Source code for transformers.modeling_utils

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch BERT model."""


import logging
import os
import typing

import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F

from .activations import get_activation
from .configuration_utils import PretrainedConfig
from .file_utils import (
    DUMMY_INPUTS,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    WEIGHTS_NAME,
    cached_path,
    hf_bucket_url,
    is_remote_url,
)


logger = logging.getLogger(__name__)

try:
    from torch.nn import Identity
except ImportError:
    # Older PyTorch compatibility
    class Identity(nn.Module):
        r"""A placeholder identity operator that is argument-insensitive.
        """

        def __init__(self, *args, **kwargs):
            super().__init__()

        def forward(self, input):
            return input


class ModuleUtilsMixin:
    """
    A few utilities for torch.nn.Modules, to be used as a mixin.
    """

    def num_parameters(self, only_trainable: bool = False) -> int:
        """
        Get number of (optionally, trainable) parameters in the module.
        """
        params = filter(lambda x: x.requires_grad, self.parameters()) if only_trainable else self.parameters()
        return sum(p.numel() for p in params)


[docs]class PreTrainedModel(nn.Module, ModuleUtilsMixin): r""" Base class for all models. :class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads. Class attributes (overridden by derived classes): - ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture. - ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values. - ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments: - ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`, - ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`, - ``path``: a path (string) to the TensorFlow checkpoint. - ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model. """ config_class = None pretrained_model_archive_map = {} base_model_prefix = "" @property def dummy_inputs(self): """ Dummy inputs to do a forward pass in the network. Returns: torch.Tensor with dummy inputs """ return {"input_ids": torch.tensor(DUMMY_INPUTS)} def __init__(self, config, *inputs, **kwargs): super().__init__() if not isinstance(config, PretrainedConfig): raise ValueError( "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. " "To create a model from a pretrained model use " "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( self.__class__.__name__, self.__class__.__name__ ) ) # Save config in model self.config = config @property def base_model(self): return getattr(self, self.base_model_prefix, self)
[docs] def get_input_embeddings(self): """ Returns the model's input embeddings. Returns: :obj:`nn.Module`: A torch module mapping vocabulary to hidden states. """ base_model = getattr(self, self.base_model_prefix, self) if base_model is not self: return base_model.get_input_embeddings() else: raise NotImplementedError
[docs] def set_input_embeddings(self, value): """ Set model's input embeddings Args: value (:obj:`nn.Module`): A module mapping vocabulary to hidden states. """ base_model = getattr(self, self.base_model_prefix, self) if base_model is not self: base_model.set_input_embeddings(value) else: raise NotImplementedError
[docs] def get_output_embeddings(self): """ Returns the model's output embeddings. Returns: :obj:`nn.Module`: A torch module mapping hidden states to vocabulary. """ return None # Overwrite for models with output embeddings
[docs] def tie_weights(self): """ Tie the weights between the input embeddings and the output embeddings. If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the weights instead. """ output_embeddings = self.get_output_embeddings() if output_embeddings is not None: self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
def _tie_or_clone_weights(self, output_embeddings, input_embeddings): """ Tie or clone module weights depending of weither we are using TorchScript or not """ if self.config.torchscript: output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone()) else: output_embeddings.weight = input_embeddings.weight if hasattr(output_embeddings, "bias") and output_embeddings.bias is not None: output_embeddings.bias.data = torch.nn.functional.pad( output_embeddings.bias.data, (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0]), "constant", 0, ) if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"): output_embeddings.out_features = input_embeddings.num_embeddings
[docs] def resize_token_embeddings(self, new_num_tokens=None): """ Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size. Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method. Arguments: new_num_tokens: (`optional`) int: New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model. Return: ``torch.nn.Embeddings`` Pointer to the input tokens Embeddings Module of the model """ base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed model_embeds = base_model._resize_token_embeddings(new_num_tokens) if new_num_tokens is None: return model_embeds # Update base model and current model config self.config.vocab_size = new_num_tokens base_model.vocab_size = new_num_tokens # Tie weights again if needed self.tie_weights() return model_embeds
