue(special_token_id in cr_output) def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self): tokenizer_list = [] if self.test_slow_tokenizer: tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())) if self.test_rust_tokenizer: tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())) for tokenizer_class, tokenizer_utils in tokenizer_list: with tempfile.TemporaryDirectory() as tmp_dir: tokenizer_utils.save_pretrained(tmp_dir) with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file: special_tokens_map = json.load(json_file) with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file: tokenizer_config = json.load(json_file) added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)] special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [ "an_additional_special_token" ] tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [ "an_additional_special_token" ] with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile: json.dump(special_tokens_map, outfile) with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile: json.dump(tokenizer_config, outfile) # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and # "special_tokens_map.json" files tokenizer_without_change_in_init = tokenizer_class.from_pretrained( tmp_dir, ) self.assertIn( "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens ) # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab self.assertEqual( ["an_additional_special_token"], tokenizer_without_change_in_init.convert_ids_to_tokens( tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]) ), ) # Now we test that we can change the value of additional_special_tokens in the from_pretrained new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)] tokenizer = tokenizer_class.from_pretrained( tmp_dir, additional_special_tokens=new_added_tokens, ) self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens) self.assertEqual( ["a_new_additional_special_token"], tokenizer.convert_ids_to_tokens( tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]) ), ) # overwritten from `test_tokenization_common` since T5 has no max length def test_pretrained_model_lists(self): # We should have at least one default checkpoint for each tokenizer # We should specify the max input length as well (used in some part to list the pretrained checkpoints) self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1) self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1) @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="t5-base", revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b", )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./scripts/fsmt/eval-allenai-wmt19.sh

#!/usr/bin/env bash # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script evals the following fsmt models # it covers: # - allenai/wmt19-de-en-6-6-base # - allenai/wmt19-de-en-6-6-big # this script needs to be run from the top level of the transformers repo if [ ! -d "src/transformers" ]; then echo "Error: This script needs to be run from the top of the transformers repo" exit 1 fi # In these scripts you may have to lower BS if you get CUDA OOM (or increase it if you have a large GPU) ### Normal eval ### export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=64 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target MODEL_PATH=allenai/wmt19-de-en-6-6-base echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS MODEL_PATH=allenai/wmt19-de-en-6-6-big echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ### Searching hparams eval ### export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=16 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target MODEL_PATH=allenai/wmt19-de-en-6-6-base echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:10:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1" MODEL_PATH=allenai/wmt19-de-en-6-6-big echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:10:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1"

#!/usr/bin/env bash # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script evals the following fsmt models # it covers: # - allenai/wmt19-de-en-6-6-base # - allenai/wmt19-de-en-6-6-big # this script needs to be run from the top level of the transformers repo if [ ! -d "src/transformers" ]; then echo "Error: This script needs to be run from the top of the transformers repo" exit 1 fi # In these scripts you may have to lower BS if you get CUDA OOM (or increase it if you have a large GPU) ### Normal eval ### export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=64 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target MODEL_PATH=allenai/wmt19-de-en-6-6-base echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS MODEL_PATH=allenai/wmt19-de-en-6-6-big echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ### Searching hparams eval ### export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=16 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target MODEL_PATH=allenai/wmt19-de-en-6-6-base echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:10:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1" MODEL_PATH=allenai/wmt19-de-en-6-6-big echo $PAIR $MODEL_PATH PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_PATH $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:10:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1"