def _resize_token_embeddings(self, new_num_tokens): old_embeddings = self.get_input_embeddings() new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens) self.set_input_embeddings(new_embeddings) return self.get_input_embeddings() def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None): """ Build a resized Embedding Module from a provided token Embedding Module. Increasing the size will add newly initialized vectors at the end Reducing the size will remove vectors from the end Args: new_num_tokens: (`optional`) int New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end Reducing the size will remove vectors from the end If not provided or None: return the provided token Embedding Module. Return: ``torch.nn.Embeddings`` Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None """ if new_num_tokens is None: return old_embeddings old_num_tokens, old_embedding_dim = old_embeddings.weight.size() if old_num_tokens == new_num_tokens: return old_embeddings # Build new embeddings new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim) new_embeddings.to(old_embeddings.weight.device) # initialize all new embeddings (in particular added tokens) self._init_weights(new_embeddings) # Copy word embeddings from the previous weights num_tokens_to_copy = min(old_num_tokens, new_num_tokens) new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :] return new_embeddings
[docs] def init_weights(self): """ Initialize and prunes weights if needed. """ # Initialize weights self.apply(self._init_weights) # Prune heads if needed if self.config.pruned_heads: self.prune_heads(self.config.pruned_heads) # Tie weights if needed self.tie_weights()
[docs] def prune_heads(self, heads_to_prune): """ Prunes heads of the base model. Arguments: heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`). E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2. """ # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads for layer, heads in heads_to_prune.items(): union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads) self.config.pruned_heads[layer] = list(union_heads) # Unfortunately we have to store it as list for JSON self.base_model._prune_heads(heads_to_prune)
[docs] def save_pretrained(self, save_directory): """ Save a model and its configuration file to a directory, so that it can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method. """ assert os.path.isdir( save_directory ), "Saving path should be a directory where the model and configuration can be saved" # Only save the model itself if we are using distributed training model_to_save = self.module if hasattr(self, "module") else self # Attach architecture to the config model_to_save.config.architectures = [model_to_save.__class__.__name__] # Save configuration file model_to_save.config.save_pretrained(save_directory) # If we save using the predefined names, we can load using `from_pretrained` output_model_file = os.path.join(save_directory, WEIGHTS_NAME) torch.save(model_to_save.state_dict(), output_model_file) logger.info("Model weights saved in {}".format(output_model_file))
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): r"""Instantiate a pretrained pytorch model from a pre-trained model configuration. The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated) To train the model, you should first set it back in training mode with ``model.train()`` The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task. The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded. Parameters: pretrained_model_name_or_path: either: - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``. - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``. - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``) model_args: (`optional`) Sequence of positional arguments: All remaning positional arguments will be passed to the underlying model's ``__init__`` method config: (`optional`) one of: - an instance of a class derived from :class:`~transformers.PretrainedConfig`, or - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()` Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when: - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory. - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory. state_dict: (`optional`) dict: an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file. This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option. cache_dir: (`optional`) string: Path to a directory in which a downloaded pre-trained model configuration should be cached if the standard cache should not be used. force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. resume_download: (`optional`) boolean, default False: Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. output_loading_info: (`optional`) boolean: Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages. kwargs: (`optional`) Remaining dictionary of keyword arguments: Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded: - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function. Examples:: # For example purposes. Not runnable. model = BertModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = BertModel.from_pretrained('./test/saved_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = BertModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading assert model.config.output_attention == True # Loading from a TF checkpoint file instead of a PyTorch model (slower) config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json') model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ config = kwargs.pop("config", None) state_dict = kwargs.pop("state_dict", None) cache_dir = kwargs.pop("cache_dir", None) from_tf = kwargs.pop("from_tf", False) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) output_loading_info = kwargs.pop("output_loading_info", False) # Load config if we don't provide a configuration if not isinstance(config, PretrainedConfig): config_path = config if config is not None else pretrained_model_name_or_path config, model_kwargs = cls.config_class.from_pretrained( config_path, *model_args, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, resume_download=resume_download, proxies=proxies, **kwargs, ) else: model_kwargs = kwargs # Load model if pretrained_model_name_or_path is not None: if pretrained_model_name_or_path in cls.pretrained_model_archive_map: archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path] elif