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/xlnet/test_modeling_xlnet.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import unittest from transformers import XLNetConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( XLNetForMultipleChoice, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForSequenceClassification, XLNetForTokenClassification, XLNetLMHeadModel, XLNetModel, ) from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST class XLNetModelTester: def __init__( self, parent, batch_size=14, seq_length=7, mem_len=10, clamp_len=-1, reuse_len=15, is_training=True, use_labels=True, vocab_size=99, cutoffs=[10, 50, 80], hidden_size=32, num_attention_heads=4, d_inner=128, num_hidden_layers=5, type_sequence_label_size=2, untie_r=True, bi_data=False, same_length=False, initializer_range=0.05, seed=1, type_vocab_size=2, bos_token_id=1, eos_token_id=2, pad_token_id=5, num_choices=4, ): self.parent = parent self.batch_size = 14 self.seq_length = 7 self.mem_len = 10 # self.key_len = seq_length + mem_len self.clamp_len = -1 self.reuse_len = 15 self.is_training = True self.use_labels = True self.vocab_size = 99 self.cutoffs = [10, 50, 80] self.hidden_size = 32 self.num_attention_heads = 4 self.d_inner = 128 self.num_hidden_layers = 5 self.type_sequence_label_size = 2 self.untie_r = True self.bi_data = False self.same_length = False self.initializer_range = 0.05 self.seed = 1 self.type_vocab_size = 2 self.bos_token_id = 1 self.eos_token_id = 2 self.pad_token_id = 5 self.num_choices = 4 def prepare_config_and_inputs(self): input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) input_mask = random_attention_mask([self.batch_size, self.seq_length]) input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size) perm_mask = torch.zeros( self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device, ) perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token target_mapping = torch.zeros( self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device, ) target_mapping[:, 0, -1] = 1.0 # predict last token sequence_labels = None lm_labels = None is_impossible_labels = None token_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) is_impossible_labels = ids_tensor([self.batch_size], 2).float() token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) config = self.get_config() return ( config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ) def get_config(self): return XLNetConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, n_head=self.num_attention_heads, d_inner=self.d_inner, n_layer=self.num_hidden_layers, untie_r=self.untie_r, mem_len=self.mem_len, clamp_len=self.clamp_len, same_length=self.same_length, reuse_len=self.reuse_len, bi_data=self.bi_data, initializer_range=self.initializer_range, num_labels=self.type_sequence_label_size, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, eos_token_id=self.eos_token_id, ) def set_seed(self): random.seed(self.seed) torch.manual_seed(self.seed) def create_and_check_xlnet_base_model( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetModel(config) model.to(torch_device) model.eval() result = model(input_ids_1, input_mask=input_mask) result = model(input_ids_1, attention_mask=input_mask) result = model(input_ids_1, token_type_ids=segment_ids) result = model(input_ids_1) config.mem_len = 0 model = XLNetModel(config) model.to(torch_device) model.eval() base_model_output = model(input_ids_1) self.parent.assertEqual(len(base_model_output), 2) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_use_mems_train( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForSequenceClassification(config) model.to(torch_device) model.train() train_size = input_ids_1.shape[0] batch_size = 4 for i in range(train_size // batch_size + 1): input_ids = input_ids_1[i : (i + 1) * batch_size] labels = sequence_labels[i : (i + 1) * batch_size] outputs = model(input_ids=input_ids, labels=labels, return_dict=True) self.parent.assertIsNone(outputs.mems) self.parent.assertIsNotNone(outputs.loss) def create_and_check_xlnet_model_use_mems( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetModel(config=config) model.to(torch_device) model.eval() # first forward pass causal_mask = torch.ones( input_ids_1.shape[0], input_ids_1.shape[1], input_ids_1.shape[1], dtype=torch.float, device=torch_device, ) causal_mask = torch.triu(causal_mask, diagonal=0) outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask) outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask) outputs_conf = model(input_ids_1) self.parent.assertTrue(len(outputs_cache) == len(outputs_conf)) self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1) output, mems = outputs_cache.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1) # causal mask causal_mask = torch.ones( input_ids_1.shape[0], input_ids_1.shape[1] + 1, input_ids_1.shape[1] + 1, dtype=torch.float, device=torch_device, ) causal_mask = torch.triu(causal_mask, diagonal=0) single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device) # second forward pass output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"] output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_xlnet_base_model_with_att_output( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetModel(config) model.to(torch_device) model.eval() attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"] self.parent.assertEqual(len(attentions), config.n_layer) self.parent.assertIsInstance(attentions[0], tuple) self.parent.assertEqual(len(attentions[0]), 2) self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape) def create_and_check_xlnet_lm_head( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetLMHeadModel(config) model.to(torch_device) model.eval() result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels) result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems) _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping) self.parent.assertEqual(result1.loss.shape, ()) self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in result1.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) self.parent.assertEqual(result2.loss.shape, ()) self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in result2.mems], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_xlnet_qa( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids_1) result_with_labels = model( input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, p_mask=input_mask, ) result_with_labels = model( input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, ) total_loss, mems = result_with_labels.to_tuple() result_with_labels = model( input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels, ) total_loss, mems = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape, ()) self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual( result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_xlnet_token_classif( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids_1) result = model(input_ids_1, labels=token_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_xlnet_sequence_classif( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids_1) result = model(input_ids_1, labels=sequence_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids_1} return config, inputs_dict @require_torch class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( XLNetModel, XLNetLMHeadModel, XLNetForTokenClassification, XLNetForSequenceClassification, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForMultipleChoice, ) if is_torch_available() else () ) all_generative_model_classes = ( (XLNetLMHeadModel,) if is_torch_available() else () ) # TODO (PVP): Check other models whether language generation is also applicable fx_compatible = False test_pruning = False # XLNet has 2 QA models -> need to manually set the correct labels for one of them here def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "XLNetForQuestionAnswering": inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = XLNetModelTester(self) self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37) def test_config(self): self.config_tester.run_common_tests() def test_xlnet_base_model(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs) def test_xlnet_base_model_use_mems(self): # checking that in auto-regressive mode, `use_mems` gives the same results self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs) def test_seq_classification_use_mems_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_use_mems_train(*config_and_inputs) def test_xlnet_base_model_with_att_output(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs) def test_xlnet_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) def test_xlnet_sequence_classif(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs) def test_xlnet_token_classif(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs) def test_xlnet_qa(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_qa(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # xlnet cannot keep gradients in attentions or hidden states return # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]: if hasattr(module, param) and getattr(module, param) is not None: weight = getattr(module, param) weight.data.fill_(3) def _check_hidden_states_for_generate( self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(hidden_states, tuple) self.assertListEqual( [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states], [True] * len(hidden_states), ) self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups) for idx, iter_hidden_states in enumerate(hidden_states): # check hidden size for i, layer_hidden_states in enumerate(iter_hidden_states): # every 2nd tensor is from extra stream if i % 2 != 0: seq_len = 1 else: # for first item dummy PAD token is appended so need one more seq_len = (min_length + 1) if idx == 0 else min_length expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size) self.assertEqual(layer_hidden_states.shape, expected_shape) def _check_attentions_for_generate( self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(attentions, tuple) self.assertListEqual( [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions) ) self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups) for idx, attentions_item in enumerate(attentions): for iter_attentions in attentions_item: tgt_len = min_length # for first item dummy PAD token is appended so need one more if idx == 0: tgt_len += 1 src_len = min_length + idx + 1 expected_shape = ( batch_size * num_beam_groups, config.num_attention_heads, tgt_len, src_len, ) # check attn size self.assertListEqual( [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions), ) @slow def test_model_from_pretrained(self): for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XLNetModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class XLNetModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_xlnet_base_cased(self): model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased") model.to(torch_device) # fmt: off input_ids = torch.tensor( [ [ 67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, ] ], dtype=torch.long, device=torch_device, ) # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family # (except for Alexei and Maria) are discovered. # The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the # remainder of the story. 1883 Western Siberia, # a young Grigori Rasputin is asked by his father and a group of men to perform magic. # Rasputin has a vision and denounces one of the men as a horse thief. Although his # father initially slaps him for making such an accusation, Rasputin watches as the # man is chased outside and beaten. Twenty years later, Rasputin sees a vision of # the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, # with people, even a bishop, begging for his blessing. """ # fmt: off expected_output_ids = [ 67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, ] # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) # are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, # narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin # is asked by his father and a group of men to perform magic. Rasputin has a vision and # denounces one of the men as a horse thief. Although his father initially slaps # him for making such an accusation, Rasputin watches as the man is chased outside and beaten. # Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest. # Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing. # <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary. # He is asked to perform a ritual of the Virgin Mary. He is asked to perform output_ids = model.generate(input_ids, max_length=200, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import unittest from transformers import XLNetConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( XLNetForMultipleChoice, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForSequenceClassification, XLNetForTokenClassification, XLNetLMHeadModel, XLNetModel, ) from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST class XLNetModelTester: def __init__( self, parent, batch_size=14, seq_length=7, mem_len=10, clamp_len=-1, reuse_len=15, is_training=True, use_labels=True, vocab_size=99, cutoffs=[10, 50, 80], hidden_size=32, num_attention_heads=4, d_inner=128, num_hidden_layers=5, type_sequence_label_size=2, untie_r=True, bi_data=False, same_length=False, initializer_range=0.05, seed=1, type_vocab_size=2, bos_token_id=1, eos_token_id=2, pad_token_id=5, num_choices=4, ): self.parent = parent self.batch_size = 14 self.seq_length = 7 self.mem_len = 10 # self.key_len = seq_length + mem_len self.clamp_len = -1 self.reuse_len = 15 self.is_training = True self.use_labels = True self.vocab_size = 99 self.cutoffs = [10, 50, 80] self.hidden_size = 32 self.num_attention_heads = 4 self.d_inner = 128 self.num_hidden_layers = 5 self.type_sequence_label_size = 2 self.untie_r = True self.bi_data = False self.same_length = False self.initializer_range = 0.05 self.seed = 1 self.type_vocab_size = 2 self.bos_token_id = 1 self.eos_token_id = 2 self.pad_token_id = 5 self.num_choices = 4 def prepare_config_and_inputs(self): input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) input_mask = random_attention_mask([self.batch_size, self.seq_length]) input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size) perm_mask = torch.zeros( self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device, ) perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token target_mapping = torch.zeros( self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device, ) target_mapping[:, 0, -1] = 1.0 # predict last token sequence_labels = None lm_labels = None is_impossible_labels = None token_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) is_impossible_labels = ids_tensor([self.batch_size], 2).float() token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) config = self.get_config() return ( config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ) def get_config(self): return XLNetConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, n_head=self.num_attention_heads, d_inner=self.d_inner, n_layer=self.num_hidden_layers, untie_r=self.untie_r, mem_len=self.mem_len, clamp_len=self.clamp_len, same_length=self.same_length, reuse_len=self.reuse_len, bi_data=self.bi_data, initializer_range=self.initializer_range, num_labels=self.type_sequence_label_size, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, eos_token_id=self.eos_token_id, ) def set_seed(self): random.seed(self.seed) torch.manual_seed(self.seed) def create_and_check_xlnet_base_model( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetModel(config) model.to(torch_device) model.eval() result = model(input_ids_1, input_mask=input_mask) result = model(input_ids_1, attention_mask=input_mask) result = model(input_ids_1, token_type_ids=segment_ids) result = model(input_ids_1) config.mem_len = 0 model = XLNetModel(config) model.to(torch_device) model.eval() base_model_output = model(input_ids_1) self.parent.assertEqual(len(base_model_output), 2) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_use_mems_train( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForSequenceClassification(config) model.to(torch_device) model.train() train_size = input_ids_1.shape[0] batch_size = 4 for i in range(train_size // batch_size + 1): input_ids = input_ids_1[i : (i + 1) * batch_size] labels = sequence_labels[i : (i + 1) * batch_size] outputs = model(input_ids=input_ids, labels=labels, return_dict=True) self.parent.assertIsNone(outputs.mems) self.parent.assertIsNotNone(outputs.loss) def create_and_check_xlnet_model_use_mems( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetModel(config=config) model.to(torch_device) model.eval() # first forward pass causal_mask = torch.ones( input_ids_1.shape[0], input_ids_1.shape[1], input_ids_1.shape[1], dtype=torch.float, device=torch_device, ) causal_mask = torch.triu(causal_mask, diagonal=0) outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask) outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask) outputs_conf = model(input_ids_1) self.parent.assertTrue(len(outputs_cache) == len(outputs_conf)) self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1) output, mems = outputs_cache.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1) # causal mask causal_mask = torch.ones( input_ids_1.shape[0], input_ids_1.shape[1] + 1, input_ids_1.shape[1] + 1, dtype=torch.float, device=torch_device, ) causal_mask = torch.triu(causal_mask, diagonal=0) single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device) # second forward pass output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"] output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_xlnet_base_model_with_att_output( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetModel(config) model.to(torch_device) model.eval() attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"] self.parent.assertEqual(len(attentions), config.n_layer) self.parent.assertIsInstance(attentions[0], tuple) self.parent.assertEqual(len(attentions[0]), 2) self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape) def create_and_check_xlnet_lm_head( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetLMHeadModel(config) model.to(torch_device) model.eval() result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels) result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems) _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping) self.parent.assertEqual(result1.loss.shape, ()) self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in result1.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) self.parent.assertEqual(result2.loss.shape, ()) self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in result2.mems], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_xlnet_qa( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids_1) result_with_labels = model( input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, p_mask=input_mask, ) result_with_labels = model( input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, ) total_loss, mems = result_with_labels.to_tuple() result_with_labels = model( input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels, ) total_loss, mems = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape, ()) self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual( result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_xlnet_token_classif( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids_1) result = model(input_ids_1, labels=token_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_xlnet_sequence_classif( self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ): model = XLNetForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids_1) result = model(input_ids_1, labels=sequence_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) self.parent.assertListEqual( [mem.shape for mem in result.mems], [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids_1} return config, inputs_dict @require_torch class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( XLNetModel, XLNetLMHeadModel, XLNetForTokenClassification, XLNetForSequenceClassification, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForMultipleChoice, ) if is_torch_available() else () ) all_generative_model_classes = ( (XLNetLMHeadModel,) if is_torch_available() else () ) # TODO (PVP): Check other models whether language generation is also applicable fx_compatible = False test_pruning = False # XLNet has 2 QA models -> need to manually set the correct labels for one of them here def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "XLNetForQuestionAnswering": inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = XLNetModelTester(self) self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37) def test_config(self): self.config_tester.run_common_tests() def test_xlnet_base_model(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs) def test_xlnet_base_model_use_mems(self): # checking that in auto-regressive mode, `use_mems` gives the same results self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs) def test_seq_classification_use_mems_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_use_mems_train(*config_and_inputs) def test_xlnet_base_model_with_att_output(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs) def test_xlnet_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) def test_xlnet_sequence_classif(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs) def test_xlnet_token_classif(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs) def test_xlnet_qa(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_qa(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # xlnet cannot keep gradients in attentions or hidden states return # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]: if hasattr(module, param) and getattr(module, param) is not None: weight = getattr(module, param) weight.data.fill_(3) def _check_hidden_states_for_generate( self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(hidden_states, tuple) self.assertListEqual( [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states], [True] * len(hidden_states), ) self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups) for idx, iter_hidden_states in enumerate(hidden_states): # check hidden size for i, layer_hidden_states in enumerate(iter_hidden_states): # every 2nd tensor is from extra stream if i % 2 != 0: seq_len = 1 else: # for first item dummy PAD token is appended so need one more seq_len = (min_length + 1) if idx == 0 else min_length expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size) self.assertEqual(layer_hidden_states.shape, expected_shape) def _check_attentions_for_generate( self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(attentions, tuple) self.assertListEqual( [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions) ) self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups) for idx, attentions_item in enumerate(attentions): for iter_attentions in attentions_item: tgt_len = min_length # for first item dummy PAD token is appended so need one more if idx == 0: tgt_len += 1 src_len = min_length + idx + 1 expected_shape = ( batch_size * num_beam_groups, config.num_attention_heads, tgt_len, src_len, ) # check attn size self.assertListEqual( [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions), ) @slow def test_model_from_pretrained(self): for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = XLNetModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class XLNetModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_xlnet_base_cased(self): model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased") model.to(torch_device) # fmt: off input_ids = torch.tensor( [ [ 67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, ] ], dtype=torch.long, device=torch_device, ) # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family # (except for Alexei and Maria) are discovered. # The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the # remainder of the story. 1883 Western Siberia, # a young Grigori Rasputin is asked by his father and a group of men to perform magic. # Rasputin has a vision and denounces one of the men as a horse thief. Although his # father initially slaps him for making such an accusation, Rasputin watches as the # man is chased outside and beaten. Twenty years later, Rasputin sees a vision of # the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, # with people, even a bishop, begging for his blessing. """ # fmt: off expected_output_ids = [ 67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, ] # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) # are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, # narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin # is asked by his father and a group of men to perform magic. Rasputin has a vision and # denounces one of the men as a horse thief. Although his father initially slaps # him for making such an accusation, Rasputin watches as the man is chased outside and beaten. # Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest. # Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing. # <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary. # He is asked to perform a ritual of the Virgin Mary. He is asked to perform output_ids = model.generate(input_ids, max_length=200, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/time_series_transformer/modeling_time_series_transformer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Time Series Transformer model.""" import random from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.distributions import ( AffineTransform, Distribution, Independent, NegativeBinomial, Normal, StudentT, TransformedDistribution, ) from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, ModelOutput from ...modeling_utils import PreTrainedModel from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_time_series_transformer import TimeSeriesTransformerConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "TimeSeriesTransformerConfig" TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "huggingface/time-series-transformer-tourism-monthly", # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer ] class AffineTransformed(TransformedDistribution): def __init__(self, base_distribution: Distribution, loc=None, scale=None, event_dim=0): self.scale = 1.0 if scale is None else scale self.loc = 0.0 if loc is None else loc super().__init__(base_distribution, [AffineTransform(loc=self.loc, scale=self.scale, event_dim=event_dim)]) @property def mean(self): """ Returns the mean of the distribution. """ return self.base_dist.mean * self.scale + self.loc @property def variance(self): """ Returns the variance of the distribution. """ return self.base_dist.variance * self.scale**2 @property def stddev(self): """ Returns the standard deviation of the distribution. """ return self.variance.sqrt() class ParameterProjection(nn.Module): def __init__( self, in_features: int, args_dim: Dict[str, int], domain_map: Callable[..., Tuple[torch.Tensor]], **kwargs ) -> None: super().__init__(**kwargs) self.args_dim = args_dim self.proj = nn.ModuleList([nn.Linear(in_features, dim) for dim in args_dim.values()]) self.domain_map = domain_map def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]: params_unbounded = [proj(x) for proj in self.proj] return self.domain_map(*params_unbounded) class LambdaLayer(nn.Module): def __init__(self, function): super().__init__() self.function = function def forward(self, x, *args): return self.function(x, *args) class DistributionOutput: distribution_class: type in_features: int args_dim: Dict[str, int] def __init__(self, dim: int = 1) -> None: self.dim = dim self.args_dim = {k: dim * self.args_dim[k] for k in self.args_dim} def _base_distribution(self, distr_args): if self.dim == 1: return self.distribution_class(*distr_args) else: return Independent(self.distribution_class(*distr_args), 1) def distribution( self, distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None, ) -> Distribution: distr = self._base_distribution(distr_args) if loc is None and scale is None: return distr else: return AffineTransformed(distr, loc=loc, scale=scale, event_dim=self.event_dim) @property def event_shape(self) -> Tuple: r""" Shape of each individual event contemplated by the distributions that this object constructs. """ return () if self.dim == 1 else (self.dim,) @property def event_dim(self) -> int: r""" Number of event dimensions, i.e., length of the `event_shape` tuple, of the distributions that this object constructs. """ return len(self.event_shape) @property def value_in_support(self) -> float: r""" A float that will have a valid numeric value when computing the log-loss of the corresponding distribution. By default 0.0. This value will be used when padding data series. """ return 0.0 def get_parameter_projection(self, in_features: int) -> nn.Module: r""" Return the parameter projection layer that maps the input to the appropriate parameters of the distribution. """ return ParameterProjection( in_features=in_features, args_dim=self.args_dim, domain_map=LambdaLayer(self.domain_map), ) def domain_map(self, *args: torch.Tensor): r""" Converts arguments to the right shape and domain. The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape. """ raise NotImplementedError() @classmethod def squareplus(cls, x: torch.Tensor) -> torch.Tensor: r""" Helper to map inputs to the positive orthant by applying the square-plus operation. Reference: https://twitter.com/jon_barron/status/1387167648669048833 """ return (x + torch.sqrt(torch.square(x) + 4.0)) / 2.0 class StudentTOutput(DistributionOutput): args_dim: Dict[str, int] = {"df": 1, "loc": 1, "scale": 1} distribution_class: type = StudentT @classmethod def domain_map(cls, df: torch.Tensor, loc: torch.Tensor, scale: torch.Tensor): scale = cls.squareplus(scale) df = 2.0 + cls.squareplus(df) return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1) class NormalOutput(DistributionOutput): args_dim: Dict[str, int] = {"loc": 1, "scale": 1} distribution_class: type = Normal @classmethod def domain_map(cls, loc: torch.Tensor, scale: torch.Tensor): scale = cls.squareplus(scale) return loc.squeeze(-1), scale.squeeze(-1) class NegativeBinomialOutput(DistributionOutput): args_dim: Dict[str, int] = {"total_count": 1, "logits": 1} distribution_class: type = NegativeBinomial @classmethod def domain_map(cls, total_count: torch.Tensor, logits: torch.Tensor): total_count = cls.squareplus(total_count) return total_count.squeeze(-1), logits.squeeze(-1) def _base_distribution(self, distr_args) -> Distribution: total_count, logits = distr_args if self.dim == 1: return self.distribution_class(total_count=total_count, logits=logits) else: return Independent(self.distribution_class(total_count=total_count, logits=logits), 1) # Overwrites the parent class method. We cannot scale using the affine # transformation since negative binomial should return integers. Instead # we scale the parameters. def distribution( self, distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None ) -> Distribution: total_count, logits = distr_args if scale is not None: # See scaling property of Gamma. logits += scale.log() return self._base_distribution((total_count, logits)) class FeatureEmbedder(nn.Module): def __init__(self, cardinalities: List[int], embedding_dims: List[int]) -> None: super().__init__() self.num_features = len(cardinalities) self.embedders = nn.ModuleList([nn.Embedding(c, d) for c, d in zip(cardinalities, embedding_dims)]) def forward(self, features: torch.Tensor) -> torch.Tensor: if self.num_features > 1: # we slice the last dimension, giving an array of length # self.num_features with shape (N,T) or (N) cat_feature_slices = torch.chunk(features, self.num_features, dim=-1) else: cat_feature_slices = [features] return torch.cat( [ embed(cat_feature_slice.squeeze(-1)) for embed, cat_feature_slice in zip(self.embedders, cat_feature_slices) ], dim=-1, ) class MeanScaler(nn.Module): """ Computes a scaling factor as the weighted average absolute value along dimension `dim`, and scales the data accordingly. Args: dim (`int`): Dimension along which to compute the scale. keepdim (`bool`, *optional*, defaults to `False`): Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it. minimum_scale (`float`, *optional*, defaults to 1e-10): Default scale that is used for elements that are constantly zero along dimension `dim`. """ def __init__(self, dim: int, keepdim: bool = False, minimum_scale: float = 1e-10): super().__init__() if not dim > 0: raise ValueError("Cannot compute scale along dim = 0 (batch dimension), please provide dim > 0") self.dim = dim self.keepdim = keepdim self.register_buffer("minimum_scale", torch.tensor(minimum_scale)) def forward(self, data: torch.Tensor, weights: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # these will have shape (N, C) total_weight = weights.sum(dim=self.dim) weighted_sum = (data.abs() * weights).sum(dim=self.dim) # first compute a global scale per-dimension total_observed = total_weight.sum(dim=0) denominator = torch.max(total_observed, torch.ones_like(total_observed)) default_scale = weighted_sum.sum(dim=0) / denominator # then compute a per-item, per-dimension scale denominator = torch.max(total_weight, torch.ones_like(total_weight)) scale = weighted_sum / denominator # use per-batch scale when no element is observed # or when the sequence contains only zeros scale = ( torch.max( self.minimum_scale, torch.where( weighted_sum > torch.zeros_like(weighted_sum), scale, default_scale * torch.ones_like(total_weight), ), ) .detach() .unsqueeze(dim=self.dim) ) return data / scale, scale if self.keepdim else scale.squeeze(dim=self.dim) class NOPScaler(nn.Module): """ Assigns a scaling factor equal to 1 along dimension `dim`, and therefore applies no scaling to the input data. Args: dim (`int`): Dimension along which to compute the scale. keepdim (`bool`, *optional*, defaults to `False`): Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it. """ def __init__(self, dim: int, keepdim: bool = False): super().__init__() self.dim = dim self.keepdim = keepdim def forward(self, data: torch.Tensor, observed_indicator: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: scale = torch.ones_like(data).mean(dim=self.dim, keepdim=self.keepdim) return data, scale def weighted_average(input_tensor: torch.Tensor, weights: Optional[torch.Tensor] = None, dim=None) -> torch.Tensor: """ Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero, meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`. Args: input_tensor (`torch.FloatTensor`): Input tensor, of which the average must be computed. weights (`torch.FloatTensor`, *optional*): Weights tensor, of the same shape as `input_tensor`. dim (`int`, *optional*): The dim along which to average `input_tensor`. Returns: `torch.FloatTensor`: The tensor with values averaged along the specified `dim`. """ if weights is not None: weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor)) sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0) return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights else: return input_tensor.mean(dim=dim) class NegativeLogLikelihood: """ Computes the negative log likelihood loss from input distribution with respect to target. """ def __call__(self, input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor: return -input.log_prob(target) # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min)) mask_cond = torch.arange(mask.size(-1)) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) @dataclass class Seq2SeqTimeSeriesModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. scale: (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): Scaling values of each time series' context window which is used to give the model inputs of the same magnitude and then used to rescale to the original scale. static_features: (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*): Static features of each time series' in a batch which are copied to the covariates at inference time. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None scale: Optional[torch.FloatTensor] = None static_features: Optional[torch.FloatTensor] = None @dataclass class Seq2SeqTimeSeriesPredictionOutput(ModelOutput): """ Base class for model's predictions outputs that also contain the loss as well parameters of the chosen distribution. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when a `future_values` is provided): Distributional loss. params (`torch.FloatTensor` of shape `(batch_size, num_samples, num_params)`): Parameters of the chosen distribution. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. scale: (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): Scaling values of each time series' context window which is used to give the model inputs of the same magnitude and then used to rescale to the original scale. static_features: (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*): Static features of each time series' in a batch which are copied to the covariates at inference time. """ loss: Optional[torch.FloatTensor] = None params: Optional[Tuple[torch.FloatTensor]] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None scale: Optional[torch.FloatTensor] = None static_features: Optional[torch.FloatTensor] = None @dataclass class SampleTimeSeriesPredictionOutput(ModelOutput): sequences: torch.FloatTensor = None # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->TimeSeriesTransformer class TimeSeriesTransformerAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value # Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->TimeSeriesTransformer class TimeSeriesTransformerEncoderLayer(nn.Module): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = TimeSeriesTransformerAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->TimeSeriesTransformer class TimeSeriesTransformerDecoderLayer(nn.Module): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = TimeSeriesTransformerAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = TimeSeriesTransformerAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs class TimeSeriesTransformerPreTrainedModel(PreTrainedModel): config_class = TimeSeriesTransformerConfig base_model_prefix = "model" main_input_name = "past_values" supports_gradient_checkpointing = True def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (TimeSeriesTransformerDecoder, TimeSeriesTransformerEncoder)): module.gradient_checkpointing = value TIME_SERIES_TRANSFORMER_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`TimeSeriesTransformerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING = r""" Args: past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Past values of the time series, that serve as context in order to predict the future. These values may contain lags, i.e. additional values from the past which are added in order to serve as "extra context". The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as `static_categorical_features`, `static_real_features`, `past_time_features`). The sequence length here is equal to `context_length` + `max(config.lags_sequence)`. Missing values need to be replaced with zeros. past_time_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_features)`, *optional*): Optional time features, which the model internally will add to `past_values`. These could be things like "month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These could also be so-called "age" features, which basically help the model know "at which point in life" a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`. past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in `[0, 1]`: - 1 for values that are **observed**, - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). static_categorical_features (`torch.LongTensor` of shape `(batch_size, number of static categorical features)`, *optional*): Optional static categorical features for which the model will learn an embedding, which it will add to the values of the time series. Static categorical features are features which have the same value for all time steps (static over time). A typical example of a static categorical feature is a time series ID. static_real_features (`torch.FloatTensor` of shape `(batch_size, number of static real features)`, *optional*): Optional static real features which the model will add to the values of the time series. Static real features are features which have the same value for all time steps (static over time). A typical example of a static real feature is promotion information. future_values (`torch.FloatTensor` of shape `(batch_size, prediction_length)`): Future values of the time series, that serve as labels for the model. The `future_values` is what the Transformer needs to learn to output, given the `past_values`. See the demo notebook and code snippets for details. Missing values need to be replaced with zeros. future_time_features (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_features)`, *optional*): Optional time features, which the model internally will add to `future_values`. These could be things like "month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These could also be so-called "age" features, which basically help the model know "at which point in life" a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on certain token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of `last_hidden_state`, `hidden_states` (*optional*) and `attentions` (*optional*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` (*optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class TimeSeriesTransformerEncoder(TimeSeriesTransformerPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`TimeSeriesTransformerEncoderLayer`]. Args: config: TimeSeriesTransformerConfig """ def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.layers = nn.ModuleList([TimeSeriesTransformerEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = inputs_embeds hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class TimeSeriesTransformerDecoder(TimeSeriesTransformerPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TimeSeriesTransformerDecoderLayer`] Args: config: TimeSeriesTransformerConfig """ def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.layers = nn.ModuleList([TimeSeriesTransformerDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length ).to(inputs_embeds.device) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: r""" Args: attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_shape = inputs_embeds.size()[:-1] # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) hidden_states = inputs_embeds hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, output_attentions, use_cache) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare Time Series Transformer Model outputting raw hidden-states without any specific head on top.", TIME_SERIES_TRANSFORMER_START_DOCSTRING, ) class TimeSeriesTransformerModel(TimeSeriesTransformerPreTrainedModel): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) if config.scaling: self.scaler = MeanScaler(dim=1, keepdim=True) else: self.scaler = NOPScaler(dim=1, keepdim=True) self.embedder = FeatureEmbedder( cardinalities=config.cardinality, embedding_dims=config.embedding_dimension, ) # transformer encoder-decoder and mask initializer self.encoder = TimeSeriesTransformerEncoder(config) self.decoder = TimeSeriesTransformerDecoder(config) # Initialize weights and apply final processing self.post_init() @property def _past_length(self) -> int: return self.config.context_length + max(self.config.lags_sequence) def get_lagged_subsequences( self, sequence: torch.Tensor, subsequences_length: int, shift: int = 0 ) -> torch.Tensor: """ Returns lagged subsequences of a given sequence. Returns a tensor of shape (N, S, C, I), where S = subsequences_length and I = len(indices), containing lagged subsequences. Specifically, lagged[i, j, :, k] = sequence[i, -indices[k]-S+j, :]. Args: sequence: Tensor The sequence from which lagged subsequences should be extracted. Shape: (N, T, C). subsequences_length : int Length of the subsequences to be extracted. shift: int Shift the lags by this amount back. """ sequence_length = sequence.shape[1] indices = [lag - shift for lag in self.config.lags_sequence] try: assert max(indices) + subsequences_length <= sequence_length, ( f"lags cannot go further than history length, found lag {max(indices)} " f"while history length is only {sequence_length}" ) except AssertionError as e: e.args += (max(indices), sequence_length) raise lagged_values = [] for lag_index in indices: begin_index = -lag_index - subsequences_length end_index = -lag_index if lag_index > 0 else None lagged_values.append(sequence[:, begin_index:end_index, ...]) return torch.stack(lagged_values, dim=-1) def create_network_inputs( self, past_values: torch.Tensor, past_time_features: torch.Tensor, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, past_observed_mask: Optional[torch.Tensor] = None, future_values: Optional[torch.Tensor] = None, future_time_features: Optional[torch.Tensor] = None, ): # time feature time_feat = ( torch.cat( ( past_time_features[:, self._past_length - self.config.context_length :, ...], future_time_features, ), dim=1, ) if future_values is not None else past_time_features[:, self._past_length - self.config.context_length :, ...] ) # target if past_observed_mask is None: past_observed_mask = torch.ones_like(past_values) context = past_values[:, -self.config.context_length :] observed_context = past_observed_mask[:, -self.config.context_length :] _, scale = self.scaler(context, observed_context) inputs = ( torch.cat((past_values, future_values), dim=1) / scale if future_values is not None else past_values / scale ) inputs_length = ( self._past_length + self.config.prediction_length if future_values is not None else self._past_length ) try: assert inputs.shape[1] == inputs_length, ( f"input length {inputs.shape[1]} and dynamic feature lengths {inputs_length} does not match", ) except AssertionError as e: e.args += (inputs.shape[1], inputs_length) raise subsequences_length = ( self.config.context_length + self.config.prediction_length if future_values is not None else self.config.context_length ) # embeddings embedded_cat = self.embedder(static_categorical_features) # static features log_scale = scale.log() if self.config.input_size == 1 else scale.squeeze(1).log() static_feat = torch.cat((embedded_cat, static_real_features, log_scale), dim=1) expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1) # all features features = torch.cat((expanded_static_feat, time_feat), dim=-1) lagged_sequence = self.get_lagged_subsequences(sequence=inputs, subsequences_length=subsequences_length) lags_shape = lagged_sequence.shape reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1) transformer_inputs = torch.cat((reshaped_lagged_sequence, features), dim=-1) return transformer_inputs, scale, static_feat def enc_dec_outputs(self, transformer_inputs): enc_input = transformer_inputs[:, : self.config.context_length, ...] dec_input = transformer_inputs[:, self.config.context_length :, ...] encoder_outputs = self.encoder(inputs_embeds=enc_input) decoder_outputs = self.decoder( inputs_embeds=dec_input, encoder_hidden_states=encoder_outputs.last_hidden_state ) return encoder_outputs, decoder_outputs def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqTimeSeriesModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, past_values: torch.Tensor, past_time_features: torch.Tensor, past_observed_mask: torch.Tensor, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, future_values: Optional[torch.Tensor] = None, future_time_features: Optional[torch.Tensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, use_cache: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Seq2SeqTimeSeriesModelOutput, Tuple]: r""" Returns: Examples: ```python >>> from huggingface_hub import hf_hub_download >>> import torch >>> from transformers import TimeSeriesTransformerModel >>> file = hf_hub_download( ... repo_id="kashif/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset" ... ) >>> batch = torch.load(file) >>> model = TimeSeriesTransformerModel.from_pretrained("huggingface/time-series-transformer-tourism-monthly") >>> # during training, one provides both past and future values >>> # as well as possible additional features >>> outputs = model( ... past_values=batch["past_values"], ... past_time_features=batch["past_time_features"], ... past_observed_mask=batch["past_observed_mask"], ... static_categorical_features=batch["static_categorical_features"], ... static_real_features=batch["static_real_features"], ... future_values=batch["future_values"], ... future_time_features=batch["future_time_features"], ... ) >>> last_hidden_state = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_inputs, scale, static_feat = self.create_network_inputs( past_values=past_values, past_time_features=past_time_features, past_observed_mask=past_observed_mask, static_categorical_features=static_categorical_features, static_real_features=static_real_features, future_values=future_values, future_time_features=future_time_features, ) if encoder_outputs is None: enc_input = transformer_inputs[:, : self.config.context_length, ...] encoder_outputs = self.encoder( inputs_embeds=enc_input, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) dec_input = transformer_inputs[:, self.config.context_length :, ...] decoder_outputs = self.decoder( inputs_embeds=dec_input, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs + (scale, static_feat) return Seq2SeqTimeSeriesModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, scale=scale, static_features=static_feat, ) @add_start_docstrings( "The Time Series Transformer Model with a distribution head on top for time-series forecasting.", TIME_SERIES_TRANSFORMER_START_DOCSTRING, ) class TimeSeriesTransformerForPrediction(TimeSeriesTransformerPreTrainedModel): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) self.model = TimeSeriesTransformerModel(config) if config.distribution_output == "student_t": self.distribution_output = StudentTOutput(dim=config.input_size) elif config.distribution_output == "normal": self.distribution_output = NormalOutput(dim=config.input_size) elif config.distribution_output == "negative_binomial": self.distribution_output = NegativeBinomialOutput(dim=config.input_size) else: raise ValueError(f"Unknown distribution output {config.distribution_output}") self.parameter_projection = self.distribution_output.get_parameter_projection(self.model.config.d_model) self.target_shape = self.distribution_output.event_shape if config.loss == "nll": self.loss = NegativeLogLikelihood() else: raise ValueError(f"Unknown loss function {config.loss}") # Initialize weights of distribution_output and apply final processing self.post_init() def output_params(self, dec_output): return self.parameter_projection(dec_output) def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() @torch.jit.ignore def output_distribution(self, params, scale=None, trailing_n=None) -> torch.distributions.Distribution: sliced_params = params if trailing_n is not None: sliced_params = [p[:, -trailing_n:] for p in params] return self.distribution_output.distribution(sliced_params, scale=scale) @add_start_docstrings_to_model_forward(TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqTimeSeriesModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, past_values: torch.Tensor, past_time_features: torch.Tensor, past_observed_mask: torch.Tensor, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, future_values: Optional[torch.Tensor] = None, future_time_features: Optional[torch.Tensor] = None, future_observed_mask: Optional[torch.Tensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, use_cache: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Seq2SeqTimeSeriesModelOutput, Tuple]: r""" Returns: future_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): Boolean mask to indicate which `future_values` were observed and which were missing. Mask values selected in `[0, 1]`: - 1 for values that are **observed**, - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). This mask is used to filter out missing values for the final loss calculation. Examples: ```python >>> from huggingface_hub import hf_hub_download >>> import torch >>> from transformers import TimeSeriesTransformerForPrediction >>> file = hf_hub_download( ... repo_id="kashif/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset" ... ) >>> batch = torch.load(file) >>> model = TimeSeriesTransformerForPrediction.from_pretrained( ... "huggingface/time-series-transformer-tourism-monthly" ... ) >>> # during training, one provides both past and future values >>> # as well as possible additional features >>> outputs = model( ... past_values=batch["past_values"], ... past_time_features=batch["past_time_features"], ... past_observed_mask=batch["past_observed_mask"], ... static_categorical_features=batch["static_categorical_features"], ... static_real_features=batch["static_real_features"], ... future_values=batch["future_values"], ... future_time_features=batch["future_time_features"], ... ) >>> loss = outputs.loss >>> loss.backward() >>> # during inference, one only provides past values >>> # as well as possible additional features >>> # the model autoregressively generates future values >>> outputs = model.generate( ... past_values=batch["past_values"], ... past_time_features=batch["past_time_features"], ... past_observed_mask=batch["past_observed_mask"], ... static_categorical_features=batch["static_categorical_features"], ... static_real_features=batch["static_real_features"], ... future_time_features=batch["future_time_features"], ... ) >>> mean_prediction = outputs.sequences.mean(dim=1) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if future_values is not None: use_cache = False outputs = self.model( past_values=past_values, past_time_features=past_time_features, past_observed_mask=past_observed_mask, static_categorical_features=static_categorical_features, static_real_features=static_real_features, future_values=future_values, future_time_features=future_time_features, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions, use_cache=use_cache, return_dict=return_dict, ) prediction_loss = None params = None if future_values is not None: params = self.output_params(outputs[0]) # outputs.last_hidden_state distribution = self.output_distribution(params, outputs[-2]) # outputs.scale loss = self.loss(distribution, future_values) if future_observed_mask is None: future_observed_mask = torch.ones_like(future_values) if len(self.target_shape) == 0: loss_weights = future_observed_mask else: loss_weights, _ = future_observed_mask.min(dim=-1, keepdim=False) prediction_loss = weighted_average(loss, weights=loss_weights) if not return_dict: outputs = ((params,) + outputs[1:]) if params is not None else outputs[1:] return ((prediction_loss,) + outputs) if prediction_loss is not None else outputs return Seq2SeqTimeSeriesPredictionOutput( loss=prediction_loss, params=params, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, scale=outputs.scale, static_features=outputs.static_features, ) @torch.no_grad() def generate( self, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, past_time_features: torch.Tensor, past_values: torch.Tensor, past_observed_mask: torch.Tensor, future_time_features: Optional[torch.Tensor], output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> torch.Tensor: outputs = self( static_categorical_features=static_categorical_features, static_real_features=static_real_features, past_time_features=past_time_features, past_values=past_values, past_observed_mask=past_observed_mask, future_time_features=future_time_features, future_values=None, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, use_cache=True, ) decoder = self.model.get_decoder() enc_last_hidden = outputs.encoder_last_hidden_state scale = outputs.scale static_feat = outputs.static_features num_parallel_samples = self.config.num_parallel_samples repeated_scale = scale.repeat_interleave(repeats=num_parallel_samples, dim=0) repeated_past_values = past_values.repeat_interleave(repeats=num_parallel_samples, dim=0) / repeated_scale expanded_static_feat = static_feat.unsqueeze(1).expand(-1, future_time_features.shape[1], -1) features = torch.cat((expanded_static_feat, future_time_features), dim=-1) repeated_features = features.repeat_interleave(repeats=num_parallel_samples, dim=0) repeated_enc_last_hidden = enc_last_hidden.repeat_interleave(repeats=num_parallel_samples, dim=0) future_samples = [] # greedy decoding for k in range(self.config.prediction_length): lagged_sequence = self.model.get_lagged_subsequences( sequence=repeated_past_values, subsequences_length=1 + k, shift=1, ) lags_shape = lagged_sequence.shape reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1) decoder_input = torch.cat((reshaped_lagged_sequence, repeated_features[:, : k + 1]), dim=-1) dec_output = decoder(inputs_embeds=decoder_input, encoder_hidden_states=repeated_enc_last_hidden) dec_last_hidden = dec_output.last_hidden_state params = self.parameter_projection(dec_last_hidden[:, -1:]) distr = self.output_distribution(params, scale=repeated_scale) next_sample = distr.sample() repeated_past_values = torch.cat((repeated_past_values, next_sample / repeated_scale), dim=1) future_samples.append(next_sample) concat_future_samples = torch.cat(future_samples, dim=1) return SampleTimeSeriesPredictionOutput( sequences=concat_future_samples.reshape( (-1, num_parallel_samples, self.config.prediction_length) + self.target_shape, ) )

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Time Series Transformer model.""" import random from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.distributions import ( AffineTransform, Distribution, Independent, NegativeBinomial, Normal, StudentT, TransformedDistribution, ) from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, ModelOutput from ...modeling_utils import PreTrainedModel from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_time_series_transformer import TimeSeriesTransformerConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "TimeSeriesTransformerConfig" TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "huggingface/time-series-transformer-tourism-monthly", # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer ] class AffineTransformed(TransformedDistribution): def __init__(self, base_distribution: Distribution, loc=None, scale=None, event_dim=0): self.scale = 1.0 if scale is None else scale self.loc = 0.0 if loc is None else loc super().__init__(base_distribution, [AffineTransform(loc=self.loc, scale=self.scale, event_dim=event_dim)]) @property def mean(self): """ Returns the mean of the distribution. """ return self.base_dist.mean * self.scale + self.loc @property def variance(self): """ Returns the variance of the distribution. """ return self.base_dist.variance * self.scale**2 @property def stddev(self): """ Returns the standard deviation of the distribution. """ return self.variance.sqrt() class ParameterProjection(nn.Module): def __init__( self, in_features: int, args_dim: Dict[str, int], domain_map: Callable[..., Tuple[torch.Tensor]], **kwargs ) -> None: super().__init__(**kwargs) self.args_dim = args_dim self.proj = nn.ModuleList([nn.Linear(in_features, dim) for dim in args_dim.values()]) self.domain_map = domain_map def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]: params_unbounded = [proj(x) for proj in self.proj] return self.domain_map(*params_unbounded) class LambdaLayer(nn.Module): def __init__(self, function): super().__init__() self.function = function def forward(self, x, *args): return self.function(x, *args) class DistributionOutput: distribution_class: type in_features: int args_dim: Dict[str, int] def __init__(self, dim: int = 1) -> None: self.dim = dim self.args_dim = {k: dim * self.args_dim[k] for k in self.args_dim} def _base_distribution(self, distr_args): if self.dim == 1: return self.distribution_class(*distr_args) else: return Independent(self.distribution_class(*distr_args), 1) def distribution( self, distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None, ) -> Distribution: distr = self._base_distribution(distr_args) if loc is None and scale is None: return distr else: return AffineTransformed(distr, loc=loc, scale=scale, event_dim=self.event_dim) @property def event_shape(self) -> Tuple: r""" Shape of each individual event contemplated by the distributions that this object constructs. """ return () if self.dim == 1 else (self.dim,) @property def event_dim(self) -> int: r""" Number of event dimensions, i.e., length of the `event_shape` tuple, of the distributions that this object constructs. """ return len(self.event_shape) @property def value_in_support(self) -> float: r""" A float that will have a valid numeric value when computing the log-loss of the corresponding distribution. By default 0.0. This value will be used when padding data series. """ return 0.0 def get_parameter_projection(self, in_features: int) -> nn.Module: r""" Return the parameter projection layer that maps the input to the appropriate parameters of the distribution. """ return ParameterProjection( in_features=in_features, args_dim=self.args_dim, domain_map=LambdaLayer(self.domain_map), ) def domain_map(self, *args: torch.Tensor): r""" Converts arguments to the right shape and domain. The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape. """ raise NotImplementedError() @classmethod def squareplus(cls, x: torch.Tensor) -> torch.Tensor: r""" Helper to map inputs to the positive orthant by applying the square-plus operation. Reference: https://twitter.com/jon_barron/status/1387167648669048833 """ return (x + torch.sqrt(torch.square(x) + 4.0)) / 2.0 class StudentTOutput(DistributionOutput): args_dim: Dict[str, int] = {"df": 1, "loc": 1, "scale": 1} distribution_class: type = StudentT @classmethod def domain_map(cls, df: torch.Tensor, loc: torch.Tensor, scale: torch.Tensor): scale = cls.squareplus(scale) df = 2.0 + cls.squareplus(df) return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1) class NormalOutput(DistributionOutput): args_dim: Dict[str, int] = {"loc": 1, "scale": 1} distribution_class: type = Normal @classmethod def domain_map(cls, loc: torch.Tensor, scale: torch.Tensor): scale = cls.squareplus(scale) return loc.squeeze(-1), scale.squeeze(-1) class NegativeBinomialOutput(DistributionOutput): args_dim: Dict[str, int] = {"total_count": 1, "logits": 1} distribution_class: type = NegativeBinomial @classmethod def domain_map(cls, total_count: torch.Tensor, logits: torch.Tensor): total_count = cls.squareplus(total_count) return total_count.squeeze(-1), logits.squeeze(-1) def _base_distribution(self, distr_args) -> Distribution: total_count, logits = distr_args if self.dim == 1: return self.distribution_class(total_count=total_count, logits=logits) else: return Independent(self.distribution_class(total_count=total_count, logits=logits), 1) # Overwrites the parent class method. We cannot scale using the affine # transformation since negative binomial should return integers. Instead # we scale the parameters. def distribution( self, distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None ) -> Distribution: total_count, logits = distr_args if scale is not None: # See scaling property of Gamma. logits += scale.log() return self._base_distribution((total_count, logits)) class FeatureEmbedder(nn.Module): def __init__(self, cardinalities: List[int], embedding_dims: List[int]) -> None: super().__init__() self.num_features = len(cardinalities) self.embedders = nn.ModuleList([nn.Embedding(c, d) for c, d in zip(cardinalities, embedding_dims)]) def forward(self, features: torch.Tensor) -> torch.Tensor: if self.num_features > 1: # we slice the last dimension, giving an array of length # self.num_features with shape (N,T) or (N) cat_feature_slices = torch.chunk(features, self.num_features, dim=-1) else: cat_feature_slices = [features] return torch.cat( [ embed(cat_feature_slice.squeeze(-1)) for embed, cat_feature_slice in zip(self.embedders, cat_feature_slices) ], dim=-1, ) class MeanScaler(nn.Module): """ Computes a scaling factor as the weighted average absolute value along dimension `dim`, and scales the data accordingly. Args: dim (`int`): Dimension along which to compute the scale. keepdim (`bool`, *optional*, defaults to `False`): Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it. minimum_scale (`float`, *optional*, defaults to 1e-10): Default scale that is used for elements that are constantly zero along dimension `dim`. """ def __init__(self, dim: int, keepdim: bool = False, minimum_scale: float = 1e-10): super().__init__() if not dim > 0: raise ValueError("Cannot compute scale along dim = 0 (batch dimension), please provide dim > 0") self.dim = dim self.keepdim = keepdim self.register_buffer("minimum_scale", torch.tensor(minimum_scale)) def forward(self, data: torch.Tensor, weights: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # these will have shape (N, C) total_weight = weights.sum(dim=self.dim) weighted_sum = (data.abs() * weights).sum(dim=self.dim) # first compute a global scale per-dimension total_observed = total_weight.sum(dim=0) denominator = torch.max(total_observed, torch.ones_like(total_observed)) default_scale = weighted_sum.sum(dim=0) / denominator # then compute a per-item, per-dimension scale denominator = torch.max(total_weight, torch.ones_like(total_weight)) scale = weighted_sum / denominator # use per-batch scale when no element is observed # or when the sequence contains only zeros scale = ( torch.max( self.minimum_scale, torch.where( weighted_sum > torch.zeros_like(weighted_sum), scale, default_scale * torch.ones_like(total_weight), ), ) .detach() .unsqueeze(dim=self.dim) ) return data / scale, scale if self.keepdim else scale.squeeze(dim=self.dim) class NOPScaler(nn.Module): """ Assigns a scaling factor equal to 1 along dimension `dim`, and therefore applies no scaling to the input data. Args: dim (`int`): Dimension along which to compute the scale. keepdim (`bool`, *optional*, defaults to `False`): Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it. """ def __init__(self, dim: int, keepdim: bool = False): super().__init__() self.dim = dim self.keepdim = keepdim def forward(self, data: torch.Tensor, observed_indicator: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: scale = torch.ones_like(data).mean(dim=self.dim, keepdim=self.keepdim) return data, scale def weighted_average(input_tensor: torch.Tensor, weights: Optional[torch.Tensor] = None, dim=None) -> torch.Tensor: """ Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero, meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`. Args: input_tensor (`torch.FloatTensor`): Input tensor, of which the average must be computed. weights (`torch.FloatTensor`, *optional*): Weights tensor, of the same shape as `input_tensor`. dim (`int`, *optional*): The dim along which to average `input_tensor`. Returns: `torch.FloatTensor`: The tensor with values averaged along the specified `dim`. """ if weights is not None: weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor)) sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0) return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights else: return input_tensor.mean(dim=dim) class NegativeLogLikelihood: """ Computes the negative log likelihood loss from input distribution with respect to target. """ def __call__(self, input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor: return -input.log_prob(target) # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min)) mask_cond = torch.arange(mask.size(-1)) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) @dataclass class Seq2SeqTimeSeriesModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. scale: (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): Scaling values of each time series' context window which is used to give the model inputs of the same magnitude and then used to rescale to the original scale. static_features: (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*): Static features of each time series' in a batch which are copied to the covariates at inference time. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None scale: Optional[torch.FloatTensor] = None static_features: Optional[torch.FloatTensor] = None @dataclass class Seq2SeqTimeSeriesPredictionOutput(ModelOutput): """ Base class for model's predictions outputs that also contain the loss as well parameters of the chosen distribution. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when a `future_values` is provided): Distributional loss. params (`torch.FloatTensor` of shape `(batch_size, num_samples, num_params)`): Parameters of the chosen distribution. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. scale: (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): Scaling values of each time series' context window which is used to give the model inputs of the same magnitude and then used to rescale to the original scale. static_features: (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*): Static features of each time series' in a batch which are copied to the covariates at inference time. """ loss: Optional[torch.FloatTensor] = None params: Optional[Tuple[torch.FloatTensor]] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None scale: Optional[torch.FloatTensor] = None static_features: Optional[torch.FloatTensor] = None @dataclass class SampleTimeSeriesPredictionOutput(ModelOutput): sequences: torch.FloatTensor = None # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->TimeSeriesTransformer class TimeSeriesTransformerAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value # Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->TimeSeriesTransformer class TimeSeriesTransformerEncoderLayer(nn.Module): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = TimeSeriesTransformerAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->TimeSeriesTransformer class TimeSeriesTransformerDecoderLayer(nn.Module): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = TimeSeriesTransformerAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = TimeSeriesTransformerAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs class TimeSeriesTransformerPreTrainedModel(PreTrainedModel): config_class = TimeSeriesTransformerConfig base_model_prefix = "model" main_input_name = "past_values" supports_gradient_checkpointing = True def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (TimeSeriesTransformerDecoder, TimeSeriesTransformerEncoder)): module.gradient_checkpointing = value TIME_SERIES_TRANSFORMER_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`TimeSeriesTransformerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING = r""" Args: past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Past values of the time series, that serve as context in order to predict the future. These values may contain lags, i.e. additional values from the past which are added in order to serve as "extra context". The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as `static_categorical_features`, `static_real_features`, `past_time_features`). The sequence length here is equal to `context_length` + `max(config.lags_sequence)`. Missing values need to be replaced with zeros. past_time_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_features)`, *optional*): Optional time features, which the model internally will add to `past_values`. These could be things like "month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These could also be so-called "age" features, which basically help the model know "at which point in life" a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`. past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in `[0, 1]`: - 1 for values that are **observed**, - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). static_categorical_features (`torch.LongTensor` of shape `(batch_size, number of static categorical features)`, *optional*): Optional static categorical features for which the model will learn an embedding, which it will add to the values of the time series. Static categorical features are features which have the same value for all time steps (static over time). A typical example of a static categorical feature is a time series ID. static_real_features (`torch.FloatTensor` of shape `(batch_size, number of static real features)`, *optional*): Optional static real features which the model will add to the values of the time series. Static real features are features which have the same value for all time steps (static over time). A typical example of a static real feature is promotion information. future_values (`torch.FloatTensor` of shape `(batch_size, prediction_length)`): Future values of the time series, that serve as labels for the model. The `future_values` is what the Transformer needs to learn to output, given the `past_values`. See the demo notebook and code snippets for details. Missing values need to be replaced with zeros. future_time_features (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_features)`, *optional*): Optional time features, which the model internally will add to `future_values`. These could be things like "month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These could also be so-called "age" features, which basically help the model know "at which point in life" a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional features. The Time Series Transformer only learns additional embeddings for `static_categorical_features`. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on certain token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of `last_hidden_state`, `hidden_states` (*optional*) and `attentions` (*optional*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` (*optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class TimeSeriesTransformerEncoder(TimeSeriesTransformerPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`TimeSeriesTransformerEncoderLayer`]. Args: config: TimeSeriesTransformerConfig """ def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.layers = nn.ModuleList([TimeSeriesTransformerEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = inputs_embeds hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class TimeSeriesTransformerDecoder(TimeSeriesTransformerPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TimeSeriesTransformerDecoderLayer`] Args: config: TimeSeriesTransformerConfig """ def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.layers = nn.ModuleList([TimeSeriesTransformerDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length ).to(inputs_embeds.device) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: r""" Args: attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_shape = inputs_embeds.size()[:-1] # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) hidden_states = inputs_embeds hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, output_attentions, use_cache) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare Time Series Transformer Model outputting raw hidden-states without any specific head on top.", TIME_SERIES_TRANSFORMER_START_DOCSTRING, ) class TimeSeriesTransformerModel(TimeSeriesTransformerPreTrainedModel): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) if config.scaling: self.scaler = MeanScaler(dim=1, keepdim=True) else: self.scaler = NOPScaler(dim=1, keepdim=True) self.embedder = FeatureEmbedder( cardinalities=config.cardinality, embedding_dims=config.embedding_dimension, ) # transformer encoder-decoder and mask initializer self.encoder = TimeSeriesTransformerEncoder(config) self.decoder = TimeSeriesTransformerDecoder(config) # Initialize weights and apply final processing self.post_init() @property def _past_length(self) -> int: return self.config.context_length + max(self.config.lags_sequence) def get_lagged_subsequences( self, sequence: torch.Tensor, subsequences_length: int, shift: int = 0 ) -> torch.Tensor: """ Returns lagged subsequences of a given sequence. Returns a tensor of shape (N, S, C, I), where S = subsequences_length and I = len(indices), containing lagged subsequences. Specifically, lagged[i, j, :, k] = sequence[i, -indices[k]-S+j, :]. Args: sequence: Tensor The sequence from which lagged subsequences should be extracted. Shape: (N, T, C). subsequences_length : int Length of the subsequences to be extracted. shift: int Shift the lags by this amount back. """ sequence_length = sequence.shape[1] indices = [lag - shift for lag in self.config.lags_sequence] try: assert max(indices) + subsequences_length <= sequence_length, ( f"lags cannot go further than history length, found lag {max(indices)} " f"while history length is only {sequence_length}" ) except AssertionError as e: e.args += (max(indices), sequence_length) raise lagged_values = [] for lag_index in indices: begin_index = -lag_index - subsequences_length end_index = -lag_index if lag_index > 0 else None lagged_values.append(sequence[:, begin_index:end_index, ...]) return torch.stack(lagged_values, dim=-1) def create_network_inputs( self, past_values: torch.Tensor, past_time_features: torch.Tensor, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, past_observed_mask: Optional[torch.Tensor] = None, future_values: Optional[torch.Tensor] = None, future_time_features: Optional[torch.Tensor] = None, ): # time feature time_feat = ( torch.cat( ( past_time_features[:, self._past_length - self.config.context_length :, ...], future_time_features, ), dim=1, ) if future_values is not None else past_time_features[:, self._past_length - self.config.context_length :, ...] ) # target if past_observed_mask is None: past_observed_mask = torch.ones_like(past_values) context = past_values[:, -self.config.context_length :] observed_context = past_observed_mask[:, -self.config.context_length :] _, scale = self.scaler(context, observed_context) inputs = ( torch.cat((past_values, future_values), dim=1) / scale if future_values is not None else past_values / scale ) inputs_length = ( self._past_length + self.config.prediction_length if future_values is not None else self._past_length ) try: assert inputs.shape[1] == inputs_length, ( f"input length {inputs.shape[1]} and dynamic feature lengths {inputs_length} does not match", ) except AssertionError as e: e.args += (inputs.shape[1], inputs_length) raise subsequences_length = ( self.config.context_length + self.config.prediction_length if future_values is not None else self.config.context_length ) # embeddings embedded_cat = self.embedder(static_categorical_features) # static features log_scale = scale.log() if self.config.input_size == 1 else scale.squeeze(1).log() static_feat = torch.cat((embedded_cat, static_real_features, log_scale), dim=1) expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1) # all features features = torch.cat((expanded_static_feat, time_feat), dim=-1) lagged_sequence = self.get_lagged_subsequences(sequence=inputs, subsequences_length=subsequences_length) lags_shape = lagged_sequence.shape reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1) transformer_inputs = torch.cat((reshaped_lagged_sequence, features), dim=-1) return transformer_inputs, scale, static_feat def enc_dec_outputs(self, transformer_inputs): enc_input = transformer_inputs[:, : self.config.context_length, ...] dec_input = transformer_inputs[:, self.config.context_length :, ...] encoder_outputs = self.encoder(inputs_embeds=enc_input) decoder_outputs = self.decoder( inputs_embeds=dec_input, encoder_hidden_states=encoder_outputs.last_hidden_state ) return encoder_outputs, decoder_outputs def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqTimeSeriesModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, past_values: torch.Tensor, past_time_features: torch.Tensor, past_observed_mask: torch.Tensor, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, future_values: Optional[torch.Tensor] = None, future_time_features: Optional[torch.Tensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, use_cache: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Seq2SeqTimeSeriesModelOutput, Tuple]: r""" Returns: Examples: ```python >>> from huggingface_hub import hf_hub_download >>> import torch >>> from transformers import TimeSeriesTransformerModel >>> file = hf_hub_download( ... repo_id="kashif/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset" ... ) >>> batch = torch.load(file) >>> model = TimeSeriesTransformerModel.from_pretrained("huggingface/time-series-transformer-tourism-monthly") >>> # during training, one provides both past and future values >>> # as well as possible additional features >>> outputs = model( ... past_values=batch["past_values"], ... past_time_features=batch["past_time_features"], ... past_observed_mask=batch["past_observed_mask"], ... static_categorical_features=batch["static_categorical_features"], ... static_real_features=batch["static_real_features"], ... future_values=batch["future_values"], ... future_time_features=batch["future_time_features"], ... ) >>> last_hidden_state = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_inputs, scale, static_feat = self.create_network_inputs( past_values=past_values, past_time_features=past_time_features, past_observed_mask=past_observed_mask, static_categorical_features=static_categorical_features, static_real_features=static_real_features, future_values=future_values, future_time_features=future_time_features, ) if encoder_outputs is None: enc_input = transformer_inputs[:, : self.config.context_length, ...] encoder_outputs = self.encoder( inputs_embeds=enc_input, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) dec_input = transformer_inputs[:, self.config.context_length :, ...] decoder_outputs = self.decoder( inputs_embeds=dec_input, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs + (scale, static_feat) return Seq2SeqTimeSeriesModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, scale=scale, static_features=static_feat, ) @add_start_docstrings( "The Time Series Transformer Model with a distribution head on top for time-series forecasting.", TIME_SERIES_TRANSFORMER_START_DOCSTRING, ) class TimeSeriesTransformerForPrediction(TimeSeriesTransformerPreTrainedModel): def __init__(self, config: TimeSeriesTransformerConfig): super().__init__(config) self.model = TimeSeriesTransformerModel(config) if config.distribution_output == "student_t": self.distribution_output = StudentTOutput(dim=config.input_size) elif config.distribution_output == "normal": self.distribution_output = NormalOutput(dim=config.input_size) elif config.distribution_output == "negative_binomial": self.distribution_output = NegativeBinomialOutput(dim=config.input_size) else: raise ValueError(f"Unknown distribution output {config.distribution_output}") self.parameter_projection = self.distribution_output.get_parameter_projection(self.model.config.d_model) self.target_shape = self.distribution_output.event_shape if config.loss == "nll": self.loss = NegativeLogLikelihood() else: raise ValueError(f"Unknown loss function {config.loss}") # Initialize weights of distribution_output and apply final processing self.post_init() def output_params(self, dec_output): return self.parameter_projection(dec_output) def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() @torch.jit.ignore def output_distribution(self, params, scale=None, trailing_n=None) -> torch.distributions.Distribution: sliced_params = params if trailing_n is not None: sliced_params = [p[:, -trailing_n:] for p in params] return self.distribution_output.distribution(sliced_params, scale=scale) @add_start_docstrings_to_model_forward(TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqTimeSeriesModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, past_values: torch.Tensor, past_time_features: torch.Tensor, past_observed_mask: torch.Tensor, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, future_values: Optional[torch.Tensor] = None, future_time_features: Optional[torch.Tensor] = None, future_observed_mask: Optional[torch.Tensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, use_cache: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Seq2SeqTimeSeriesModelOutput, Tuple]: r""" Returns: future_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): Boolean mask to indicate which `future_values` were observed and which were missing. Mask values selected in `[0, 1]`: - 1 for values that are **observed**, - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). This mask is used to filter out missing values for the final loss calculation. Examples: ```python >>> from huggingface_hub import hf_hub_download >>> import torch >>> from transformers import TimeSeriesTransformerForPrediction >>> file = hf_hub_download( ... repo_id="kashif/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset" ... ) >>> batch = torch.load(file) >>> model = TimeSeriesTransformerForPrediction.from_pretrained( ... "huggingface/time-series-transformer-tourism-monthly" ... ) >>> # during training, one provides both past and future values >>> # as well as possible additional features >>> outputs = model( ... past_values=batch["past_values"], ... past_time_features=batch["past_time_features"], ... past_observed_mask=batch["past_observed_mask"], ... static_categorical_features=batch["static_categorical_features"], ... static_real_features=batch["static_real_features"], ... future_values=batch["future_values"], ... future_time_features=batch["future_time_features"], ... ) >>> loss = outputs.loss >>> loss.backward() >>> # during inference, one only provides past values >>> # as well as possible additional features >>> # the model autoregressively generates future values >>> outputs = model.generate( ... past_values=batch["past_values"], ... past_time_features=batch["past_time_features"], ... past_observed_mask=batch["past_observed_mask"], ... static_categorical_features=batch["static_categorical_features"], ... static_real_features=batch["static_real_features"], ... future_time_features=batch["future_time_features"], ... ) >>> mean_prediction = outputs.sequences.mean(dim=1) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if future_values is not None: use_cache = False outputs = self.model( past_values=past_values, past_time_features=past_time_features, past_observed_mask=past_observed_mask, static_categorical_features=static_categorical_features, static_real_features=static_real_features, future_values=future_values, future_time_features=future_time_features, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions, use_cache=use_cache, return_dict=return_dict, ) prediction_loss = None params = None if future_values is not None: params = self.output_params(outputs[0]) # outputs.last_hidden_state distribution = self.output_distribution(params, outputs[-2]) # outputs.scale loss = self.loss(distribution, future_values) if future_observed_mask is None: future_observed_mask = torch.ones_like(future_values) if len(self.target_shape) == 0: loss_weights = future_observed_mask else: loss_weights, _ = future_observed_mask.min(dim=-1, keepdim=False) prediction_loss = weighted_average(loss, weights=loss_weights) if not return_dict: outputs = ((params,) + outputs[1:]) if params is not None else outputs[1:] return ((prediction_loss,) + outputs) if prediction_loss is not None else outputs return Seq2SeqTimeSeriesPredictionOutput( loss=prediction_loss, params=params, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, scale=outputs.scale, static_features=outputs.static_features, ) @torch.no_grad() def generate( self, static_categorical_features: torch.Tensor, static_real_features: torch.Tensor, past_time_features: torch.Tensor, past_values: torch.Tensor, past_observed_mask: torch.Tensor, future_time_features: Optional[torch.Tensor], output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> torch.Tensor: outputs = self( static_categorical_features=static_categorical_features, static_real_features=static_real_features, past_time_features=past_time_features, past_values=past_values, past_observed_mask=past_observed_mask, future_time_features=future_time_features, future_values=None, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, use_cache=True, ) decoder = self.model.get_decoder() enc_last_hidden = outputs.encoder_last_hidden_state scale = outputs.scale static_feat = outputs.static_features num_parallel_samples = self.config.num_parallel_samples repeated_scale = scale.repeat_interleave(repeats=num_parallel_samples, dim=0) repeated_past_values = past_values.repeat_interleave(repeats=num_parallel_samples, dim=0) / repeated_scale expanded_static_feat = static_feat.unsqueeze(1).expand(-1, future_time_features.shape[1], -1) features = torch.cat((expanded_static_feat, future_time_features), dim=-1) repeated_features = features.repeat_interleave(repeats=num_parallel_samples, dim=0) repeated_enc_last_hidden = enc_last_hidden.repeat_interleave(repeats=num_parallel_samples, dim=0) future_samples = [] # greedy decoding for k in range(self.config.prediction_length): lagged_sequence = self.model.get_lagged_subsequences( sequence=repeated_past_values, subsequences_length=1 + k, shift=1, ) lags_shape = lagged_sequence.shape reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1) decoder_input = torch.cat((reshaped_lagged_sequence, repeated_features[:, : k + 1]), dim=-1) dec_output = decoder(inputs_embeds=decoder_input, encoder_hidden_states=repeated_enc_last_hidden) dec_last_hidden = dec_output.last_hidden_state params = self.parameter_projection(dec_last_hidden[:, -1:]) distr = self.output_distribution(params, scale=repeated_scale) next_sample = distr.sample() repeated_past_values = torch.cat((repeated_past_values, next_sample / repeated_scale), dim=1) future_samples.append(next_sample) concat_future_samples = torch.cat(future_samples, dim=1) return SampleTimeSeriesPredictionOutput( sequences=concat_future_samples.reshape( (-1, num_parallel_samples, self.config.prediction_length) + self.target_shape, ) )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/fsmt/test_modeling_fsmt.py