os.path.isdir(pretrained_model_name_or_path): if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")): # Load from a TF 1.0 checkpoint archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index") elif from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)): # Load from a TF 2.0 checkpoint archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME) elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)): # Load from a PyTorch checkpoint archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME) else: raise EnvironmentError( "Error no file named {} found in directory {} or `from_tf` set to False".format( [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"], pretrained_model_name_or_path ) ) elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): archive_file = pretrained_model_name_or_path elif os.path.isfile(pretrained_model_name_or_path + ".index"): assert ( from_tf ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format( pretrained_model_name_or_path + ".index" ) archive_file = pretrained_model_name_or_path + ".index" else: archive_file = hf_bucket_url( pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME) ) # redirect to the cache, if necessary try: resolved_archive_file = cached_path( archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, ) except EnvironmentError: if pretrained_model_name_or_path in cls.pretrained_model_archive_map: msg = "Couldn't reach server at '{}' to download pretrained weights.".format(archive_file) else: msg = ( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url to model weight files named one of {} but " "couldn't find any such file at this path or url.".format( pretrained_model_name_or_path, ", ".join(cls.pretrained_model_archive_map.keys()), archive_file, [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME], ) ) raise EnvironmentError(msg) if resolved_archive_file == archive_file: logger.info("loading weights file {}".format(archive_file)) else: logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file)) else: resolved_archive_file = None # Instantiate model. model = cls(config, *model_args, **model_kwargs) if state_dict is None and not from_tf: try: state_dict = torch.load(resolved_archive_file, map_location="cpu") except Exception: raise OSError( "Unable to load weights from pytorch checkpoint file. " "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. " ) missing_keys = [] unexpected_keys = [] error_msgs = [] if from_tf: if resolved_archive_file.endswith(".index"): # Load from a TensorFlow 1.X checkpoint - provided by original authors model = cls.load_tf_weights(model, config, resolved_archive_file[:-6]) # Remove the '.index' else: # Load from our TensorFlow 2.0 checkpoints try: from transformers import load_tf2_checkpoint_in_pytorch_model model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True) except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see " "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions." ) raise else: # Convert old format to new format if needed from a PyTorch state_dict old_keys = [] new_keys = [] for key in state_dict.keys(): new_key = None if "gamma" in key: new_key = key.replace("gamma", "weight") if "beta" in key: new_key = key.replace("beta", "bias") if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): state_dict[new_key] = state_dict.pop(old_key) # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, "_metadata", None) state_dict = state_dict.copy() if metadata is not None: state_dict._metadata = metadata # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants # so we need to apply the function recursively. def load(module: nn.Module, prefix=""): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs ) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + ".") # Make sure we are able to load base models as well as derived models (with heads) start_prefix = "" model_to_load = model if not hasattr(model, cls.base_model_prefix) and any( s.startswith(cls.base_model_prefix) for s in state_dict.keys() ): start_prefix = cls.base_model_prefix + "." if hasattr(model, cls.base_model_prefix) and not any( s.startswith(cls.base_model_prefix) for s in state_dict.keys() ): model_to_load = getattr(model, cls.base_model_prefix) load(model_to_load, prefix=start_prefix) if len(missing_keys) > 0: logger.info( "Weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, missing_keys ) ) if len(unexpected_keys) > 0: logger.info( "Weights from pretrained model not used in {}: {}".format( model.__class__.__name__, unexpected_keys ) ) if len(error_msgs) > 0: raise RuntimeError( "Error(s) in loading state_dict for {}:\n\t{}".format( model.__class__.__name__, "\n\t".join(error_msgs) ) ) model.tie_weights() # make sure word embedding weights are still tied if needed # Set model in evaluation mode to desactivate DropOut modules by default model.eval() if output_loading_info: loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs} return model, loading_info return model
def prepare_inputs_for_generation(self, input_ids, **kwargs): return {"input_ids": input_ids} def _do_output_past(self, outputs): has_output_past = hasattr(self.config, "output_past") and self.config.output_past has_mem_len = hasattr(self.config, "mem_len") and self.config.mem_len if has_output_past and not has_mem_len and len(outputs) > 1: return True elif has_mem_len and self.config.mem_len > 0 and len(outputs) > 1: return True return False