# coding=utf-8 # Copyright 2020 Huggingface # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tempfile import unittest import timeout_decorator # noqa from parameterized import parameterized from transformers import FSMTConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer from transformers.models.fsmt.modeling_fsmt import ( SinusoidalPositionalEmbedding, _prepare_fsmt_decoder_inputs, invert_mask, shift_tokens_right, ) from transformers.pipelines import TranslationPipeline class FSMTModelTester: def __init__( self, parent, src_vocab_size=99, tgt_vocab_size=99, langs=["ru", "en"], batch_size=13, seq_length=7, is_training=False, use_labels=False, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, bos_token_id=0, pad_token_id=1, eos_token_id=2, ): self.parent = parent self.src_vocab_size = src_vocab_size self.tgt_vocab_size = tgt_vocab_size self.langs = langs self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id torch.manual_seed(0) # hack needed for modeling_common tests - despite not really having this attribute in this model self.vocab_size = self.src_vocab_size def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.src_vocab_size).clamp( 3, ) input_ids[:, -1] = 2 # Eos Token config = self.get_config() inputs_dict = prepare_fsmt_inputs_dict(config, input_ids) return config, inputs_dict def get_config(self): return FSMTConfig( vocab_size=self.src_vocab_size, # hack needed for common tests src_vocab_size=self.src_vocab_size, tgt_vocab_size=self.tgt_vocab_size, langs=self.langs, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"] inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"] inputs_dict["use_cache"] = False return config, inputs_dict def prepare_fsmt_inputs_dict( config, input_ids, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, } @require_torch class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False def setUp(self): self.model_tester = FSMTModelTester(self) self.langs = ["en", "ru"] config = { "langs": self.langs, "src_vocab_size": 10, "tgt_vocab_size": 20, } # XXX: hack to appease to all other models requiring `vocab_size` config["vocab_size"] = 99 # no such thing in FSMT self.config_tester = ConfigTester(self, config_class=FSMTConfig, **config) def test_config(self): self.config_tester.run_common_tests() # XXX: override test_model_common_attributes / different Embedding type def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding)) model.set_input_embeddings(nn.Embedding(10, 10)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.modules.sparse.Embedding)) def test_initialization_more(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = FSMTModel(config) model.to(torch_device) model.eval() # test init # self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item()) def _check_var(module): """Check that we initialized various parameters from N(0, config.init_std).""" self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2) _check_var(model.encoder.embed_tokens) _check_var(model.encoder.layers[0].self_attn.k_proj) _check_var(model.encoder.layers[0].fc1) # XXX: different std for fairseq version of SinusoidalPositionalEmbedding # self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2) def test_advanced_inputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() config.use_cache = False inputs_dict["input_ids"][:, -2:] = config.pad_token_id decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs( config, inputs_dict["input_ids"] ) model = FSMTModel(config).to(torch_device).eval() decoder_features_with_created_mask = model(**inputs_dict)[0] decoder_features_with_passed_mask = model( decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict )[0] _assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask) useless_mask = torch.zeros_like(decoder_attn_mask) decoder_features = model(decoder_attention_mask=useless_mask, **inputs_dict)[0] self.assertTrue(isinstance(decoder_features, torch.Tensor)) # no hidden states or attentions self.assertEqual( decoder_features.size(), (self.model_tester.batch_size, self.model_tester.seq_length, config.tgt_vocab_size), ) if decoder_attn_mask.min().item() < -1e3: # some tokens were masked self.assertFalse((decoder_features_with_created_mask == decoder_features).all().item()) # Test different encoder attention masks decoder_features_with_long_encoder_mask = model( inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"].long() )[0] _assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask) def test_save_load_missing_keys(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) @unittest.skip("Test has a segmentation fault on torch 1.8.0") def test_export_to_onnx(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = FSMTModel(config).to(torch_device) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( model, (inputs_dict["input_ids"], inputs_dict["attention_mask"]), f"{tmpdirname}/fsmt_test.onnx", export_params=True, opset_version=12, input_names=["input_ids", "attention_mask"], ) @unittest.skip("can't be implemented for FSMT due to dual vocab.") def test_resize_tokens_embeddings(self): pass @unittest.skip("Passing inputs_embeds not implemented for FSMT.") def test_inputs_embeds(self): pass @unittest.skip("model weights aren't tied in FSMT.") def test_tie_model_weights(self): pass @unittest.skip("TODO: Decoder embeddings cannot be resized at the moment") def test_resize_embeddings_untied(self): pass @require_torch class FSMTHeadTests(unittest.TestCase): src_vocab_size = 99 tgt_vocab_size = 99 langs = ["ru", "en"] def _get_config(self): return FSMTConfig( src_vocab_size=self.src_vocab_size, tgt_vocab_size=self.tgt_vocab_size, langs=self.langs, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) def _get_config_and_data(self): input_ids = torch.tensor( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ], dtype=torch.long, device=torch_device, ) batch_size = input_ids.shape[0] config = self._get_config() return config, input_ids, batch_size def test_generate_beam_search(self): input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], dtype=torch.long, device=torch_device) config = self._get_config() lm_model = FSMTForConditionalGeneration(config).to(torch_device) lm_model.eval() max_length = 5 new_input_ids = lm_model.generate( input_ids.clone(), do_sample=True, num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=max_length, ) self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length)) def test_shift_tokens_right(self): input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long) shifted = shift_tokens_right(input_ids, 1) n_pad_before = input_ids.eq(1).float().sum() n_pad_after = shifted.eq(1).float().sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(torch.eq(shifted[:, 0], 2).all()) def test_generate_fp16(self): config, input_ids, batch_size = self._get_config_and_data() attention_mask = input_ids.ne(1).to(torch_device) model = FSMTForConditionalGeneration(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_dummy_inputs(self): config, *_ = self._get_config_and_data() model = FSMTForConditionalGeneration(config).eval().to(torch_device) model(**model.dummy_inputs) def test_prepare_fsmt_decoder_inputs(self): config, *_ = self._get_config_and_data() input_ids = _long_tensor(([4, 4, 2])) decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]]) causal_mask_dtype = torch.float32 ignore = torch.finfo(causal_mask_dtype).min decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs( config, input_ids, decoder_input_ids, causal_mask_dtype=causal_mask_dtype ) expected_causal_mask = torch.tensor( [[0, ignore, ignore], [0, 0, ignore], [0, 0, 0]] # never attend to the final token, because its pad ).to(input_ids.device) self.assertEqual(decoder_attn_mask.size(), decoder_input_ids.size()) self.assertTrue(torch.eq(expected_causal_mask, causal_mask).all()) def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") def _long_tensor(tok_lst): return torch.tensor(tok_lst, dtype=torch.long, device=torch_device) TOLERANCE = 1e-4 pairs = [ ["en-ru"], ["ru-en"], ["en-de"], ["de-en"], ] @require_torch @require_sentencepiece @require_tokenizers class FSMTModelIntegrationTests(unittest.TestCase): tokenizers_cache = {} models_cache = {} default_mname = "facebook/wmt19-en-ru" @cached_property def default_tokenizer(self): return self.get_tokenizer(self.default_mname) @cached_property def default_model(self): return self.get_model(self.default_mname) def get_tokenizer(self, mname): if mname not in self.tokenizers_cache: self.tokenizers_cache[mname] = FSMTTokenizer.from_pretrained(mname) return self.tokenizers_cache[mname] def get_model(self, mname): if mname not in self.models_cache: self.models_cache[mname] = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device) if torch_device == "cuda": self.models_cache[mname].half() return self.models_cache[mname] @slow def test_inference_no_head(self): tokenizer = self.default_tokenizer model = FSMTModel.from_pretrained(self.default_mname).to(torch_device) src_text = "My friend computer will translate this for me" input_ids = tokenizer([src_text], return_tensors="pt")["input_ids"] input_ids = _long_tensor(input_ids).to(torch_device) inputs_dict = prepare_fsmt_inputs_dict(model.config, input_ids) with torch.no_grad(): output = model(**inputs_dict)[0] expected_shape = torch.Size((1, 10, model.config.tgt_vocab_size)) self.assertEqual(output.shape, expected_shape) # expected numbers were generated when en-ru model, using just fairseq's model4.pt # may have to adjust if switched to a different checkpoint expected_slice = torch.tensor( [[-1.5753, -1.5753, 2.8975], [-0.9540, -0.9540, 1.0299], [-3.3131, -3.3131, 0.5219]] ).to(torch_device) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) def translation_setup(self, pair): text = { "en": "Machine learning is great, isn't it?", "ru": "Машинное обучение - это здорово, не так ли?", "de": "Maschinelles Lernen ist großartig, oder?", } src, tgt = pair.split("-") print(f"Testing {src} -> {tgt}") mname = f"facebook/wmt19-{pair}" src_text = text[src] tgt_text = text[tgt] tokenizer = self.get_tokenizer(mname) model = self.get_model(mname) return tokenizer, model, src_text, tgt_text @parameterized.expand(pairs) @slow def test_translation_direct(self, pair): tokenizer, model, src_text, tgt_text = self.translation_setup(pair) input_ids = tokenizer.encode(src_text, return_tensors="pt").to(torch_device) outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) assert decoded == tgt_text, f"\n\ngot: {decoded}\nexp: {tgt_text}\n" @parameterized.expand(pairs) @slow def test_translation_pipeline(self, pair): tokenizer, model, src_text, tgt_text = self.translation_setup(pair) device = 0 if torch_device == "cuda" else -1 pipeline = TranslationPipeline(model, tokenizer, framework="pt", device=device) output = pipeline([src_text]) self.assertEqual([tgt_text], [x["translation_text"] for x in output]) @require_torch class TestSinusoidalPositionalEmbeddings(unittest.TestCase): padding_idx = 1 tolerance = 1e-4 def test_basic(self): input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device) emb1 = SinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6, padding_idx=self.padding_idx).to( torch_device ) emb = emb1(input_ids) desired_weights = torch.tensor( [ [9.0930e-01, 1.9999e-02, 2.0000e-04, -4.1615e-01, 9.9980e-01, 1.0000e00], [1.4112e-01, 2.9995e-02, 3.0000e-04, -9.8999e-01, 9.9955e-01, 1.0000e00], ] ).to(torch_device) self.assertTrue( torch.allclose(emb[0], desired_weights, atol=self.tolerance), msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n", ) def test_odd_embed_dim(self): # odd embedding_dim is allowed SinusoidalPositionalEmbedding(num_positions=4, embedding_dim=5, padding_idx=self.padding_idx).to(torch_device) # odd num_embeddings is allowed SinusoidalPositionalEmbedding(num_positions=5, embedding_dim=4, padding_idx=self.padding_idx).to(torch_device) @unittest.skip("different from marian (needs more research)") def test_positional_emb_weights_against_marian(self): desired_weights = torch.tensor( [ [0, 0, 0, 0, 0], [0.84147096, 0.82177866, 0.80180490, 0.78165019, 0.76140374], [0.90929741, 0.93651021, 0.95829457, 0.97505713, 0.98720258], ] ) emb1 = SinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512, padding_idx=self.padding_idx).to( torch_device ) weights = emb1.weights.data[:3, :5] # XXX: only the 1st and 3rd lines match - this is testing against # verbatim copy of SinusoidalPositionalEmbedding from fairseq self.assertTrue( torch.allclose(weights, desired_weights, atol=self.tolerance), msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n", ) # test that forward pass is just a lookup, there is no ignore padding logic input_ids = torch.tensor( [[4, 10, self.padding_idx, self.padding_idx, self.padding_idx]], dtype=torch.long, device=torch_device ) no_cache_pad_zero = emb1(input_ids)[0] # XXX: only the 1st line matches the 3rd self.assertTrue( torch.allclose(torch.tensor(desired_weights, device=torch_device), no_cache_pad_zero[:3, :5], atol=1e-3) )

# coding=utf-8 # Copyright 2020 Huggingface # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tempfile import unittest import timeout_decorator # noqa from parameterized import parameterized from transformers import FSMTConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer from transformers.models.fsmt.modeling_fsmt import ( SinusoidalPositionalEmbedding, _prepare_fsmt_decoder_inputs, invert_mask, shift_tokens_right, ) from transformers.pipelines import TranslationPipeline class FSMTModelTester: def __init__( self, parent, src_vocab_size=99, tgt_vocab_size=99, langs=["ru", "en"], batch_size=13, seq_length=7, is_training=False, use_labels=False, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, bos_token_id=0, pad_token_id=1, eos_token_id=2, ): self.parent = parent self.src_vocab_size = src_vocab_size self.tgt_vocab_size = tgt_vocab_size self.langs = langs self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id torch.manual_seed(0) # hack needed for modeling_common tests - despite not really having this attribute in this model self.vocab_size = self.src_vocab_size def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.src_vocab_size).clamp( 3, ) input_ids[:, -1] = 2 # Eos Token config = self.get_config() inputs_dict = prepare_fsmt_inputs_dict(config, input_ids) return config, inputs_dict def get_config(self): return FSMTConfig( vocab_size=self.src_vocab_size, # hack needed for common tests src_vocab_size=self.src_vocab_size, tgt_vocab_size=self.tgt_vocab_size, langs=self.langs, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"] inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"] inputs_dict["use_cache"] = False return config, inputs_dict def prepare_fsmt_inputs_dict( config, input_ids, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, } @require_torch class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False def setUp(self): self.model_tester = FSMTModelTester(self) self.langs = ["en", "ru"] config = { "langs": self.langs, "src_vocab_size": 10, "tgt_vocab_size": 20, } # XXX: hack to appease to all other models requiring `vocab_size` config["vocab_size"] = 99 # no such thing in FSMT self.config_tester = ConfigTester(self, config_class=FSMTConfig, **config) def test_config(self): self.config_tester.run_common_tests() # XXX: override test_model_common_attributes / different Embedding type def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding)) model.set_input_embeddings(nn.Embedding(10, 10)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.modules.sparse.Embedding)) def test_initialization_more(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = FSMTModel(config) model.to(torch_device) model.eval() # test init # self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item()) def _check_var(module): """Check that we initialized various parameters from N(0, config.init_std).""" self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2) _check_var(model.encoder.embed_tokens) _check_var(model.encoder.layers[0].self_attn.k_proj) _check_var(model.encoder.layers[0].fc1) # XXX: different std for fairseq version of SinusoidalPositionalEmbedding # self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2) def test_advanced_inputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() config.use_cache = False inputs_dict["input_ids"][:, -2:] = config.pad_token_id decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs( config, inputs_dict["input_ids"] ) model = FSMTModel(config).to(torch_device).eval() decoder_features_with_created_mask = model(**inputs_dict)[0] decoder_features_with_passed_mask = model( decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict )[0] _assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask) useless_mask = torch.zeros_like(decoder_attn_mask) decoder_features = model(decoder_attention_mask=useless_mask, **inputs_dict)[0] self.assertTrue(isinstance(decoder_features, torch.Tensor)) # no hidden states or attentions self.assertEqual( decoder_features.size(), (self.model_tester.batch_size, self.model_tester.seq_length, config.tgt_vocab_size), ) if decoder_attn_mask.min().item() < -1e3: # some tokens were masked self.assertFalse((decoder_features_with_created_mask == decoder_features).all().item()) # Test different encoder attention masks decoder_features_with_long_encoder_mask = model( inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"].long() )[0] _assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask) def test_save_load_missing_keys(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) @unittest.skip("Test has a segmentation fault on torch 1.8.0") def test_export_to_onnx(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = FSMTModel(config).to(torch_device) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( model, (inputs_dict["input_ids"], inputs_dict["attention_mask"]), f"{tmpdirname}/fsmt_test.onnx", export_params=True, opset_version=12, input_names=["input_ids", "attention_mask"], ) @unittest.skip("can't be implemented for FSMT due to dual vocab.") def test_resize_tokens_embeddings(self): pass @unittest.skip("Passing inputs_embeds not implemented for FSMT.") def test_inputs_embeds(self): pass @unittest.skip("model weights aren't tied in FSMT.") def test_tie_model_weights(self): pass @unittest.skip("TODO: Decoder embeddings cannot be resized at the moment") def test_resize_embeddings_untied(self): pass @require_torch class FSMTHeadTests(unittest.TestCase): src_vocab_size = 99 tgt_vocab_size = 99 langs = ["ru", "en"] def _get_config(self): return FSMTConfig( src_vocab_size=self.src_vocab_size, tgt_vocab_size=self.tgt_vocab_size, langs=self.langs, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) def _get_config_and_data(self): input_ids = torch.tensor( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ], dtype=torch.long, device=torch_device, ) batch_size = input_ids.shape[0] config = self._get_config() return config, input_ids, batch_size def test_generate_beam_search(self): input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], dtype=torch.long, device=torch_device) config = self._get_config() lm_model = FSMTForConditionalGeneration(config).to(torch_device) lm_model.eval() max_length = 5 new_input_ids = lm_model.generate( input_ids.clone(), do_sample=True, num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=max_length, ) self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length)) def test_shift_tokens_right(self): input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long) shifted = shift_tokens_right(input_ids, 1) n_pad_before = input_ids.eq(1).float().sum() n_pad_after = shifted.eq(1).float().sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(torch.eq(shifted[:, 0], 2).all()) def test_generate_fp16(self): config, input_ids, batch_size = self._get_config_and_data() attention_mask = input_ids.ne(1).to(torch_device) model = FSMTForConditionalGeneration(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_dummy_inputs(self): config, *_ = self._get_config_and_data() model = FSMTForConditionalGeneration(config).eval().to(torch_device) model(**model.dummy_inputs) def test_prepare_fsmt_decoder_inputs(self): config, *_ = self._get_config_and_data() input_ids = _long_tensor(([4, 4, 2])) decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]]) causal_mask_dtype = torch.float32 ignore = torch.finfo(causal_mask_dtype).min decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs( config, input_ids, decoder_input_ids, causal_mask_dtype=causal_mask_dtype ) expected_causal_mask = torch.tensor( [[0, ignore, ignore], [0, 0, ignore], [0, 0, 0]] # never attend to the final token, because its pad ).to(input_ids.device) self.assertEqual(decoder_attn_mask.size(), decoder_input_ids.size()) self.assertTrue(torch.eq(expected_causal_mask, causal_mask).all()) def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") def _long_tensor(tok_lst): return torch.tensor(tok_lst, dtype=torch.long, device=torch_device) TOLERANCE = 1e-4 pairs = [ ["en-ru"], ["ru-en"], ["en-de"], ["de-en"], ] @require_torch @require_sentencepiece @require_tokenizers class FSMTModelIntegrationTests(unittest.TestCase): tokenizers_cache = {} models_cache = {} default_mname = "facebook/wmt19-en-ru" @cached_property def default_tokenizer(self): return self.get_tokenizer(self.default_mname) @cached_property def default_model(self): return self.get_model(self.default_mname) def get_tokenizer(self, mname): if mname not in self.tokenizers_cache: self.tokenizers_cache[mname] = FSMTTokenizer.from_pretrained(mname) return self.tokenizers_cache[mname] def get_model(self, mname): if mname not in self.models_cache: self.models_cache[mname] = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device) if torch_device == "cuda": self.models_cache[mname].half() return self.models_cache[mname] @slow def test_inference_no_head(self): tokenizer = self.default_tokenizer model = FSMTModel.from_pretrained(self.default_mname).to(torch_device) src_text = "My friend computer will translate this for me" input_ids = tokenizer([src_text], return_tensors="pt")["input_ids"] input_ids = _long_tensor(input_ids).to(torch_device) inputs_dict = prepare_fsmt_inputs_dict(model.config, input_ids) with torch.no_grad(): output = model(**inputs_dict)[0] expected_shape = torch.Size((1, 10, model.config.tgt_vocab_size)) self.assertEqual(output.shape, expected_shape) # expected numbers were generated when en-ru model, using just fairseq's model4.pt # may have to adjust if switched to a different checkpoint expected_slice = torch.tensor( [[-1.5753, -1.5753, 2.8975], [-0.9540, -0.9540, 1.0299], [-3.3131, -3.3131, 0.5219]] ).to(torch_device) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) def translation_setup(self, pair): text = { "en": "Machine learning is great, isn't it?", "ru": "Машинное обучение - это здорово, не так ли?", "de": "Maschinelles Lernen ist großartig, oder?", } src, tgt = pair.split("-") print(f"Testing {src} -> {tgt}") mname = f"facebook/wmt19-{pair}" src_text = text[src] tgt_text = text[tgt] tokenizer = self.get_tokenizer(mname) model = self.get_model(mname) return tokenizer, model, src_text, tgt_text @parameterized.expand(pairs) @slow def test_translation_direct(self, pair): tokenizer, model, src_text, tgt_text = self.translation_setup(pair) input_ids = tokenizer.encode(src_text, return_tensors="pt").to(torch_device) outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) assert decoded == tgt_text, f"\n\ngot: {decoded}\nexp: {tgt_text}\n" @parameterized.expand(pairs) @slow def test_translation_pipeline(self, pair): tokenizer, model, src_text, tgt_text = self.translation_setup(pair) device = 0 if torch_device == "cuda" else -1 pipeline = TranslationPipeline(model, tokenizer, framework="pt", device=device) output = pipeline([src_text]) self.assertEqual([tgt_text], [x["translation_text"] for x in output]) @require_torch class TestSinusoidalPositionalEmbeddings(unittest.TestCase): padding_idx = 1 tolerance = 1e-4 def test_basic(self): input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device) emb1 = SinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6, padding_idx=self.padding_idx).to( torch_device ) emb = emb1(input_ids) desired_weights = torch.tensor( [ [9.0930e-01, 1.9999e-02, 2.0000e-04, -4.1615e-01, 9.9980e-01, 1.0000e00], [1.4112e-01, 2.9995e-02, 3.0000e-04, -9.8999e-01, 9.9955e-01, 1.0000e00], ] ).to(torch_device) self.assertTrue( torch.allclose(emb[0], desired_weights, atol=self.tolerance), msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n", ) def test_odd_embed_dim(self): # odd embedding_dim is allowed SinusoidalPositionalEmbedding(num_positions=4, embedding_dim=5, padding_idx=self.padding_idx).to(torch_device) # odd num_embeddings is allowed SinusoidalPositionalEmbedding(num_positions=5, embedding_dim=4, padding_idx=self.padding_idx).to(torch_device) @unittest.skip("different from marian (needs more research)") def test_positional_emb_weights_against_marian(self): desired_weights = torch.tensor( [ [0, 0, 0, 0, 0], [0.84147096, 0.82177866, 0.80180490, 0.78165019, 0.76140374], [0.90929741, 0.93651021, 0.95829457, 0.97505713, 0.98720258], ] ) emb1 = SinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512, padding_idx=self.padding_idx).to( torch_device ) weights = emb1.weights.data[:3, :5] # XXX: only the 1st and 3rd lines match - this is testing against # verbatim copy of SinusoidalPositionalEmbedding from fairseq self.assertTrue( torch.allclose(weights, desired_weights, atol=self.tolerance), msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n", ) # test that forward pass is just a lookup, there is no ignore padding logic input_ids = torch.tensor( [[4, 10, self.padding_idx, self.padding_idx, self.padding_idx]], dtype=torch.long, device=torch_device ) no_cache_pad_zero = emb1(input_ids)[0] # XXX: only the 1st line matches the 3rd self.assertTrue( torch.allclose(torch.tensor(desired_weights, device=torch_device), no_cache_pad_zero[:3, :5], atol=1e-3) )