[docs] @torch.no_grad() def generate( self, input_ids=None, max_length=None, do_sample=True, num_beams=None, temperature=None, top_k=None, top_p=None, repetition_penalty=None, bos_token_id=None, pad_token_id=None, eos_token_ids=None, length_penalty=None, num_return_sequences=None, ): r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling and beam-search. Adapted in part from `Facebook's XLM beam search code`_. .. _`Facebook's XLM beam search code`: https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529 Parameters: input_ids: (`optional`) `torch.LongTensor` of shape `(batch_size, sequence_length)` The sequence used as a prompt for the generation. If `None` the method initializes it as an empty `torch.LongTensor` of shape `(1,)`. max_length: (`optional`) int The max length of the sequence to be generated. Between 1 and infinity. Default to 20. do_sample: (`optional`) bool If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `True`. num_beams: (`optional`) int Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1. temperature: (`optional`) float The value used to module the next token probabilities. Must be strictely positive. Default to 1.0. top_k: (`optional`) int The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50. top_p: (`optional`) float The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1. repetition_penalty: (`optional`) float The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0. bos_token_id: (`optional`) int Beginning of sentence token if no prompt is provided. Default to 0. eos_token_ids: (`optional`) int or list of int End of sequence token or list of tokens to stop the generation. Default to 0. length_penalty: (`optional`) float Exponential penalty to the length. Default to 1. num_return_sequences: (`optional`) int The number of independently computed returned sequences for each element in the batch. Default to 1. Examples:: tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache. outputs = model.generate(max_length=40, bos_token_id=tokenizer.bos_token_id, eos_token_ids=tokenizer.eos_token_id) # do greedy decoding without beam search print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) tokenizer = AutoTokenizer.from_pretrained('openai-gpt') # Initialize tokenizer model = AutoModelWithLMHead.from_pretrained('openai-gpt') # Download model and configuration from S3 and cache. input_context = 'The dog' input_ids = torch.tensor(tokenizer.encode(input_context)).unsqueeze(0) # encode input context outputs = model.generate(input_ids=input_ids, do_sample=True, num_beams=5, num_return_sequences=3, temperature=1.5) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog' for i in range(3): # 3 output sequences were generated print('Generated {}: {}'.format(i, tokenizer.decode(outputs[0][i], skip_special_tokens=True))) tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache. input_context = 'The dog' input_ids = torch.tensor(tokenizer.encode(input_context)).unsqueeze(0) # encode input context outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, bos_token_id=tokenizer.bos_token_id, eos_token_ids=tokenizer.eos_token_id, num_beams=3) # generate sequences using greedy beam search decoding (3 beams) print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) tokenizer = AutoTokenizer.from_pretrained('ctrl') # Initialize tokenizer model = AutoModelWithLMHead.from_pretrained('ctrl') # Download model and configuration from S3 and cache. input_context = 'Legal My neighbor is' # "Legal" is one of the control codes for ctrl input_ids = torch.tensor(tokenizer.encode(input_context)).unsqueeze(0) # encode input context outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2) # generate sequences using using greedy search print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) """ # We cannot generate if the model does not have a LM head if self.get_output_embeddings() is None: raise AttributeError( "You tried to generate sequences with a model that does not have a LM Head." "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`)" ) max_length = max_length if max_length is not None else self.config.max_length do_sample = do_sample if do_sample is not None else self.config.do_sample num_beams = num_beams if num_beams is not None else self.config.num_beams temperature = temperature if temperature is not None else self.config.temperature top_k = top_k if top_k is not None else self.config.top_k top_p = top_p if top_p is not None else self.config.top_p repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_ids = eos_token_ids if eos_token_ids is not None else self.config.eos_token_ids length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty num_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) if input_ids is not None: batch_size = input_ids.shape[0] # overriden by the input batch_size else: batch_size = 1 if isinstance(eos_token_ids, int): eos_token_ids = [eos_token_ids] assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictely positive integer." assert isinstance(do_sample, bool), "`do_sample` should be a boolean." assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictely positive integer." assert temperature > 0, "`temperature` should be strictely positive." assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer." assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1." assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1." assert isinstance(bos_token_id, int) and bos_token_id >= 0, "`bos_token_id` should be a positive integer." assert isinstance(pad_token_id, int) and pad_token_id >= 0, "`pad_token_id` should be a positive integer." assert isinstance(eos_token_ids, (list, tuple)) and ( e >= 0 for e in eos_token_ids ), "`eos_token_ids` should be a positive integer or a list/tuple of positive integers." assert length_penalty > 0, "`length_penalty` should be strictely positive." assert ( isinstance(num_return_sequences, int) and num_return_sequences > 0 ), "`num_return_sequences` should be a strictely positive integer." if input_ids is None: input_ids = torch.full( (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device ) else: assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)." # current position and vocab size cur_len = input_ids.shape[1] vocab_size = self.config.vocab_size if num_return_sequences != 1: # Expand input to num return sequences input_ids = input_ids.unsqueeze(1).expand(batch_size, num_return_sequences, cur_len) input_ids = input_ids.contiguous().view( batch_size * num_return_sequences, cur_len ) # (batch_size * num_return_sequences, cur_len) effective_batch_size = batch_size * num_return_sequences else: effective_batch_size = batch_size if num_beams > 1: output = self._generate_beam_search( input_ids, cur_len, max_length, do_sample, temperature, top_k, top_p, repetition_penalty, pad_token_id, eos_token_ids, effective_batch_size, length_penalty, num_beams, vocab_size, ) else: output = self._generate_no_beam_search( input_ids, cur_len, max_length, do_sample, temperature, top_k, top_p, repetition_penalty, pad_token_id, eos_token_ids, effective_batch_size, ) if num_return_sequences != 1: output = output.view(batch_size, num_return_sequences, -1) return output
def _generate_no_beam_search( self, input_ids, cur_len, max_length, do_sample, temperature, top_k, top_p, repetition_penalty, pad_token_id, eos_token_ids, batch_size, ): """ Generate sequences for each example without beam search (num_beams == 1). All returned sequence are generated independantly. """ # current position / max lengths / length of generated sentences / unfinished sentences unfinished_sents = input_ids.new(batch_size).fill_(1) past = None while cur_len < max_length: model_inputs = self.prepare_inputs_for_generation(input_ids, past=past) outputs = self(**model_inputs) next_token_logits = outputs[0][:, -1, :] # if model has past, then set the past variable to speed up decoding if self._do_output_past(outputs): past = outputs[1] # repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858) if repetition_penalty != 1.0: for i in range(batch_size): for previous_token in set(input_ids[i].tolist()): # if score < 0 then repetition penalty has to multiplied to reduce the previous token probability if next_token_logits[i, previous_token] < 0: next_token_logits[i, previous_token] *= repetition_penalty else: next_token_logits[i, previous_token] /= repetition_penalty if do_sample: # Temperature (higher temperature => more likely to sample low probability tokens) if temperature != 1.0: next_token_logits = next_token_logits / temperature # Top-p/top-k filtering next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p) # Sample next_token = torch.multinomial(F.softmax(next_token_logits, dim=-1), num_samples=1).squeeze(1) else: # Greedy decoding next_token = torch.argmax(next_token_logits, dim=-1) # update generations and finished sentences tokens_to_add = next_token * unfinished_sents + pad_token_id * (1 - unfinished_sents) input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1) for eos_token_id in eos_token_ids: unfinished_sents.mul_(tokens_to_add.ne(eos_token_id).long()) cur_len = cur_len + 1 # stop when there is a </s> in each sentence, or if we exceed the maximul length if unfinished_sents.max() == 0: break # add eos_token_ids to unfinished sentences if cur_len == max_length: input_ids[:, -1].masked_fill_(unfinished_sents.to(dtype=torch.bool), eos_token_ids[0]) return input_ids def _generate_beam_search( self, input_ids, cur_len, max_length, do_sample, temperature, top_k, top_p, repetition_penalty, pad_token_id, eos_token_ids, batch_size, length_penalty, num_beams, vocab_size, ): """ Generate sequences for each example with beam search. """ # Expand input to num beams input_ids = input_ids.unsqueeze(1).expand(batch_size, num_beams, cur_len) input_ids = input_ids.contiguous().view(batch_size * num_beams, cur_len) # (batch_size * num_beams, cur_len) # generated hypotheses generated_hyps = [ BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=False) for _ in range(batch_size) ] # scores for each sentence in the beam beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device) beam_scores[:, 1:] = -1e9 beam_scores = beam_scores.view(-1) # shape (batch_size * num_beams,) # cache compute states past = None # done sentences done = [False for _ in range(batch_size)] while cur_len < max_length: model_inputs = self.prepare_inputs_for_generation(input_ids, past=past) outputs = self(**model_inputs) # (batch_size * num_beams, cur_len, vocab_size) scores = outputs[0][:, -1, :] # (batch_size * num_beams, vocab_size) # if model has past, then set the past variable to speed up decoding if self._do_output_past(outputs): past = outputs[1] # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858) if repetition_penalty != 1.0: for i in range(batch_size * num_beams): for previous_token in set(input_ids[i].tolist()): # if score < 0 then repetition penalty has to multiplied to reduce the previous token probability if scores[i, previous_token] < 0: scores[i, previous_token] *= repetition_penalty else: scores[i, previous_token] /= repetition_penalty if do_sample: # Temperature (higher temperature => more likely to sample low probability tokens) if temperature != 1.0: scores = scores / temperature # Top-p/top-k filtering scores = top_k_top_p_filtering( scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2 ) # (batch_size * num_beams, vocab_size) # Sample 2 next words for each beam (so we have some spare tokens and match output of greedy beam search) next_words = torch.multinomial(F.softmax(scores, dim=-1), num_samples=2) # (batch_size * num_beams, 2) # Compute next scores _scores = F.log_softmax(scores, dim=-1) # (batch_size * num_beams, vocab_size) _scores = torch.gather(_scores, -1, next_words) # (batch_size * num_beams, 2) next_scores = _scores + beam_scores[:, None].expand_as(_scores) # (batch_size * num_beams, 2) # Match shape of greedy beam search next_words = next_words.view(batch_size, 2 * num_beams) # (batch_size, 2 * num_beams) next_scores = next_scores.view(batch_size, 2 * num_beams) # (batch_size, 2 * num_beams) else: # do greedy beam search scores = F.log_softmax(scores, dim=-1) # (batch_size * num_beams, vocab_size) assert scores.size() == (batch_size * num_beams, vocab_size) # Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product) _scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size) # re-organize to group the beam together (we are keeping top hypothesis accross beams) _scores = _scores.view(batch_size, num_beams * vocab_size) # (batch_size, num_beams * vocab_size) next_scores, next_words = torch.topk(_scores, 2 * num_beams, dim=1, largest=True, sorted=True) assert next_scores.size() == next_words.size() == (batch_size, 2 * num_beams) # next batch beam content # list of (batch_size * num_beams) tuple(next hypothesis score, next word, current position in the batch) next_batch_beam = [] # for each sentence for batch_ex in range(batch_size): # if we are done with this sentence done[batch_ex] = done[batch_ex] or generated_hyps[batch_ex].is_done(next_scores[batch_ex].max().item()) if done[batch_ex]: next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch continue # next sentence beam content next_sent_beam = [] # next words for this sentence for idx, score in zip(next_words[batch_ex], next_scores[batch_ex]): # get beam and word IDs beam_id = idx // vocab_size word_id = idx % vocab_size # end of sentence, or next word if word_id.item() in eos_token_ids or cur_len + 1 == max_length: generated_hyps[batch_ex].add( input_ids[batch_ex * num_beams + beam_id, :cur_len].clone(), score.item() ) else: next_sent_beam.append((score, word_id, batch_ex * num_beams + beam_id)) # the beam for next step is full if len(next_sent_beam) == num_beams: break # update next beam content assert len(next_sent_beam) == 0 if cur_len + 1 == max_length else num_beams if len(next_sent_beam) == 0: next_sent_beam = [(0, pad_token_id, 0)] * num_beams # pad the batch next_batch_beam.extend(next_sent_beam) assert len(next_batch_beam) == num_beams * (batch_ex + 1) # sanity check / prepare next batch assert len(next_batch_beam) == batch_size * num_beams beam_scores = beam_scores.new([x[0] for x in next_batch_beam]) beam_words = input_ids.new([x[1] for x in next_batch_beam]) beam_idx = input_ids.new([x[2] for x in next_batch_beam]) # re-order batch input_ids = input_ids[beam_idx, :] input_ids = torch.cat([input_ids, beam_words.unsqueeze(1)], dim=-1) # re-order internal states if past: reordered_past = [] for layer_past in past: # get the correct batch idx from layer past batch dim # batch dim of `past` and `mems` is at 2nd position reordered_layer_past = [layer_past[:, i].unsqueeze(1).clone().detach() for i in beam_idx] reordered_layer_past = torch.cat(reordered_layer_past, dim=1) # check that shape matches assert reordered_layer_past.shape == layer_past.shape reordered_past.append(reordered_layer_past) past = tuple(reordered_past) # update current length cur_len = cur_len + 1 # stop when we are done with each sentence if all(done): break # visualize hypotheses # print([len(x) for x in generated_hyps], cur_len) # globals().update( locals() ); # !import code; code.interact(local=vars()) # for ii in range(batch_size): # for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True): # print("%.3f " % ss + " ".join(self.dico[x] for x in ww.tolist())) # print("") # select the best hypotheses tgt_len = input_ids.new(batch_size) best = [] for i, hypotheses in enumerate(generated_hyps): best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1] tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol best.append(best_hyp) # generate target batch decoded = input_ids.new(batch_size, tgt_len.max().item()).fill_(pad_token_id) for i, hypo in enumerate(best): decoded[i, : tgt_len[i] - 1] = hypo decoded[i, tgt_len[i] - 1] = eos_token_ids[0] return decoded
def top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (batch size, vocabulary size) if top_k > 0: keep only top k tokens with highest probability (top-k filtering). if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) Make sure we keep at least min_tokens_to_keep per batch example in the output From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 """ if top_k > 0: top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold (token with 0 are kept) sorted_indices_to_remove = cumulative_probs > top_p if min_tokens_to_keep > 1: # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below) sorted_indices_to_remove[..., :min_tokens_to_keep] = 0 # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 # scatter sorted tensors to original indexing indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) logits[indices_to_remove] = filter_value return logits class BeamHypotheses(object): def __init__(self, n_hyp, max_length, length_penalty, early_stopping): """ Initialize n-best list of hypotheses. """ self.max_length = max_length - 1 # ignoring bos_token self.length_penalty = length_penalty self.early_stopping = early_stopping self.n_hyp = n_hyp self.hyp = [] self.worst_score = 1e9 def __len__(self): """ Number of hypotheses in the list. """ return len(self.hyp) def add(self, hyp, sum_logprobs): """ Add a new hypothesis to the list. """ score = sum_logprobs / len(hyp) ** self.length_penalty if len(self) < self.n_hyp or score > self.worst_score: self.hyp.append((score, hyp)) if len(self) > self.n_hyp: sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.hyp)]) del self.hyp[sorted_scores[0][1]] self.worst_score = sorted_scores[1][0] else: self.worst_score = min(score, self.worst_score) def is_done(self, best_sum_logprobs): """ 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 len(self) < self.n_hyp: return False elif self.early_stopping: return True else: return self.worst_score >= best_sum_logprobs / self.max_length ** self.length_penalty class Conv1D(nn.Module): def __init__(self, nf, nx): """ Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2) Basically works like a Linear layer but the weights are transposed """ super().