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/models/regnet/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_regnet"] = [ "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "RegNetForImageClassification", "RegNetModel", "RegNetPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_regnet"] = [ "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRegNetForImageClassification", "TFRegNetModel", "TFRegNetPreTrainedModel", ] if TYPE_CHECKING: from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_regnet import ( REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, RegNetForImageClassification, RegNetModel, RegNetPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_regnet import ( TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel, TFRegNetPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_regnet"] = [ "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "RegNetForImageClassification", "RegNetModel", "RegNetPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_regnet"] = [ "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRegNetForImageClassification", "TFRegNetModel", "TFRegNetPreTrainedModel", ] if TYPE_CHECKING: from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_regnet import ( REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, RegNetForImageClassification, RegNetModel, RegNetPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_regnet import ( TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel, TFRegNetPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./docs/source/en/model_doc/mobilenet_v2.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # MobileNet V2 ## Overview The MobileNet model was proposed in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. The abstract from the paper is the following: *In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3.* *The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters.* Tips: - The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_1.0\_224**, where **1.0** is the depth multiplier (sometimes also referred to as "alpha" or the width multiplier) and **224** is the resolution of the input images the model was trained on. - Even though the checkpoint is trained on images of specific size, the model will work on images of any size. The smallest supported image size is 32x32. - One can use [`MobileNetV2FeatureExtractor`] to prepare images for the model. - The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). However, the model predicts 1001 classes: the 1000 classes from ImageNet plus an extra “background” class (index 0). - The segmentation model uses a [DeepLabV3+](https://arxiv.org/abs/1802.02611) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/). - The original TensorFlow checkpoints use different padding rules than PyTorch, requiring the model to determine the padding amount at inference time, since this depends on the input image size. To use native PyTorch padding behavior, create a [`MobileNetV2Config`] with `tf_padding = False`. Unsupported features: - The [`MobileNetV2Model`] outputs a globally pooled version of the last hidden state. In the original model it is possible to use an average pooling layer with a fixed 7x7 window and stride 1 instead of global pooling. For inputs that are larger than the recommended image size, this gives a pooled output that is larger than 1x1. The Hugging Face implementation does not support this. - The original TensorFlow checkpoints include quantized models. We do not support these models as they include additional "FakeQuantization" operations to unquantize the weights. - It's common to extract the output from the expansion layers at indices 10 and 13, as well as the output from the final 1x1 convolution layer, for downstream purposes. Using `output_hidden_states=True` returns the output from all intermediate layers. There is currently no way to limit this to specific layers. - The DeepLabV3+ segmentation head does not use the final convolution layer from the backbone, but this layer gets computed anyway. There is currently no way to tell [`MobileNetV2Model`] up to which layer it should run. This model was contributed by [matthijs](https://huggingface.co/Matthijs). The original code and weights can be found [here for the main model](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet) and [here for DeepLabV3+](https://github.com/tensorflow/models/tree/master/research/deeplab). ## MobileNetV2Config [[autodoc]] MobileNetV2Config ## MobileNetV2FeatureExtractor [[autodoc]] MobileNetV2FeatureExtractor - preprocess - post_process_semantic_segmentation ## MobileNetV2ImageProcessor [[autodoc]] MobileNetV2ImageProcessor - preprocess - post_process_semantic_segmentation ## MobileNetV2Model [[autodoc]] MobileNetV2Model - forward ## MobileNetV2ForImageClassification [[autodoc]] MobileNetV2ForImageClassification - forward ## MobileNetV2ForSemanticSegmentation [[autodoc]] MobileNetV2ForSemanticSegmentation - forward

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # MobileNet V2 ## Overview The MobileNet model was proposed in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. The abstract from the paper is the following: *In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3.* *The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters.* Tips: - The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_1.0\_224**, where **1.0** is the depth multiplier (sometimes also referred to as "alpha" or the width multiplier) and **224** is the resolution of the input images the model was trained on. - Even though the checkpoint is trained on images of specific size, the model will work on images of any size. The smallest supported image size is 32x32. - One can use [`MobileNetV2FeatureExtractor`] to prepare images for the model. - The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes). However, the model predicts 1001 classes: the 1000 classes from ImageNet plus an extra “background” class (index 0). - The segmentation model uses a [DeepLabV3+](https://arxiv.org/abs/1802.02611) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/). - The original TensorFlow checkpoints use different padding rules than PyTorch, requiring the model to determine the padding amount at inference time, since this depends on the input image size. To use native PyTorch padding behavior, create a [`MobileNetV2Config`] with `tf_padding = False`. Unsupported features: - The [`MobileNetV2Model`] outputs a globally pooled version of the last hidden state. In the original model it is possible to use an average pooling layer with a fixed 7x7 window and stride 1 instead of global pooling. For inputs that are larger than the recommended image size, this gives a pooled output that is larger than 1x1. The Hugging Face implementation does not support this. - The original TensorFlow checkpoints include quantized models. We do not support these models as they include additional "FakeQuantization" operations to unquantize the weights. - It's common to extract the output from the expansion layers at indices 10 and 13, as well as the output from the final 1x1 convolution layer, for downstream purposes. Using `output_hidden_states=True` returns the output from all intermediate layers. There is currently no way to limit this to specific layers. - The DeepLabV3+ segmentation head does not use the final convolution layer from the backbone, but this layer gets computed anyway. There is currently no way to tell [`MobileNetV2Model`] up to which layer it should run. This model was contributed by [matthijs](https://huggingface.co/Matthijs). The original code and weights can be found [here for the main model](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet) and [here for DeepLabV3+](https://github.com/tensorflow/models/tree/master/research/deeplab). ## MobileNetV2Config [[autodoc]] MobileNetV2Config ## MobileNetV2FeatureExtractor [[autodoc]] MobileNetV2FeatureExtractor - preprocess - post_process_semantic_segmentation ## MobileNetV2ImageProcessor [[autodoc]] MobileNetV2ImageProcessor - preprocess - post_process_semantic_segmentation ## MobileNetV2Model [[autodoc]] MobileNetV2Model - forward ## MobileNetV2ForImageClassification [[autodoc]] MobileNetV2ForImageClassification - forward ## MobileNetV2ForSemanticSegmentation [[autodoc]] MobileNetV2ForSemanticSegmentation - forward

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/blenderbot_small/test_modeling_blenderbot_small.py

# coding=utf-8 # Copyright 2021, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch BlenderbotSmall model. """ import tempfile import unittest from transformers import BlenderbotSmallConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor if is_torch_available(): import torch from transformers import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallTokenizer from transformers.models.blenderbot_small.modeling_blenderbot_small import ( BlenderbotSmallDecoder, BlenderbotSmallEncoder, BlenderbotSmallForCausalLM, ) def prepare_blenderbot_small_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class BlenderbotSmallModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id # forcing a certain token to be generated, sets all other tokens to -inf # if however the token to be generated is already at -inf then it can lead token # `nan` values and thus break generation self.forced_bos_token_id = None self.forced_eos_token_id = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.get_config() inputs_dict = prepare_blenderbot_small_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return BlenderbotSmallConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, forced_bos_token_id=self.forced_bos_token_id, forced_eos_token_id=self.forced_eos_token_id, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BlenderbotSmallModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] head_mask = inputs_dict["head_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = BlenderbotSmallModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = BlenderbotSmallEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[ 0 ] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = BlenderbotSmallDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, encoder_attention_mask=inputs_dict["attention_mask"], )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class BlenderbotSmallModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BlenderbotSmallModel, BlenderbotSmallForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (BlenderbotSmallForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True fx_compatible = True test_pruning = False test_missing_keys = False def setUp(self): self.model_tester = BlenderbotSmallModelTester(self) self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = BlenderbotSmallForConditionalGeneration(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def assert_tensors_close(a, b, atol=1e-12, prefix=""): """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item() if a.numel() > 100: msg = f"tensor values are {pct_different:.1%} percent different." else: msg = f"{a} != {b}" if prefix: msg = prefix + ": " + msg raise AssertionError(msg) @require_torch class Blenderbot90MIntegrationTests(unittest.TestCase): ckpt = "facebook/blenderbot-90M" @cached_property def model(self): model = BlenderbotSmallForConditionalGeneration.from_pretrained(self.ckpt).to(torch_device) if torch_device == "cuda": model = model.half() return model @cached_property def tokenizer(self): return BlenderbotSmallTokenizer.from_pretrained(self.ckpt) @slow def test_90_generation_from_long_input(self): src_text = [ "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel" " like i'm going to throw up.\nand why is that?" ] model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device) assert isinstance(self.tokenizer, BlenderbotSmallTokenizer) generated_ids = self.model.generate(**model_inputs)[0] reply = self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) assert reply in ( "i don't know. i just feel like i'm going to throw up. it's not fun.", "i'm not sure. i just feel like i've been feeling like i have to be in a certain place", ) @slow def test_90_generation_from_short_input(self): model_inputs = self.tokenizer(["sam"], return_tensors="pt").to(torch_device) generated_utterances = self.model.generate(**model_inputs) clean_txt = self.tokenizer.decode( generated_utterances[0], skip_special_tokens=True, clean_up_tokenization_spaces=True ) assert clean_txt in ( "have you ever been to a sam club? it's a great club in the south.", "have you ever heard of sam harris? he's an american singer, songwriter, and actor.", ) class BlenderbotSmallStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=4, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = BlenderbotSmallConfig( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = BlenderbotSmallDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, attention_mask, lm_labels, ): model = BlenderbotSmallDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class BlenderbotSmallStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BlenderbotSmallDecoder, BlenderbotSmallForCausalLM) if is_torch_available() else () all_generative_model_classes = (BlenderbotSmallForCausalLM,) if is_torch_available() else () test_pruning = False is_encoder_decoder = False def setUp( self, ): self.model_tester = BlenderbotSmallStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return

# coding=utf-8 # Copyright 2021, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch BlenderbotSmall model. """ import tempfile import unittest from transformers import BlenderbotSmallConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor if is_torch_available(): import torch from transformers import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallTokenizer from transformers.models.blenderbot_small.modeling_blenderbot_small import ( BlenderbotSmallDecoder, BlenderbotSmallEncoder, BlenderbotSmallForCausalLM, ) def prepare_blenderbot_small_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class BlenderbotSmallModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id # forcing a certain token to be generated, sets all other tokens to -inf # if however the token to be generated is already at -inf then it can lead token # `nan` values and thus break generation self.forced_bos_token_id = None self.forced_eos_token_id = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.get_config() inputs_dict = prepare_blenderbot_small_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return BlenderbotSmallConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, forced_bos_token_id=self.forced_bos_token_id, forced_eos_token_id=self.forced_eos_token_id, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BlenderbotSmallModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] head_mask = inputs_dict["head_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = BlenderbotSmallModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = BlenderbotSmallEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[ 0 ] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = BlenderbotSmallDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, encoder_attention_mask=inputs_dict["attention_mask"], )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class BlenderbotSmallModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BlenderbotSmallModel, BlenderbotSmallForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (BlenderbotSmallForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True fx_compatible = True test_pruning = False test_missing_keys = False def setUp(self): self.model_tester = BlenderbotSmallModelTester(self) self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = BlenderbotSmallForConditionalGeneration(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def assert_tensors_close(a, b, atol=1e-12, prefix=""): """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item() if a.numel() > 100: msg = f"tensor values are {pct_different:.1%} percent different." else: msg = f"{a} != {b}" if prefix: msg = prefix + ": " + msg raise AssertionError(msg) @require_torch class Blenderbot90MIntegrationTests(unittest.TestCase): ckpt = "facebook/blenderbot-90M" @cached_property def model(self): model = BlenderbotSmallForConditionalGeneration.from_pretrained(self.ckpt).to(torch_device) if torch_device == "cuda": model = model.half() return model @cached_property def tokenizer(self): return BlenderbotSmallTokenizer.from_pretrained(self.ckpt) @slow def test_90_generation_from_long_input(self): src_text = [ "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel" " like i'm going to throw up.\nand why is that?" ] model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device) assert isinstance(self.tokenizer, BlenderbotSmallTokenizer) generated_ids = self.model.generate(**model_inputs)[0] reply = self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) assert reply in ( "i don't know. i just feel like i'm going to throw up. it's not fun.", "i'm not sure. i just feel like i've been feeling like i have to be in a certain place", ) @slow def test_90_generation_from_short_input(self): model_inputs = self.tokenizer(["sam"], return_tensors="pt").to(torch_device) generated_utterances = self.model.generate(**model_inputs) clean_txt = self.tokenizer.decode( generated_utterances[0], skip_special_tokens=True, clean_up_tokenization_spaces=True ) assert clean_txt in ( "have you ever been to a sam club? it's a great club in the south.", "have you ever heard of sam harris? he's an american singer, songwriter, and actor.", ) class BlenderbotSmallStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=4, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = BlenderbotSmallConfig( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = BlenderbotSmallDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, attention_mask, lm_labels, ): model = BlenderbotSmallDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class BlenderbotSmallStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BlenderbotSmallDecoder, BlenderbotSmallForCausalLM) if is_torch_available() else () all_generative_model_classes = (BlenderbotSmallForCausalLM,) if is_torch_available() else () test_pruning = False is_encoder_decoder = False def setUp( self, ): self.model_tester = BlenderbotSmallStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./src/transformers/commands/__init__.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from argparse import ArgumentParser class BaseTransformersCLICommand(ABC): @staticmethod @abstractmethod def register_subcommand(parser: ArgumentParser): raise NotImplementedError() @abstractmethod def run(self): raise NotImplementedError()

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from argparse import ArgumentParser class BaseTransformersCLICommand(ABC): @staticmethod @abstractmethod def register_subcommand(parser: ArgumentParser): raise NotImplementedError() @abstractmethod def run(self): raise NotImplementedError()