__init__() self.nf = nf w = torch.empty(nx, nf) nn.init.normal_(w, std=0.02) self.weight = nn.Parameter(w) self.bias = nn.Parameter(torch.zeros(nf)) def forward(self, x): size_out = x.size()[:-1] + (self.nf,) x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight) x = x.view(*size_out) return x class PoolerStartLogits(nn.Module): """ Compute SQuAD start_logits from sequence hidden states. """ def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, 1) def forward(self, hidden_states, p_mask=None): """ Args: **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)` invalid position mask such as query and special symbols (PAD, SEP, CLS) 1.0 means token should be masked. """ x = self.dense(hidden_states).squeeze(-1) if p_mask is not None: if next(self.parameters()).dtype == torch.float16: x = x * (1 - p_mask) - 65500 * p_mask else: x = x * (1 - p_mask) - 1e30 * p_mask return x class PoolerEndLogits(nn.Module): """ Compute SQuAD end_logits from sequence hidden states and start token hidden state. """ def __init__(self, config): super().__init__() self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size) self.activation = nn.Tanh() self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dense_1 = nn.Linear(config.hidden_size, 1) def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None): """ Args: One of ``start_states``, ``start_positions`` should be not None. If both are set, ``start_positions`` overrides ``start_states``. **start_states**: ``torch.LongTensor`` of shape identical to hidden_states hidden states of the first tokens for the labeled span. **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)`` position of the first token for the labeled span: **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)`` Mask of invalid position such as query and special symbols (PAD, SEP, CLS) 1.0 means token should be masked. """ assert ( start_states is not None or start_positions is not None ), "One of start_states, start_positions should be not None" if start_positions is not None: slen, hsz = hidden_states.shape[-2:] start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz) start_states = hidden_states.gather(-2, start_positions) # shape (bsz, 1, hsz) start_states = start_states.expand(-1, slen, -1) # shape (bsz, slen, hsz) x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1)) x = self.activation(x) x = self.LayerNorm(x) x = self.dense_1(x).squeeze(-1) if p_mask is not None: if next(self.parameters()).dtype == torch.float16: x = x * (1 - p_mask) - 65500 * p_mask else: x = x * (1 - p_mask) - 1e30 * p_mask return x class PoolerAnswerClass(nn.Module): """ Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """ def __init__(self, config): super().__init__() self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size) self.activation = nn.Tanh() self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False) def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None): """ Args: One of ``start_states``, ``start_positions`` should be not None. If both are set, ``start_positions`` overrides ``start_states``. **start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``. hidden states of the first tokens for the labeled span. **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)`` position of the first token for the labeled span. **cls_index**: torch.LongTensor of shape ``(batch_size,)`` position of the CLS token. If None, take the last token. note(Original repo): no dependency on end_feature so that we can obtain one single `cls_logits` for each sample """ hsz = hidden_states.shape[-1] assert ( start_states is not None or start_positions is not None ), "One of start_states, start_positions should be not None" if start_positions is not None: start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz) start_states = hidden_states.gather(-2, start_positions).squeeze(-2) # shape (bsz, hsz) if cls_index is not None: cls_index = cls_index[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz) cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, hsz) else: cls_token_state = hidden_states[:, -1, :] # shape (bsz, hsz) x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1)) x = self.activation(x) x = self.dense_1(x).squeeze(-1) return x class SQuADHead(nn.Module): r""" A SQuAD head inspired by XLNet. Parameters: config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model. Inputs: **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)`` hidden states of sequence tokens **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)`` position of the first token for the labeled span. **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)`` position of the last token for the labeled span. **cls_index**: torch.LongTensor of shape ``(batch_size,)`` position of the CLS token. If None, take the last token. **is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)`` Whether the question has a possible answer in the paragraph or not. **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)`` Mask of invalid position such as query and special symbols (PAD, SEP, CLS) 1.0 means token should be masked. Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``: Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses. **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)`` Log probabilities for the top config.start_n_top start token possibilities (beam-search). **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)`` Indices for the top config.start_n_top start token possibilities (beam-search). **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)`` Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search). **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)`` Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search). **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) ``torch.FloatTensor`` of shape ``(batch_size,)`` Log probabilities for the ``is_impossible`` label of the answers. """ def __init__(self, config): super().