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/sagemaker/README.md

# Testing new Hugging Face Deep Learning Container. This document explains the testing strategy for releasing the new Hugging Face Deep Learning Container. AWS maintains 14 days of currency with framework releases. Besides framework releases, AWS release train is bi-weekly on Monday. Code cutoff date for any changes is the Wednesday before release-Monday. ## Test Case 1: Releasing a New Version (Minor/Major) of 🤗 Transformers ### Requirements: Test should run on Release Candidate for new `transformers` release to validate the new release is compatible with the DLCs. To run these tests you need credentials for the HF SageMaker AWS Account. You can ask @philschmid or @n1t0 to get access. ### Run Tests: Before we can run the tests we need to adjust the `requirements.txt` for PyTorch under `/tests/sagemaker/scripts/pytorch` and for TensorFlow under `/tests/sagemaker/scripts/pytorch`. We adjust the branch to the new RC-tag. ``` git+https://github.com/huggingface/transformers.git@v4.5.0.rc0 # install main or adjust ist with vX.X.X for installing version specific-transforms ``` After we adjusted the `requirements.txt` we can run Amazon SageMaker tests with: ```bash AWS_PROFILE=<enter-your-profile> make test-sagemaker ``` These tests take around 10-15 minutes to finish. Preferably make a screenshot of the successfully ran tests. ### After Transformers Release: After we have released the Release Candidate we need to create a PR at the [Deep Learning Container Repository](https://github.com/aws/deep-learning-containers). **Creating the update PR:** 1. Update the two latest `buildspec.yaml` config for [PyTorch](https://github.com/aws/deep-learning-containers/tree/master/huggingface/pytorch) and [TensorFlow](https://github.com/aws/deep-learning-containers/tree/master/huggingface/tensorflow). The two latest `buildspec.yaml` are the `buildspec.yaml` without a version tag and the one with the highest framework version, e.g. `buildspec-1-7-1.yml` and not `buildspec-1-6.yml`. To update the `buildspec.yaml` we need to adjust either the `transformers_version` or the `datasets_version` or both. Example for upgrading to `transformers 4.5.0` and `datasets 1.6.0`. ```yaml account_id: &ACCOUNT_ID <set-$ACCOUNT_ID-in-environment> region: &REGION <set-$REGION-in-environment> base_framework: &BASE_FRAMEWORK pytorch framework: &FRAMEWORK !join [ "huggingface_", *BASE_FRAMEWORK] version: &VERSION 1.6.0 short_version: &SHORT_VERSION 1.6 repository_info: training_repository: &TRAINING_REPOSITORY image_type: &TRAINING_IMAGE_TYPE training root: !join [ "huggingface/", *BASE_FRAMEWORK, "/", *TRAINING_IMAGE_TYPE ] repository_name: &REPOSITORY_NAME !join ["pr", "-", "huggingface", "-", *BASE_FRAMEWORK, "-", *TRAINING_IMAGE_TYPE] repository: &REPOSITORY !join [ *ACCOUNT_ID, .dkr.ecr., *REGION, .amazonaws.com/, *REPOSITORY_NAME ] images: BuildHuggingFacePytorchGpuPy37Cu110TrainingDockerImage: <<: *TRAINING_REPOSITORY build: &HUGGINGFACE_PYTORCH_GPU_TRAINING_PY3 false image_size_baseline: &IMAGE_SIZE_BASELINE 15000 device_type: &DEVICE_TYPE gpu python_version: &DOCKER_PYTHON_VERSION py3 tag_python_version: &TAG_PYTHON_VERSION py36 cuda_version: &CUDA_VERSION cu110 os_version: &OS_VERSION ubuntu18.04 transformers_version: &TRANSFORMERS_VERSION 4.5.0 # this was adjusted from 4.4.2 to 4.5.0 datasets_version: &DATASETS_VERSION 1.6.0 # this was adjusted from 1.5.0 to 1.6.0 tag: !join [ *VERSION, '-', 'transformers', *TRANSFORMERS_VERSION, '-', *DEVICE_TYPE, '-', *TAG_PYTHON_VERSION, '-', *CUDA_VERSION, '-', *OS_VERSION ] docker_file: !join [ docker/, *SHORT_VERSION, /, *DOCKER_PYTHON_VERSION, /, *CUDA_VERSION, /Dockerfile., *DEVICE_TYPE ] ``` 2. In the PR comment describe what test, we ran and with which package versions. Here you can copy the table from [Current Tests](#current-tests). 2. In the PR comment describe what test we ran and with which framework versions. Here you can copy the table from [Current Tests](#current-tests). You can take a look at this [PR](https://github.com/aws/deep-learning-containers/pull/1016), which information are needed. ## Test Case 2: Releasing a New AWS Framework DLC ## Execute Tests ### Requirements: AWS is going to release new DLCs for PyTorch and/or TensorFlow. The Tests should run on the new framework versions with current `transformers` release to validate the new framework release is compatible with the `transformers` version. To run these tests you need credentials for the HF SageMaker AWS Account. You can ask @philschmid or @n1t0 to get access. AWS will notify us with a new issue in the repository pointing to their framework upgrade PR. ### Run Tests: Before we can run the tests we need to adjust the `requirements.txt` for Pytorch under `/tests/sagemaker/scripts/pytorch` and for Tensorflow under `/tests/sagemaker/scripts/pytorch`. We add the new framework version to it. ``` torch==1.8.1 # for pytorch tensorflow-gpu==2.5.0 # for tensorflow ``` After we adjusted the `requirements.txt` we can run Amazon SageMaker tests with. ```bash AWS_PROFILE=<enter-your-profile> make test-sagemaker ``` These tests take around 10-15 minutes to finish. Preferably make a screenshot of the successfully ran tests. ### After successful Tests: After we have successfully run tests for the new framework version we need to create a PR at the [Deep Learning Container Repository](https://github.com/aws/deep-learning-containers). **Creating the update PR:** 1. Create a new `buildspec.yaml` config for [PyTorch](https://github.com/aws/deep-learning-containers/tree/master/huggingface/pytorch) and [TensorFlow](https://github.com/aws/deep-learning-containers/tree/master/huggingface/tensorflow) and rename the old `buildspec.yaml` to `buildespec-x.x.x`, where `x.x.x` is the base framework version, e.g. if pytorch 1.6.0 is the latest version in `buildspec.yaml` the file should be renamed to `buildspec-yaml-1-6.yaml`. To create the new `buildspec.yaml` we need to adjust the `version` and the `short_version`. Example for upgrading to `pytorch 1.7.1`. ```yaml account_id: &ACCOUNT_ID <set-$ACCOUNT_ID-in-environment> region: &REGION <set-$REGION-in-environment> base_framework: &BASE_FRAMEWORK pytorch framework: &FRAMEWORK !join [ "huggingface_", *BASE_FRAMEWORK] version: &VERSION 1.7.1 # this was adjusted from 1.6.0 to 1.7.1 short_version: &SHORT_VERSION 1.7 # this was adjusted from 1.6 to 1.7 repository_info: training_repository: &TRAINING_REPOSITORY image_type: &TRAINING_IMAGE_TYPE training root: !join [ "huggingface/", *BASE_FRAMEWORK, "/", *TRAINING_IMAGE_TYPE ] repository_name: &REPOSITORY_NAME !join ["pr", "-", "huggingface", "-", *BASE_FRAMEWORK, "-", *TRAINING_IMAGE_TYPE] repository: &REPOSITORY !join [ *ACCOUNT_ID, .dkr.ecr., *REGION, .amazonaws.com/, *REPOSITORY_NAME ] images: BuildHuggingFacePytorchGpuPy37Cu110TrainingDockerImage: <<: *TRAINING_REPOSITORY build: &HUGGINGFACE_PYTORCH_GPU_TRAINING_PY3 false image_size_baseline: &IMAGE_SIZE_BASELINE 15000 device_type: &DEVICE_TYPE gpu python_version: &DOCKER_PYTHON_VERSION py3 tag_python_version: &TAG_PYTHON_VERSION py36 cuda_version: &CUDA_VERSION cu110 os_version: &OS_VERSION ubuntu18.04 transformers_version: &TRANSFORMERS_VERSION 4.4.2 datasets_version: &DATASETS_VERSION 1.5.0 tag: !join [ *VERSION, '-', 'transformers', *TRANSFORMERS_VERSION, '-', *DEVICE_TYPE, '-', *TAG_PYTHON_VERSION, '-', *CUDA_VERSION, '-', *OS_VERSION ] docker_file: !join [ docker/, *SHORT_VERSION, /, *DOCKER_PYTHON_VERSION, /, *CUDA_VERSION, /Dockerfile., *DEVICE_TYPE ] ``` 2. In the PR comment describe what test we ran and with which framework versions. Here you can copy the table from [Current Tests](#current-tests). You can take a look at this [PR](https://github.com/aws/deep-learning-containers/pull/1025), which information are needed. ## Current Tests | ID | Description | Platform | #GPUS | Collected & evaluated metrics | |-------------------------------------|-------------------------------------------------------------------|-----------------------------|-------|------------------------------------------| | pytorch-transfromers-test-single | test bert finetuning using BERT fromtransformerlib+PT | SageMaker createTrainingJob | 1 | train_runtime, eval_accuracy & eval_loss | | pytorch-transfromers-test-2-ddp | test bert finetuning using BERT from transformer lib+ PT DPP | SageMaker createTrainingJob | 16 | train_runtime, eval_accuracy & eval_loss | | pytorch-transfromers-test-2-smd | test bert finetuning using BERT from transformer lib+ PT SM DDP | SageMaker createTrainingJob | 16 | train_runtime, eval_accuracy & eval_loss | | pytorch-transfromers-test-1-smp | test roberta finetuning using BERT from transformer lib+ PT SM MP | SageMaker createTrainingJob | 8 | train_runtime, eval_accuracy & eval_loss | | tensorflow-transfromers-test-single | Test bert finetuning using BERT from transformer lib+TF | SageMaker createTrainingJob | 1 | train_runtime, eval_accuracy & eval_loss | | tensorflow-transfromers-test-2-smd | test bert finetuning using BERT from transformer lib+ TF SM DDP | SageMaker createTrainingJob | 16 | train_runtime, eval_accuracy & eval_loss |

# Testing new Hugging Face Deep Learning Container. This document explains the testing strategy for releasing the new Hugging Face Deep Learning Container. AWS maintains 14 days of currency with framework releases. Besides framework releases, AWS release train is bi-weekly on Monday. Code cutoff date for any changes is the Wednesday before release-Monday. ## Test Case 1: Releasing a New Version (Minor/Major) of 🤗 Transformers ### Requirements: Test should run on Release Candidate for new `transformers` release to validate the new release is compatible with the DLCs. To run these tests you need credentials for the HF SageMaker AWS Account. You can ask @philschmid or @n1t0 to get access. ### Run Tests: Before we can run the tests we need to adjust the `requirements.txt` for PyTorch under `/tests/sagemaker/scripts/pytorch` and for TensorFlow under `/tests/sagemaker/scripts/pytorch`. We adjust the branch to the new RC-tag. ``` git+https://github.com/huggingface/transformers.git@v4.5.0.rc0 # install main or adjust ist with vX.X.X for installing version specific-transforms ``` After we adjusted the `requirements.txt` we can run Amazon SageMaker tests with: ```bash AWS_PROFILE=<enter-your-profile> make test-sagemaker ``` These tests take around 10-15 minutes to finish. Preferably make a screenshot of the successfully ran tests. ### After Transformers Release: After we have released the Release Candidate we need to create a PR at the [Deep Learning Container Repository](https://github.com/aws/deep-learning-containers). **Creating the update PR:** 1. Update the two latest `buildspec.yaml` config for [PyTorch](https://github.com/aws/deep-learning-containers/tree/master/huggingface/pytorch) and [TensorFlow](https://github.com/aws/deep-learning-containers/tree/master/huggingface/tensorflow). The two latest `buildspec.yaml` are the `buildspec.yaml` without a version tag and the one with the highest framework version, e.g. `buildspec-1-7-1.yml` and not `buildspec-1-6.yml`. To update the `buildspec.yaml` we need to adjust either the `transformers_version` or the `datasets_version` or both. Example for upgrading to `transformers 4.5.0` and `datasets 1.6.0`. ```yaml account_id: &ACCOUNT_ID <set-$ACCOUNT_ID-in-environment> region: &REGION <set-$REGION-in-environment> base_framework: &BASE_FRAMEWORK pytorch framework: &FRAMEWORK !join [ "huggingface_", *BASE_FRAMEWORK] version: &VERSION 1.6.0 short_version: &SHORT_VERSION 1.6 repository_info: training_repository: &TRAINING_REPOSITORY image_type: &TRAINING_IMAGE_TYPE training root: !join [ "huggingface/", *BASE_FRAMEWORK, "/", *TRAINING_IMAGE_TYPE ] repository_name: &REPOSITORY_NAME !join ["pr", "-", "huggingface", "-", *BASE_FRAMEWORK, "-", *TRAINING_IMAGE_TYPE] repository: &REPOSITORY !join [ *ACCOUNT_ID, .dkr.ecr., *REGION, .amazonaws.com/, *REPOSITORY_NAME ] images: BuildHuggingFacePytorchGpuPy37Cu110TrainingDockerImage: <<: *TRAINING_REPOSITORY build: &HUGGINGFACE_PYTORCH_GPU_TRAINING_PY3 false image_size_baseline: &IMAGE_SIZE_BASELINE 15000 device_type: &DEVICE_TYPE gpu python_version: &DOCKER_PYTHON_VERSION py3 tag_python_version: &TAG_PYTHON_VERSION py36 cuda_version: &CUDA_VERSION cu110 os_version: &OS_VERSION ubuntu18.04 transformers_version: &TRANSFORMERS_VERSION 4.5.0 # this was adjusted from 4.4.2 to 4.5.0 datasets_version: &DATASETS_VERSION 1.6.0 # this was adjusted from 1.5.0 to 1.6.0 tag: !join [ *VERSION, '-', 'transformers', *TRANSFORMERS_VERSION, '-', *DEVICE_TYPE, '-', *TAG_PYTHON_VERSION, '-', *CUDA_VERSION, '-', *OS_VERSION ] docker_file: !join [ docker/, *SHORT_VERSION, /, *DOCKER_PYTHON_VERSION, /, *CUDA_VERSION, /Dockerfile., *DEVICE_TYPE ] ``` 2. In the PR comment describe what test, we ran and with which package versions. Here you can copy the table from [Current Tests](#current-tests). 2. In the PR comment describe what test we ran and with which framework versions. Here you can copy the table from [Current Tests](#current-tests). You can take a look at this [PR](https://github.com/aws/deep-learning-containers/pull/1016), which information are needed. ## Test Case 2: Releasing a New AWS Framework DLC ## Execute Tests ### Requirements: AWS is going to release new DLCs for PyTorch and/or TensorFlow. The Tests should run on the new framework versions with current `transformers` release to validate the new framework release is compatible with the `transformers` version. To run these tests you need credentials for the HF SageMaker AWS Account. You can ask @philschmid or @n1t0 to get access. AWS will notify us with a new issue in the repository pointing to their framework upgrade PR. ### Run Tests: Before we can run the tests we need to adjust the `requirements.txt` for Pytorch under `/tests/sagemaker/scripts/pytorch` and for Tensorflow under `/tests/sagemaker/scripts/pytorch`. We add the new framework version to it. ``` torch==1.8.1 # for pytorch tensorflow-gpu==2.5.0 # for tensorflow ``` After we adjusted the `requirements.txt` we can run Amazon SageMaker tests with. ```bash AWS_PROFILE=<enter-your-profile> make test-sagemaker ``` These tests take around 10-15 minutes to finish. Preferably make a screenshot of the successfully ran tests. ### After successful Tests: After we have successfully run tests for the new framework version we need to create a PR at the [Deep Learning Container Repository](https://github.com/aws/deep-learning-containers). **Creating the update PR:** 1. Create a new `buildspec.yaml` config for [PyTorch](https://github.com/aws/deep-learning-containers/tree/master/huggingface/pytorch) and [TensorFlow](https://github.com/aws/deep-learning-containers/tree/master/huggingface/tensorflow) and rename the old `buildspec.yaml` to `buildespec-x.x.x`, where `x.x.x` is the base framework version, e.g. if pytorch 1.6.0 is the latest version in `buildspec.yaml` the file should be renamed to `buildspec-yaml-1-6.yaml`. To create the new `buildspec.yaml` we need to adjust the `version` and the `short_version`. Example for upgrading to `pytorch 1.7.1`. ```yaml account_id: &ACCOUNT_ID <set-$ACCOUNT_ID-in-environment> region: &REGION <set-$REGION-in-environment> base_framework: &BASE_FRAMEWORK pytorch framework: &FRAMEWORK !join [ "huggingface_", *BASE_FRAMEWORK] version: &VERSION 1.7.1 # this was adjusted from 1.6.0 to 1.7.1 short_version: &SHORT_VERSION 1.7 # this was adjusted from 1.6 to 1.7 repository_info: training_repository: &TRAINING_REPOSITORY image_type: &TRAINING_IMAGE_TYPE training root: !join [ "huggingface/", *BASE_FRAMEWORK, "/", *TRAINING_IMAGE_TYPE ] repository_name: &REPOSITORY_NAME !join ["pr", "-", "huggingface", "-", *BASE_FRAMEWORK, "-", *TRAINING_IMAGE_TYPE] repository: &REPOSITORY !join [ *ACCOUNT_ID, .dkr.ecr., *REGION, .amazonaws.com/, *REPOSITORY_NAME ] images: BuildHuggingFacePytorchGpuPy37Cu110TrainingDockerImage: <<: *TRAINING_REPOSITORY build: &HUGGINGFACE_PYTORCH_GPU_TRAINING_PY3 false image_size_baseline: &IMAGE_SIZE_BASELINE 15000 device_type: &DEVICE_TYPE gpu python_version: &DOCKER_PYTHON_VERSION py3 tag_python_version: &TAG_PYTHON_VERSION py36 cuda_version: &CUDA_VERSION cu110 os_version: &OS_VERSION ubuntu18.04 transformers_version: &TRANSFORMERS_VERSION 4.4.2 datasets_version: &DATASETS_VERSION 1.5.0 tag: !join [ *VERSION, '-', 'transformers', *TRANSFORMERS_VERSION, '-', *DEVICE_TYPE, '-', *TAG_PYTHON_VERSION, '-', *CUDA_VERSION, '-', *OS_VERSION ] docker_file: !join [ docker/, *SHORT_VERSION, /, *DOCKER_PYTHON_VERSION, /, *CUDA_VERSION, /Dockerfile., *DEVICE_TYPE ] ``` 2. In the PR comment describe what test we ran and with which framework versions. Here you can copy the table from [Current Tests](#current-tests). You can take a look at this [PR](https://github.com/aws/deep-learning-containers/pull/1025), which information are needed. ## Current Tests | ID | Description | Platform | #GPUS | Collected & evaluated metrics | |-------------------------------------|-------------------------------------------------------------------|-----------------------------|-------|------------------------------------------| | pytorch-transfromers-test-single | test bert finetuning using BERT fromtransformerlib+PT | SageMaker createTrainingJob | 1 | train_runtime, eval_accuracy & eval_loss | | pytorch-transfromers-test-2-ddp | test bert finetuning using BERT from transformer lib+ PT DPP | SageMaker createTrainingJob | 16 | train_runtime, eval_accuracy & eval_loss | | pytorch-transfromers-test-2-smd | test bert finetuning using BERT from transformer lib+ PT SM DDP | SageMaker createTrainingJob | 16 | train_runtime, eval_accuracy & eval_loss | | pytorch-transfromers-test-1-smp | test roberta finetuning using BERT from transformer lib+ PT SM MP | SageMaker createTrainingJob | 8 | train_runtime, eval_accuracy & eval_loss | | tensorflow-transfromers-test-single | Test bert finetuning using BERT from transformer lib+TF | SageMaker createTrainingJob | 1 | train_runtime, eval_accuracy & eval_loss | | tensorflow-transfromers-test-2-smd | test bert finetuning using BERT from transformer lib+ TF SM DDP | SageMaker createTrainingJob | 16 | train_runtime, eval_accuracy & eval_loss |

huggingface/transformers

Rephrasing the link.

# What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

Narsil

e9d9982e7c99da93c7b5ed0058bdd49e749aee5b

a239bdd28ff0ea85e4405fdd02623d25268904f0

Rephrasing the link.. # What does this PR do? Fixes https://github.com/huggingface/transformers/pull/20226#discussion_r1023296368 <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. Please tag fewer than 3 people. Models: - albert, bert, xlm: @LysandreJik - blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj - longformer, reformer, transfoxl, xlnet: @patrickvonplaten - fsmt: @stas00 - funnel: @sgugger - gpt2: @patrickvonplaten, @LysandreJik - rag: @patrickvonplaten, @lhoestq - tensorflow: @LysandreJik Library: - benchmarks: @patrickvonplaten - deepspeed: @stas00 - ray/raytune: @richardliaw, @amogkam - text generation: @patrickvonplaten - tokenizers: @n1t0, @LysandreJik - trainer: @sgugger - pipelines: @LysandreJik Documentation: @sgugger HF projects: - datasets: [different repo](https://github.com/huggingface/datasets) - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) Examples: - maintained examples (not research project or legacy): @sgugger, @patil-suraj - research_projects/bert-loses-patience: @JetRunner - research_projects/distillation: @VictorSanh -->

./tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Wav2Vec2-Conformer model. """ import math import unittest import numpy as np from datasets import load_dataset from transformers import Wav2Vec2ConformerConfig, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from transformers import ( Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import ( Wav2Vec2ConformerGumbelVectorQuantizer, _compute_mask_indices, _sample_negative_indices, ) class Wav2Vec2ConformerModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=4, num_attention_heads=2, hidden_dropout_prob=0.1, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, num_adapter_layers=1, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, position_embeddings_type="relative", scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.num_adapter_layers = num_adapter_layers self.adapter_stride = adapter_stride self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.scope = scope self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.position_embeddings_type = position_embeddings_type output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1 def prepare_config_and_inputs(self, position_embeddings_type="relative"): input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config(position_embeddings_type=position_embeddings_type) return config, input_values, attention_mask def get_config(self, position_embeddings_type="relative"): return Wav2Vec2ConformerConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, do_stable_layer_norm=self.do_stable_layer_norm, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, num_adapter_layers=self.num_adapter_layers, adapter_stride=self.adapter_stride, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, position_embeddings_type=position_embeddings_type, ) def create_and_check_model(self, config, input_values, attention_mask): model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter(self, config, input_values, attention_mask): config.add_adapter = True model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 2 * config.hidden_size model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size) ) def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size), ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lenghts are at least # one shorter than logit lenghts to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with self.parent.assertRaises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Wav2Vec2ConformerModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( Wav2Vec2ConformerForCTC, Wav2Vec2ConformerModel, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForXVector, ) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = Wav2Vec2ConformerModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_no_rel_pos(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter(*config_and_inputs) def test_model_with_adapter_for_ctc(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs) def test_model_with_adapter_proj_dim(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Wav2Vec2Conformer has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Wav2Vec2Conformer cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Wav2Vec2Conformer has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_flax_to_pt(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_pt_to_flax(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "pos_bias_v", "pos_bias_u", "pointwise_conv1", "pointwise_conv2", "feature_projection.projection.weight", "feature_projection.projection.bias", "objective.weight", ] if param.requires_grad: if any([x in name for x in uniform_init_parms]): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None: module.pos_bias_u.data.fill_(3) if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None: module.pos_bias_v.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") self.assertIsNotNone(model) @require_torch class Wav2Vec2ConformerUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_low_prob(self): # with these settings num_masked_spans=0.5, which means probabilistic rounding # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in # the other 5 out of 10, cases num_masked_spans=1 n_trials = 100 batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 count_dimensions_masked = 0 count_dimensions_not_masked = 0 for _ in range(n_trials): mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) num_masks = torch.sum(mask).item() if num_masks > 0: count_dimensions_masked += 1 else: count_dimensions_not_masked += 1 # as we test for at least 10 masked dimension and at least # 10 non-masked dimension, this test could fail with probability: # P(100 coin flips, at most 9 heads) = 1.66e-18 self.assertGreater(count_dimensions_masked, int(n_trials * 0.1)) self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1)) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask ) mask = torch.from_numpy(mask).to(torch_device) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_mask_indices_short_audio(self): batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) # force one example to be heavily padded attention_mask[0, 5:] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2 ) # make sure that non-padded examples cannot be padded self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any()) def test_compute_perplexity(self): probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs) self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3) # mask half of the input mask = torch.ones((2,), device=torch_device, dtype=torch.bool) mask[0] = 0 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask) self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3) def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # sample negative indices sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100) # sample negative indices sampled_negative_indices = _sample_negative_indices( (batch_size, sequence_length), num_negatives, mask.cpu().numpy() ) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @slow class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]) speech_samples = speech_samples[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_ctc_normal_batched_rel_pos(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loincloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_normal_batched_rope(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rope-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_pretrained(self): model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) batch_size = inputs_dict["input_values"].shape[0] feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1])) features_shape = (batch_size, feature_seq_length) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, min_masks=2, ) mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # ... now compare to randomly initialized model config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval() with torch.no_grad(): outputs_rand = model_rand( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim_rand = torch.cosine_similarity( outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1 ) # retrieve cosine sim of masked features cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices] # a pretrained wav2vec2_conformer model has learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states > 0.5 # a random wav2vec2_conformer model has not learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states is very likely < 0.1 self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Wav2Vec2-Conformer model. """ import math import unittest import numpy as np from datasets import load_dataset from transformers import Wav2Vec2ConformerConfig, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from transformers import ( Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import ( Wav2Vec2ConformerGumbelVectorQuantizer, _compute_mask_indices, _sample_negative_indices, ) class Wav2Vec2ConformerModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=4, num_attention_heads=2, hidden_dropout_prob=0.1, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, num_adapter_layers=1, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, position_embeddings_type="relative", scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.num_adapter_layers = num_adapter_layers self.adapter_stride = adapter_stride self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.scope = scope self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.position_embeddings_type = position_embeddings_type output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1 def prepare_config_and_inputs(self, position_embeddings_type="relative"): input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config(position_embeddings_type=position_embeddings_type) return config, input_values, attention_mask def get_config(self, position_embeddings_type="relative"): return Wav2Vec2ConformerConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, do_stable_layer_norm=self.do_stable_layer_norm, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, num_adapter_layers=self.num_adapter_layers, adapter_stride=self.adapter_stride, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, position_embeddings_type=position_embeddings_type, ) def create_and_check_model(self, config, input_values, attention_mask): model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter(self, config, input_values, attention_mask): config.add_adapter = True model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 2 * config.hidden_size model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size) ) def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size), ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = Wav2Vec2ConformerModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lenghts are at least # one shorter than logit lenghts to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with self.parent.assertRaises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Wav2Vec2ConformerModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( Wav2Vec2ConformerForCTC, Wav2Vec2ConformerModel, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForXVector, ) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = Wav2Vec2ConformerModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_no_rel_pos(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter(*config_and_inputs) def test_model_with_adapter_for_ctc(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs) def test_model_with_adapter_proj_dim(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Wav2Vec2Conformer has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Wav2Vec2Conformer cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Wav2Vec2Conformer has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_flax_to_pt(self): pass @is_pt_flax_cross_test # non-robust architecture does not exist in Flax def test_equivalence_pt_to_flax(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "pos_bias_v", "pos_bias_u", "pointwise_conv1", "pointwise_conv2", "feature_projection.projection.weight", "feature_projection.projection.bias", "objective.weight", ] if param.requires_grad: if any([x in name for x in uniform_init_parms]): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None: module.pos_bias_u.data.fill_(3) if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None: module.pos_bias_v.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") self.assertIsNotNone(model) @require_torch class Wav2Vec2ConformerUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_low_prob(self): # with these settings num_masked_spans=0.5, which means probabilistic rounding # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in # the other 5 out of 10, cases num_masked_spans=1 n_trials = 100 batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 count_dimensions_masked = 0 count_dimensions_not_masked = 0 for _ in range(n_trials): mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) num_masks = torch.sum(mask).item() if num_masks > 0: count_dimensions_masked += 1 else: count_dimensions_not_masked += 1 # as we test for at least 10 masked dimension and at least # 10 non-masked dimension, this test could fail with probability: # P(100 coin flips, at most 9 heads) = 1.66e-18 self.assertGreater(count_dimensions_masked, int(n_trials * 0.1)) self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1)) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask ) mask = torch.from_numpy(mask).to(torch_device) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_mask_indices_short_audio(self): batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) # force one example to be heavily padded attention_mask[0, 5:] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2 ) # make sure that non-padded examples cannot be padded self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any()) def test_compute_perplexity(self): probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs) self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3) # mask half of the input mask = torch.ones((2,), device=torch_device, dtype=torch.bool) mask[0] = 0 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask) self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3) def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # sample negative indices sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100) # sample negative indices sampled_negative_indices = _sample_negative_indices( (batch_size, sequence_length), num_negatives, mask.cpu().numpy() ) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @slow class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]) speech_samples = speech_samples[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_ctc_normal_batched_rel_pos(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loincloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_normal_batched_rope(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rope-large-960h-ft", do_lower_case=True ) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_pretrained(self): model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) batch_size = inputs_dict["input_values"].shape[0] feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1])) features_shape = (batch_size, feature_seq_length) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, min_masks=2, ) mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # ... now compare to randomly initialized model config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval() with torch.no_grad(): outputs_rand = model_rand( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim_rand = torch.cosine_similarity( outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1 ) # retrieve cosine sim of masked features cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices] # a pretrained wav2vec2_conformer model has learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states > 0.5 # a random wav2vec2_conformer model has not learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states is very likely < 0.1 self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/image-classification/requirements.txt

torch>=1.5.0 torchvision>=0.6.0 datasets>=1.17.0 evaluate

accelerate>=0.12.0 torch>=1.5.0 torchvision>=0.6.0 datasets>=1.17.0 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/language-modeling/requirements.txt

accelerate torch >= 1.3 datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf evaluate

accelerate >= 0.12.0 torch >= 1.3 datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/multiple-choice/requirements.txt

accelerate sentencepiece != 0.1.92 protobuf torch >= 1.3 evaluate

accelerate >= 0.12.0 sentencepiece != 0.1.92 protobuf torch >= 1.3 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/question-answering/requirements.txt

accelerate datasets >= 1.8.0 torch >= 1.3.0 evaluate

accelerate >= 0.12.0 datasets >= 1.8.0 torch >= 1.3.0 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/speech-pretraining/requirements.txt

datasets >= 1.12.0 torch >= 1.5 torchaudio accelerate >= 0.5.0 librosa

datasets >= 1.12.0 torch >= 1.5 torchaudio accelerate >= 0.12.0 librosa

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/summarization/requirements.txt

accelerate datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf rouge-score nltk py7zr torch >= 1.3 evaluate

accelerate >= 0.12.0 datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf rouge-score nltk py7zr torch >= 1.3 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/text-classification/requirements.txt

accelerate datasets >= 1.8.0 sentencepiece != 0.1.92 scipy scikit-learn protobuf torch >= 1.3 evaluate

accelerate >= 0.12.0 datasets >= 1.8.0 sentencepiece != 0.1.92 scipy scikit-learn protobuf torch >= 1.3 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/token-classification/requirements.txt

accelerate seqeval datasets >= 1.8.0 torch >= 1.3 evaluate

accelerate >= 0.12.0 seqeval datasets >= 1.8.0 torch >= 1.3 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/translation/requirements.txt

accelerate datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf sacrebleu >= 1.4.12 py7zr torch >= 1.3 evaluate

accelerate >= 0.12.0 datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf sacrebleu >= 1.4.12 py7zr torch >= 1.3 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/jax-projects/big_bird/requirements.txt

git+https://github.com/huggingface/transformers@main datasets sentencepiece wandb flax jsonlines

git+https://github.com/huggingface/transformers@main datasets sentencepiece wandb flax jsonlines

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/semantic-segmentation/requirements.txt

git://github.com/huggingface/accelerate.git datasets >= 2.0.0 torch >= 1.3 evaluate

git://github.com/huggingface/accelerate.git datasets >= 2.0.0 torch >= 1.3 evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/decision_transformer/requirements.txt

absl-py==1.0.0 aiohttp==3.8.1 aiosignal==1.2.0 alembic==1.7.7 appdirs==1.4.4 APScheduler==3.9.1 arrow==1.2.2 asttokens==2.0.5 astunparse==1.6.3 async-timeout==4.0.2 attrs==21.4.0 audioread==2.1.9 autopage==0.5.0 backcall==0.2.0 backoff==1.11.1 backports.zoneinfo==0.2.1 binaryornot==0.4.4 black==22.1.0 boto3==1.16.34 botocore==1.19.63 Brotli==1.0.9 cachetools==5.0.0 certifi==2021.10.8 cffi==1.15.0 chardet==4.0.0 charset-normalizer==2.0.12 chex==0.1.1 click==8.0.4 cliff==3.10.1 clldutils==3.11.1 cloudpickle==2.0.0 cmaes==0.8.2 cmd2==2.4.0 codecarbon==1.2.0 colorlog==6.6.0 cookiecutter==2.1.1 cryptography==36.0.2 csvw==2.0.0 cycler==0.11.0 Cython==0.29.28 dash==2.3.0 dash-bootstrap-components==1.0.3 dash-core-components==2.0.0 dash-html-components==2.0.0 dash-table==5.0.0 datasets==2.0.0 decorator==5.1.1 Deprecated==1.2.13 dill==0.3.4 dlinfo==1.2.1 dm-tree==0.1.6 docker==4.4.4 execnet==1.9.0 executing==0.8.3 faiss-cpu==1.7.2 fasteners==0.17.3 filelock==3.6.0 fire==0.4.0 flake8==4.0.1 Flask==2.0.3 Flask-Compress==1.11 flatbuffers==2.0 flax==0.4.0 fonttools==4.31.1 frozenlist==1.3.0 fsspec==2022.2.0 fugashi==1.1.2 gast==0.5.3 gitdb==4.0.9 GitPython==3.1.18 glfw==2.5.1 google-auth==2.6.2 google-auth-oauthlib==0.4.6 google-pasta==0.2.0 greenlet==1.1.2 grpcio==1.44.0 gym==0.23.1 gym-notices==0.0.6 h5py==3.6.0 huggingface-hub==0.4.0 hypothesis==6.39.4 idna==3.3 imageio==2.16.1 importlib-metadata==4.11.3 importlib-resources==5.4.0 iniconfig==1.1.1 ipadic==1.0.0 ipython==8.1.1 isodate==0.6.1 isort==5.10.1 itsdangerous==2.1.1 jax==0.3.4 jaxlib==0.3.2 jedi==0.18.1 Jinja2==2.11.3 jinja2-time==0.2.0 jmespath==0.10.0 joblib==1.2.0 jsonschema==4.4.0 keras==2.8.0 Keras-Preprocessing==1.1.2 kiwisolver==1.4.0 kubernetes==12.0.1 libclang==13.0.0 librosa==0.9.1 llvmlite==0.38.0 Mako==1.2.2 Markdown==3.3.6 MarkupSafe==1.1.1 matplotlib==3.5.1 matplotlib-inline==0.1.3 mccabe==0.6.1 msgpack==1.0.3 mujoco-py==2.1.2.14 multidict==6.0.2 multiprocess==0.70.12.2 mypy-extensions==0.4.3 nltk==3.7 numba==0.55.1 numpy==1.22.3 oauthlib==3.2.1 onnx==1.11.0 onnxconverter-common==1.9.0 opt-einsum==3.3.0 optax==0.1.1 optuna==2.10.0 packaging==21.3 pandas==1.4.1 parameterized==0.8.1 parso==0.8.3 pathspec==0.9.0 pbr==5.8.1 pexpect==4.8.0 phonemizer==3.0.1 pickleshare==0.7.5 Pillow==9.0.1 Pint==0.16.1 plac==1.3.4 platformdirs==2.5.1 plotly==5.6.0 pluggy==1.0.0 pooch==1.6.0 portalocker==2.0.0 poyo==0.5.0 prettytable==3.2.0 prompt-toolkit==3.0.28 protobuf==3.19.5 psutil==5.9.0 ptyprocess==0.7.0 pure-eval==0.2.2 py==1.11.0 py-cpuinfo==8.0.0 pyarrow==7.0.0 pyasn1==0.4.8 pyasn1-modules==0.2.8 pycodestyle==2.8.0 pycparser==2.21 pyctcdecode==0.3.0 pyflakes==2.4.0 Pygments==2.11.2 pygtrie==2.4.2 pynvml==11.4.1 pyOpenSSL==22.0.0 pyparsing==3.0.7 pyperclip==1.8.2 pypng==0.0.21 pyrsistent==0.18.1 pytest==7.1.1 pytest-forked==1.4.0 pytest-timeout==2.1.0 pytest-xdist==2.5.0 python-dateutil==2.8.2 python-slugify==6.1.1 pytz==2022.1 pytz-deprecation-shim==0.1.0.post0 PyYAML==6.0 ray==1.11.0 redis==4.1.4 regex==2022.3.15 requests==2.27.1 requests-oauthlib==1.3.1 resampy==0.2.2 responses==0.18.0 rfc3986==1.5.0 rouge-score==0.0.4 rsa==4.8 s3transfer==0.3.7 sacrebleu==1.5.1 sacremoses==0.0.49 scikit-learn==1.0.2 scipy==1.8.0 segments==2.2.0 sentencepiece==0.1.96 sigopt==8.2.0 six==1.16.0 smmap==5.0.0 sortedcontainers==2.4.0 SoundFile==0.10.3.post1 SQLAlchemy==1.4.32 stack-data==0.2.0 stevedore==3.5.0 tabulate==0.8.9 tenacity==8.0.1 tensorboard==2.8.0 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.1 tensorboardX==2.5 tensorflow==2.8.1 tensorflow-io-gcs-filesystem==0.24.0 termcolor==1.1.0 text-unidecode==1.3 tf-estimator-nightly==2.8.0.dev2021122109 tf2onnx==1.9.3 threadpoolctl==3.1.0 timeout-decorator==0.5.0 timm==0.5.4 tokenizers==0.11.6 tomli==2.0.1 toolz==0.11.2 torch==1.11.0 torchaudio==0.11.0 torchvision==0.12.0 tqdm==4.63.0 traitlets==5.1.1 -e git+git@github.com:edbeeching/transformers.git@77b90113ca0a0e4058b046796c874bdc98f1da61#egg=transformers typing-extensions==4.1.1 tzdata==2022.1 tzlocal==4.1 unidic==1.1.0 unidic-lite==1.0.8 uritemplate==4.1.1 urllib3==1.26.9 wasabi==0.9.0 wcwidth==0.2.5 websocket-client==1.3.1 Werkzeug==2.0.3 wrapt==1.14.0 xxhash==3.0.0 yarl==1.7.2 zipp==3.7.0

absl-py==1.0.0 aiohttp==3.8.1 aiosignal==1.2.0 alembic==1.7.7 appdirs==1.4.4 APScheduler==3.9.1 arrow==1.2.2 asttokens==2.0.5 astunparse==1.6.3 async-timeout==4.0.2 attrs==21.4.0 audioread==2.1.9 autopage==0.5.0 backcall==0.2.0 backoff==1.11.1 backports.zoneinfo==0.2.1 binaryornot==0.4.4 black==22.1.0 boto3==1.16.34 botocore==1.19.63 Brotli==1.0.9 cachetools==5.0.0 certifi==2021.10.8 cffi==1.15.0 chardet==4.0.0 charset-normalizer==2.0.12 chex==0.1.1 click==8.0.4 cliff==3.10.1 clldutils==3.11.1 cloudpickle==2.0.0 cmaes==0.8.2 cmd2==2.4.0 codecarbon==1.2.0 colorlog==6.6.0 cookiecutter==2.1.1 cryptography==36.0.2 csvw==2.0.0 cycler==0.11.0 Cython==0.29.28 dash==2.3.0 dash-bootstrap-components==1.0.3 dash-core-components==2.0.0 dash-html-components==2.0.0 dash-table==5.0.0 datasets==2.0.0 decorator==5.1.1 Deprecated==1.2.13 dill==0.3.4 dlinfo==1.2.1 dm-tree==0.1.6 docker==4.4.4 execnet==1.9.0 executing==0.8.3 faiss-cpu==1.7.2 fasteners==0.17.3 filelock==3.6.0 fire==0.4.0 flake8==4.0.1 Flask==2.0.3 Flask-Compress==1.11 flatbuffers==2.0 flax==0.4.0 fonttools==4.31.1 frozenlist==1.3.0 fsspec==2022.2.0 fugashi==1.1.2 gast==0.5.3 gitdb==4.0.9 GitPython==3.1.18 glfw==2.5.1 google-auth==2.6.2 google-auth-oauthlib==0.4.6 google-pasta==0.2.0 greenlet==1.1.2 grpcio==1.44.0 gym==0.23.1 gym-notices==0.0.6 h5py==3.6.0 huggingface-hub==0.4.0 hypothesis==6.39.4 idna==3.3 imageio==2.16.1 importlib-metadata==4.11.3 importlib-resources==5.4.0 iniconfig==1.1.1 ipadic==1.0.0 ipython==8.1.1 isodate==0.6.1 isort==5.10.1 itsdangerous==2.1.1 jax==0.3.4 jaxlib==0.3.2 jedi==0.18.1 Jinja2==2.11.3 jinja2-time==0.2.0 jmespath==0.10.0 joblib==1.2.0 jsonschema==4.4.0 keras==2.8.0 Keras-Preprocessing==1.1.2 kiwisolver==1.4.0 kubernetes==12.0.1 libclang==13.0.0 librosa==0.9.1 llvmlite==0.38.0 Mako==1.2.2 Markdown==3.3.6 MarkupSafe==1.1.1 matplotlib==3.5.1 matplotlib-inline==0.1.3 mccabe==0.6.1 msgpack==1.0.3 mujoco-py==2.1.2.14 multidict==6.0.2 multiprocess==0.70.12.2 mypy-extensions==0.4.3 nltk==3.7 numba==0.55.1 numpy==1.22.3 oauthlib==3.2.1 onnx==1.11.0 onnxconverter-common==1.9.0 opt-einsum==3.3.0 optax==0.1.1 optuna==2.10.0 packaging==21.3 pandas==1.4.1 parameterized==0.8.1 parso==0.8.3 pathspec==0.9.0 pbr==5.8.1 pexpect==4.8.0 phonemizer==3.0.1 pickleshare==0.7.5 Pillow==9.0.1 Pint==0.16.1 plac==1.3.4 platformdirs==2.5.1 plotly==5.6.0 pluggy==1.0.0 pooch==1.6.0 portalocker==2.0.0 poyo==0.5.0 prettytable==3.2.0 prompt-toolkit==3.0.28 protobuf==3.19.5 psutil==5.9.0 ptyprocess==0.7.0 pure-eval==0.2.2 py==1.11.0 py-cpuinfo==8.0.0 pyarrow==7.0.0 pyasn1==0.4.8 pyasn1-modules==0.2.8 pycodestyle==2.8.0 pycparser==2.21 pyctcdecode==0.3.0 pyflakes==2.4.0 Pygments==2.11.2 pygtrie==2.4.2 pynvml==11.4.1 pyOpenSSL==22.0.0 pyparsing==3.0.7 pyperclip==1.8.2 pypng==0.0.21 pyrsistent==0.18.1 pytest==7.1.1 pytest-forked==1.4.0 pytest-timeout==2.1.0 pytest-xdist==2.5.0 python-dateutil==2.8.2 python-slugify==6.1.1 pytz==2022.1 pytz-deprecation-shim==0.1.0.post0 PyYAML==6.0 ray==1.11.0 redis==4.1.4 regex==2022.3.15 requests==2.27.1 requests-oauthlib==1.3.1 resampy==0.2.2 responses==0.18.0 rfc3986==1.5.0 rouge-score==0.0.4 rsa==4.8 s3transfer==0.3.7 sacrebleu==1.5.1 sacremoses==0.0.49 scikit-learn==1.0.2 scipy==1.8.0 segments==2.2.0 sentencepiece==0.1.96 sigopt==8.2.0 six==1.16.0 smmap==5.0.0 sortedcontainers==2.4.0 SoundFile==0.10.3.post1 SQLAlchemy==1.4.32 stack-data==0.2.0 stevedore==3.5.0 tabulate==0.8.9 tenacity==8.0.1 tensorboard==2.8.0 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.1 tensorboardX==2.5 tensorflow==2.8.1 tensorflow-io-gcs-filesystem==0.24.0 termcolor==1.1.0 text-unidecode==1.3 tf-estimator-nightly==2.8.0.dev2021122109 tf2onnx==1.9.3 threadpoolctl==3.1.0 timeout-decorator==0.5.0 timm==0.5.4 tokenizers==0.11.6 tomli==2.0.1 toolz==0.11.2 torch==1.11.0 torchaudio==0.11.0 torchvision==0.12.0 tqdm==4.63.0 traitlets==5.1.1 -e git+git@github.com:edbeeching/transformers.git@77b90113ca0a0e4058b046796c874bdc98f1da61#egg=transformers typing-extensions==4.1.1 tzdata==2022.1 tzlocal==4.1 unidic==1.1.0 unidic-lite==1.0.8 uritemplate==4.1.1 urllib3==1.26.9 wasabi==0.9.0 wcwidth==0.2.5 websocket-client==1.3.1 Werkzeug==2.0.3 wrapt==1.14.0 xxhash==3.0.0 yarl==1.7.2 zipp==3.7.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/adversarial/requirements.txt

transformers == 3.5.1

transformers == 3.5.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/bert-loses-patience/requirements.txt

transformers == 3.5.1

transformers == 3.5.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/jax-projects/hybrid_clip/requirements.txt

jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8 -f https://download.pytorch.org/whl/torch_stable.html torch==1.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html torchvision==0.10.0+cpu

jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8 -f https://download.pytorch.org/whl/torch_stable.html torch==1.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html torchvision==0.10.0+cpu

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/deebert/requirements.txt

transformers == 3.5.1

transformers == 3.5.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/sagemaker/scripts/pytorch/requirements.txt

git+https://github.com/huggingface/transformers.git@main # install main or adjust it with vX.X.X for installing version specific transforms datasets==1.8.0

git+https://github.com/huggingface/transformers.git@main # install main or adjust it with vX.X.X for installing version specific transforms datasets==1.8.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/tests_samples/GermEval/train.txt

Schartau B-PER sagte O dem O " O Tagesspiegel B-ORG " O vom O Freitag O , O Fischer B-PER sei O " O in O einer O Weise O aufgetreten O , O die O alles O andere O als O überzeugend O war O " O . O Firmengründer O Wolf B-PER Peter I-PER Bree I-PER arbeitete O Anfang O der O siebziger O Jahre O als O Möbelvertreter O , O als O er O einen O fliegenden O Händler O aus O dem O Libanon B-LOC traf O . O Ob O sie O dabei O nach O dem O Runden O Tisch O am O 23. O April O in O Berlin B-LOC durch O ein O pädagogisches O Konzept O unterstützt O wird O , O ist O allerdings O zu O bezweifeln O . O Bayern B-ORG München I-ORG ist O wieder O alleiniger O Top- O Favorit O auf O den O Gewinn O der O deutschen B-LOCderiv Fußball-Meisterschaft O . O Dabei O hätte O der O tapfere O Schlussmann O allen O Grund O gehabt O , O sich O viel O früher O aufzuregen O . O ARD-Programmchef B-ORGpart Günter B-PER Struve I-PER war O wegen O eines O vierwöchigen O Urlaubs O für O eine O Stellungnahme O nicht O erreichbar O . O Alternativ O sollten O sich O die O Restaurantbetreiber O aus O Sicht O der O Solingerin B-LOCderiv zu O längeren O Öffnungszeiten O verpflichten O , O um O wartende O Kunden O aufzunehmen O . O Die O Deutsche B-ORG Flugsicherung I-ORG ( O DFS B-ORG ) O beschloss O ein O Flugverbot O für O alle O internationalen O Flughäfen O mit O Ausnahme O der O beiden O Berliner B-LOCderiv Flughäfen O bis O 2.00 O Uhr O nachts O . O New O Small O Family O mit O E-Motor O : O Studie O E-Up O ! O Eine O Schwachstelle O war O beispielsweise O der O Spiegelkasten O . O Denn O durch O den O Einsatz O moderner O Fahrzeugtechnik O ( O Dieseltriebwagen O ) O und O schalldämmender O Fenster O entsteht O keine O Einschränkung O der O Wohnqualität O . O

Schartau B-PER sagte O dem O " O Tagesspiegel B-ORG " O vom O Freitag O , O Fischer B-PER sei O " O in O einer O Weise O aufgetreten O , O die O alles O andere O als O überzeugend O war O " O . O Firmengründer O Wolf B-PER Peter I-PER Bree I-PER arbeitete O Anfang O der O siebziger O Jahre O als O Möbelvertreter O , O als O er O einen O fliegenden O Händler O aus O dem O Libanon B-LOC traf O . O Ob O sie O dabei O nach O dem O Runden O Tisch O am O 23. O April O in O Berlin B-LOC durch O ein O pädagogisches O Konzept O unterstützt O wird O , O ist O allerdings O zu O bezweifeln O . O Bayern B-ORG München I-ORG ist O wieder O alleiniger O Top- O Favorit O auf O den O Gewinn O der O deutschen B-LOCderiv Fußball-Meisterschaft O . O Dabei O hätte O der O tapfere O Schlussmann O allen O Grund O gehabt O , O sich O viel O früher O aufzuregen O . O ARD-Programmchef B-ORGpart Günter B-PER Struve I-PER war O wegen O eines O vierwöchigen O Urlaubs O für O eine O Stellungnahme O nicht O erreichbar O . O Alternativ O sollten O sich O die O Restaurantbetreiber O aus O Sicht O der O Solingerin B-LOCderiv zu O längeren O Öffnungszeiten O verpflichten O , O um O wartende O Kunden O aufzunehmen O . O Die O Deutsche B-ORG Flugsicherung I-ORG ( O DFS B-ORG ) O beschloss O ein O Flugverbot O für O alle O internationalen O Flughäfen O mit O Ausnahme O der O beiden O Berliner B-LOCderiv Flughäfen O bis O 2.00 O Uhr O nachts O . O New O Small O Family O mit O E-Motor O : O Studie O E-Up O ! O Eine O Schwachstelle O war O beispielsweise O der O Spiegelkasten O . O Denn O durch O den O Einsatz O moderner O Fahrzeugtechnik O ( O Dieseltriebwagen O ) O und O schalldämmender O Fenster O entsteht O keine O Einschränkung O der O Wohnqualität O . O

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/multiple-choice/requirements.txt

sentencepiece != 0.1.92 protobuf tensorflow >= 2.3

sentencepiece != 0.1.92 protobuf tensorflow >= 2.3

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/lxmert/requirements.txt

appdirs==1.4.3 argon2-cffi==20.1.0 async-generator==1.10 attrs==20.2.0 backcall==0.2.0 CacheControl==0.12.6 certifi==2020.6.20 cffi==1.14.2 chardet==3.0.4 click==7.1.2 colorama==0.4.3 contextlib2==0.6.0 cycler==0.10.0 datasets==1.0.0 decorator==4.4.2 defusedxml==0.6.0 dill==0.3.2 distlib==0.3.0 distro==1.4.0 entrypoints==0.3 filelock==3.0.12 future==0.18.2 html5lib==1.0.1 idna==2.8 ipaddr==2.2.0 ipykernel==5.3.4 ipython ipython-genutils==0.2.0 ipywidgets==7.5.1 jedi==0.17.2 Jinja2>=2.11.3 joblib==1.2.0 jsonschema==3.2.0 jupyter==1.0.0 jupyter-client==6.1.7 jupyter-console==6.2.0 jupyter-core==4.6.3 jupyterlab-pygments==0.1.1 kiwisolver==1.2.0 lockfile==0.12.2 MarkupSafe==1.1.1 matplotlib==3.3.1 mistune==2.0.3 msgpack==0.6.2 nbclient==0.5.0 nbconvert==6.5.1 nbformat==5.0.7 nest-asyncio==1.4.0 notebook==6.4.12 numpy==1.22.0 opencv-python==4.4.0.42 packaging==20.3 pandas==1.1.2 pandocfilters==1.4.2 parso==0.7.1 pep517==0.8.2 pexpect==4.8.0 pickleshare==0.7.5 Pillow>=8.1.1 progress==1.5 prometheus-client==0.8.0 prompt-toolkit==3.0.7 ptyprocess==0.6.0 pyaml==20.4.0 pyarrow==1.0.1 pycparser==2.20 Pygments>=2.7.4 pyparsing==2.4.6 pyrsistent==0.16.0 python-dateutil==2.8.1 pytoml==0.1.21 pytz==2020.1 PyYAML>=5.4 pyzmq==19.0.2 qtconsole==4.7.7 QtPy==1.9.0 regex==2020.7.14 requests==2.22.0 retrying==1.3.3 sacremoses==0.0.43 Send2Trash==1.5.0 sentencepiece==0.1.91 six==1.14.0 terminado==0.8.3 testpath==0.4.4 tokenizers==0.8.1rc2 torch==1.6.0 torchvision==0.7.0 tornado==6.0.4 tqdm==4.48.2 traitlets git+https://github.com/huggingface/transformers.git urllib3==1.26.5 wcwidth==0.2.5 webencodings==0.5.1 wget==3.2 widgetsnbextension==3.5.1 xxhash==2.0.0

appdirs==1.4.3 argon2-cffi==20.1.0 async-generator==1.10 attrs==20.2.0 backcall==0.2.0 CacheControl==0.12.6 certifi==2020.6.20 cffi==1.14.2 chardet==3.0.4 click==7.1.2 colorama==0.4.3 contextlib2==0.6.0 cycler==0.10.0 datasets==1.0.0 decorator==4.4.2 defusedxml==0.6.0 dill==0.3.2 distlib==0.3.0 distro==1.4.0 entrypoints==0.3 filelock==3.0.12 future==0.18.2 html5lib==1.0.1 idna==2.8 ipaddr==2.2.0 ipykernel==5.3.4 ipython ipython-genutils==0.2.0 ipywidgets==7.5.1 jedi==0.17.2 Jinja2>=2.11.3 joblib==1.2.0 jsonschema==3.2.0 jupyter==1.0.0 jupyter-client==6.1.7 jupyter-console==6.2.0 jupyter-core==4.6.3 jupyterlab-pygments==0.1.1 kiwisolver==1.2.0 lockfile==0.12.2 MarkupSafe==1.1.1 matplotlib==3.3.1 mistune==2.0.3 msgpack==0.6.2 nbclient==0.5.0 nbconvert==6.5.1 nbformat==5.0.7 nest-asyncio==1.4.0 notebook==6.4.12 numpy==1.22.0 opencv-python==4.4.0.42 packaging==20.3 pandas==1.1.2 pandocfilters==1.4.2 parso==0.7.1 pep517==0.8.2 pexpect==4.8.0 pickleshare==0.7.5 Pillow>=8.1.1 progress==1.5 prometheus-client==0.8.0 prompt-toolkit==3.0.7 ptyprocess==0.6.0 pyaml==20.4.0 pyarrow==1.0.1 pycparser==2.20 Pygments>=2.7.4 pyparsing==2.4.6 pyrsistent==0.16.0 python-dateutil==2.8.1 pytoml==0.1.21 pytz==2020.1 PyYAML>=5.4 pyzmq==19.0.2 qtconsole==4.7.7 QtPy==1.9.0 regex==2020.7.14 requests==2.22.0 retrying==1.3.3 sacremoses==0.0.43 Send2Trash==1.5.0 sentencepiece==0.1.91 six==1.14.0 terminado==0.8.3 testpath==0.4.4 tokenizers==0.8.1rc2 torch==1.6.0 torchvision==0.7.0 tornado==6.0.4 tqdm==4.48.2 traitlets git+https://github.com/huggingface/transformers.git urllib3==1.26.5 wcwidth==0.2.5 webencodings==0.5.1 wget==3.2 widgetsnbextension==3.5.1 xxhash==2.0.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/sample_text.txt

This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত Text should be one-sentence-per-line, with empty lines between documents. This sample text is public domain and was randomly selected from Project Guttenberg. The rain had only ceased with the gray streaks of morning at Blazing Star, and the settlement awoke to a moral sense of cleanliness, and the finding of forgotten knives, tin cups, and smaller camp utensils, where the heavy showers had washed away the debris and dust heaps before the cabin doors. Indeed, it was recorded in Blazing Star that a fortunate early riser had once picked up on the highway a solid chunk of gold quartz which the rain had freed from its incumbering soil, and washed into immediate and glittering popularity. Possibly this may have been the reason why early risers in that locality, during the rainy season, adopted a thoughtful habit of body, and seldom lifted their eyes to the rifted or india-ink washed skies above them. "Cass" Beard had risen early that morning, but not with a view to discovery. A leak in his cabin roof,--quite consistent with his careless, improvident habits,--had roused him at 4 A. M., with a flooded "bunk" and wet blankets. The chips from his wood pile refused to kindle a fire to dry his bed-clothes, and he had recourse to a more provident neighbor's to supply the deficiency. This was nearly opposite. Mr. Cassius crossed the highway, and stopped suddenly. Something glittered in the nearest red pool before him. Gold, surely! But, wonderful to relate, not an irregular, shapeless fragment of crude ore, fresh from Nature's crucible, but a bit of jeweler's handicraft in the form of a plain gold ring. Looking at it more attentively, he saw that it bore the inscription, "May to Cass." Like most of his fellow gold-seekers, Cass was superstitious. The fountain of classic wisdom, Hypatia herself. As the ancient sage--the name is unimportant to a monk--pumped water nightly that he might study by day, so I, the guardian of cloaks and parasols, at the sacred doors of her lecture-room, imbibe celestial knowledge. From my youth I felt in me a soul above the matter-entangled herd. She revealed to me the glorious fact, that I am a spark of Divinity itself. A fallen star, I am, sir!' continued he, pensively, stroking his lean stomach--'a fallen star!--fallen, if the dignity of philosophy will allow of the simile, among the hogs of the lower world--indeed, even into the hog-bucket itself. Well, after all, I will show you the way to the Archbishop's. There is a philosophic pleasure in opening one's treasures to the modest young. Perhaps you will assist me by carrying this basket of fruit?' And the little man jumped up, put his basket on Philammon's head, and trotted off up a neighbouring street. Philammon followed, half contemptuous, half wondering at what this philosophy might be, which could feed the self-conceit of anything so abject as his ragged little apish guide; but the novel roar and whirl of the street, the perpetual stream of busy faces, the line of curricles, palanquins, laden asses, camels, elephants, which met and passed him, and squeezed him up steps and into doorways, as they threaded their way through the great Moon-gate into the ample street beyond, drove everything from his mind but wondering curiosity, and a vague, helpless dread of that great living wilderness, more terrible than any dead wilderness of sand which he had left behind. Already he longed for the repose, the silence of the Laura--for faces which knew him and smiled upon him; but it was too late to turn back now. His guide held on for more than a mile up the great main street, crossed in the centre of the city, at right angles, by one equally magnificent, at each end of which, miles away, appeared, dim and distant over the heads of the living stream of passengers, the yellow sand-hills of the desert; while at the end of the vista in front of them gleamed the blue harbour, through a network of countless masts. At last they reached the quay at the opposite end of the street; and there burst on Philammon's astonished eyes a vast semicircle of blue sea, ringed with palaces and towers. He stopped involuntarily; and his little guide stopped also, and looked askance at the young monk, to watch the effect which that grand panorama should produce on him.

This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত Text should be one-sentence-per-line, with empty lines between documents. This sample text is public domain and was randomly selected from Project Guttenberg. The rain had only ceased with the gray streaks of morning at Blazing Star, and the settlement awoke to a moral sense of cleanliness, and the finding of forgotten knives, tin cups, and smaller camp utensils, where the heavy showers had washed away the debris and dust heaps before the cabin doors. Indeed, it was recorded in Blazing Star that a fortunate early riser had once picked up on the highway a solid chunk of gold quartz which the rain had freed from its incumbering soil, and washed into immediate and glittering popularity. Possibly this may have been the reason why early risers in that locality, during the rainy season, adopted a thoughtful habit of body, and seldom lifted their eyes to the rifted or india-ink washed skies above them. "Cass" Beard had risen early that morning, but not with a view to discovery. A leak in his cabin roof,--quite consistent with his careless, improvident habits,--had roused him at 4 A. M., with a flooded "bunk" and wet blankets. The chips from his wood pile refused to kindle a fire to dry his bed-clothes, and he had recourse to a more provident neighbor's to supply the deficiency. This was nearly opposite. Mr. Cassius crossed the highway, and stopped suddenly. Something glittered in the nearest red pool before him. Gold, surely! But, wonderful to relate, not an irregular, shapeless fragment of crude ore, fresh from Nature's crucible, but a bit of jeweler's handicraft in the form of a plain gold ring. Looking at it more attentively, he saw that it bore the inscription, "May to Cass." Like most of his fellow gold-seekers, Cass was superstitious. The fountain of classic wisdom, Hypatia herself. As the ancient sage--the name is unimportant to a monk--pumped water nightly that he might study by day, so I, the guardian of cloaks and parasols, at the sacred doors of her lecture-room, imbibe celestial knowledge. From my youth I felt in me a soul above the matter-entangled herd. She revealed to me the glorious fact, that I am a spark of Divinity itself. A fallen star, I am, sir!' continued he, pensively, stroking his lean stomach--'a fallen star!--fallen, if the dignity of philosophy will allow of the simile, among the hogs of the lower world--indeed, even into the hog-bucket itself. Well, after all, I will show you the way to the Archbishop's. There is a philosophic pleasure in opening one's treasures to the modest young. Perhaps you will assist me by carrying this basket of fruit?' And the little man jumped up, put his basket on Philammon's head, and trotted off up a neighbouring street. Philammon followed, half contemptuous, half wondering at what this philosophy might be, which could feed the self-conceit of anything so abject as his ragged little apish guide; but the novel roar and whirl of the street, the perpetual stream of busy faces, the line of curricles, palanquins, laden asses, camels, elephants, which met and passed him, and squeezed him up steps and into doorways, as they threaded their way through the great Moon-gate into the ample street beyond, drove everything from his mind but wondering curiosity, and a vague, helpless dread of that great living wilderness, more terrible than any dead wilderness of sand which he had left behind. Already he longed for the repose, the silence of the Laura--for faces which knew him and smiled upon him; but it was too late to turn back now. His guide held on for more than a mile up the great main street, crossed in the centre of the city, at right angles, by one equally magnificent, at each end of which, miles away, appeared, dim and distant over the heads of the living stream of passengers, the yellow sand-hills of the desert; while at the end of the vista in front of them gleamed the blue harbour, through a network of countless masts. At last they reached the quay at the opposite end of the street; and there burst on Philammon's astonished eyes a vast semicircle of blue sea, ringed with palaces and towers. He stopped involuntarily; and his little guide stopped also, and looked askance at the young monk, to watch the effect which that grand panorama should produce on him.

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/movement-pruning/requirements.txt

torch>=1.4.0 -e git+https://github.com/huggingface/transformers.git@352d5472b0c1dec0f420d606d16747d851b4bda8#egg=transformers knockknock>=0.1.8.1 h5py>=2.10.0 numpy>=1.18.2 scipy>=1.4.1

torch>=1.4.0 -e git+https://github.com/huggingface/transformers.git@352d5472b0c1dec0f420d606d16747d851b4bda8#egg=transformers knockknock>=0.1.8.1 h5py>=2.10.0 numpy>=1.18.2 scipy>=1.4.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/empty.txt

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/tests_samples/COCO/coco_annotations.txt

[{"segmentation": [[333.96, 175.14, 338.26, 134.33, 342.55, 95.67, 348.99, 79.57, 368.32, 80.64, 371.54, 91.38, 364.03, 106.41, 356.51, 145.07, 351.14, 166.55, 350.07, 184.8, 345.77, 185.88, 332.89, 178.36, 332.89, 172.99]], "area": 2120.991099999999, "iscrowd": 0, "image_id": 39769, "bbox": [332.89, 79.57, 38.65, 106.31], "category_id": 75, "id": 1108446}, {"segmentation": [[44.03, 86.01, 112.75, 74.2, 173.96, 77.42, 175.03, 89.23, 170.74, 98.9, 147.11, 102.12, 54.77, 119.3, 53.69, 119.3, 44.03, 113.93, 41.88, 94.6, 41.88, 94.6]], "area": 4052.607, "iscrowd": 0, "image_id": 39769, "bbox": [41.88, 74.2, 133.15, 45.1], "category_id": 75, "id": 1110067}, {"segmentation": [[1.08, 473.53, 633.17, 473.53, 557.66, 376.45, 535.01, 366.74, 489.71, 305.26, 470.29, 318.2, 456.27, 351.64, 413.12, 363.51, 376.45, 358.11, 348.4, 350.56, 363.51, 331.15, 357.03, 288.0, 353.8, 257.8, 344.09, 190.92, 333.3, 177.98, 345.17, 79.82, 284.76, 130.52, 265.35, 151.01, 308.49, 189.84, 317.12, 215.73, 293.39, 243.78, 269.66, 212.49, 235.15, 199.55, 214.65, 193.08, 187.69, 217.89, 159.64, 278.29, 135.91, 313.89, 169.35, 292.31, 203.87, 281.53, 220.04, 292.31, 220.04, 307.42, 175.82, 345.17, 155.33, 360.27, 105.71, 363.51, 85.21, 374.29, 74.43, 366.74, 70.11, 465.98, 42.07, 471.37, 33.44, 457.35, 34.52, 414.2, 29.12, 368.9, 9.71, 291.24, 46.38, 209.26, 99.24, 128.36, 131.6, 107.87, 50.7, 117.57, 40.99, 103.55, 40.99, 85.21, 60.4, 77.66, 141.3, 70.11, 173.66, 72.27, 174.74, 92.76, 204.94, 72.27, 225.44, 62.56, 262.11, 56.09, 292.31, 53.93, 282.61, 81.98, 298.79, 96.0, 310.65, 102.47, 348.4, 74.43, 373.21, 81.98, 430.38, 35.6, 484.31, 23.73, 540.4, 46.38, 593.26, 66.88, 638.56, 80.9, 632.09, 145.62, 581.39, 118.65, 543.64, 130.52, 533.93, 167.19, 512.36, 197.39, 498.34, 218.97, 529.62, 253.48, 549.03, 273.98, 584.63, 276.13, 587.87, 293.39, 566.29, 305.26, 531.78, 298.79, 549.03, 319.28, 576.0, 358.11, 560.9, 376.45, 639.64, 471.37, 639.64, 2.16, 1.08, 0.0]], "area": 176277.55269999994, "iscrowd": 0, "image_id": 39769, "bbox": [1.08, 0.0, 638.56, 473.53], "category_id": 63, "id": 1605237}, {"segmentation": [[1.07, 1.18, 640.0, 3.33, 638.93, 472.59, 4.3, 479.03]], "area": 301552.6694999999, "iscrowd": 0, "image_id": 39769, "bbox": [1.07, 1.18, 638.93, 477.85], "category_id": 65, "id": 1612051}, {"segmentation": [[138.75, 319.38, 148.75, 294.38, 165.0, 246.87, 197.5, 205.63, 247.5, 203.13, 268.75, 216.88, 280.0, 239.38, 293.75, 244.38, 303.75, 241.88, 307.5, 228.13, 318.75, 220.63, 315.0, 200.63, 291.25, 171.88, 265.0, 156.88, 258.75, 148.13, 262.5, 135.63, 282.5, 123.13, 292.5, 115.63, 311.25, 108.13, 313.75, 106.88, 296.25, 93.13, 282.5, 84.38, 292.5, 64.38, 288.75, 60.63, 266.25, 54.38, 232.5, 63.12, 206.25, 70.63, 170.0, 100.63, 136.25, 114.38, 101.25, 138.13, 56.25, 194.38, 27.5, 259.38, 17.5, 299.38, 32.5, 378.13, 31.25, 448.13, 41.25, 469.38, 66.25, 466.88, 70.0, 419.38, 71.25, 391.88, 77.5, 365.63, 113.75, 364.38, 145.0, 360.63, 168.75, 349.38, 191.25, 330.63, 212.5, 319.38, 223.75, 305.63, 206.25, 286.88, 172.5, 288.13]], "area": 53301.618749999994, "iscrowd": 0, "image_id": 39769, "bbox": [17.5, 54.38, 301.25, 415.0], "category_id": 17, "id": 2190839}, {"segmentation": [[543.75, 136.88, 570.0, 114.38, 591.25, 123.13, 616.25, 140.63, 640.0, 143.13, 636.25, 124.37, 605.0, 103.13, 640.0, 103.13, 633.75, 86.88, 587.5, 73.13, 548.75, 49.38, 505.0, 35.63, 462.5, 25.63, 405.0, 48.13, 362.5, 111.88, 347.5, 179.38, 355.0, 220.63, 356.25, 230.63, 365.0, 264.38, 358.75, 266.88, 358.75, 270.63, 356.25, 291.88, 356.25, 325.63, 355.0, 338.13, 350.0, 348.13, 365.0, 354.38, 396.25, 351.88, 423.75, 355.63, 446.25, 350.63, 460.0, 345.63, 462.5, 321.88, 468.75, 306.88, 481.25, 299.38, 516.25, 341.88, 536.25, 368.13, 570.0, 369.38, 578.75, 359.38, 555.0, 330.63, 532.5, 298.13, 563.75, 299.38, 582.5, 298.13, 586.25, 286.88, 578.75, 278.13, 548.75, 269.38, 525.0, 256.88, 505.0, 206.88, 536.25, 161.88, 540.0, 149.38]], "area": 59700.95625, "iscrowd": 0, "image_id": 39769, "bbox": [347.5, 25.63, 292.5, 343.75], "category_id": 17, "id": 2190842}]

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huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/tapex/requirements.txt

numpy datasets pandas nltk

numpy datasets pandas nltk

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/onnx/summarization/requirements.txt

torch >= 1.10

torch >= 1.10

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/benchmarking/requirements.txt

tensorflow >= 2.3

tensorflow >= 2.3

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/rag-end2end-retriever/requirements.txt

faiss-cpu >= 1.7.2 datasets psutil >= 5.9.1 torch >= 1.11.0 pytorch-lightning == 1.6.4 nvidia-ml-py3 == 7.352.0 ray >= 1.13.0

faiss-cpu >= 1.7.2 datasets psutil >= 5.9.1 torch >= 1.11.0 pytorch-lightning == 1.6.4 nvidia-ml-py3 == 7.352.0 ray >= 1.13.0