__init__() self.start_n_top = config.start_n_top self.end_n_top = config.end_n_top self.start_logits = PoolerStartLogits(config) self.end_logits = PoolerEndLogits(config) self.answer_class = PoolerAnswerClass(config) def forward( self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None ): outputs = () start_logits = self.start_logits(hidden_states, p_mask=p_mask) if start_positions is not None and end_positions is not None: # If we are on multi-GPU, let's remove the dimension added by batch splitting for x in (start_positions, end_positions, cls_index, is_impossible): if x is not None and x.dim() > 1: x.squeeze_(-1) # during training, compute the end logits based on the ground truth of the start position end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask) loss_fct = CrossEntropyLoss() start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if cls_index is not None and is_impossible is not None: # Predict answerability from the representation of CLS and START cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index) loss_fct_cls = nn.BCEWithLogitsLoss() cls_loss = loss_fct_cls(cls_logits, is_impossible) # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss total_loss += cls_loss * 0.5 outputs = (total_loss,) + outputs else: # during inference, compute the end logits based on beam search bsz, slen, hsz = hidden_states.size() start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen) start_top_log_probs, start_top_index = torch.topk( start_log_probs, self.start_n_top, dim=-1 ) # shape (bsz, start_n_top) start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz) start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz) start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz) hidden_states_expanded = hidden_states.unsqueeze(2).expand_as( start_states ) # shape (bsz, slen, start_n_top, hsz) p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask) end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top) end_top_log_probs, end_top_index = torch.topk( end_log_probs, self.end_n_top, dim=1 ) # shape (bsz, end_n_top, start_n_top) end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top) end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top) start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs) cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index) outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits # or (if labels are provided) (total_loss,) return outputs class SequenceSummary(nn.Module): r""" Compute a single vector summary of a sequence hidden states according to various possibilities: Args of the config class: summary_type: - 'last' => [default] take the last token hidden state (like XLNet) - 'first' => take the first token hidden state (like Bert) - 'mean' => take the mean of all tokens hidden states - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2) - 'attn' => Not implemented now, use multi-head attention summary_use_proj: Add a projection after the vector extraction summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. summary_activation: 'tanh' or another string => add an activation to the output, Other => no activation. Default summary_first_dropout: Add a dropout before the projection and activation summary_last_dropout: Add a dropout after the projection and activation """ def __init__(self, config: PretrainedConfig): super().__init__() self.summary_type = getattr(config, "summary_type", "last") if self.summary_type == "attn": # We should use a standard multi-head attention module with absolute positional embedding for that. # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 # We can probably just use the multi-head attention module of PyTorch >=1.1.0 raise NotImplementedError self.summary = Identity() if hasattr(config, "summary_use_proj") and config.summary_use_proj: if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0: num_classes = config.num_labels else: num_classes = config.hidden_size self.summary = nn.Linear(config.hidden_size, num_classes) activation_string = getattr(config, "summary_activation", None) self.activation = ( get_activation(activation_string) if activation_string else Identity() ) # type: typing.Callable self.first_dropout = Identity() if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0: self.first_dropout = nn.Dropout(config.summary_first_dropout) self.last_dropout = Identity() if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0: self.last_dropout = nn.Dropout(config.summary_last_dropout) def forward(self, hidden_states, cls_index=None): """ hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer. cls_index: [optional] position of the classification token if summary_type == 'cls_index', shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states. if summary_type == 'cls_index' and cls_index is None: we take the last token of the sequence as classification token """ if self.summary_type == "last": output = hidden_states[:, -1] elif self.summary_type == "first": output = hidden_states[:, 0] elif self.summary_type == "mean": output = hidden_states.mean(dim=1) elif self.summary_type == "cls_index": if cls_index is None: cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long) else: cls_index = cls_index.unsqueeze(-1).unsqueeze(-1) cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),)) # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size) elif self.summary_type == "attn": raise NotImplementedError output = self.first_dropout(output) output = self.summary(output) output = self.activation(output) output = self.last_dropout(output) return output def prune_linear_layer(layer, index, dim=0): """ Prune a linear layer (a model parameters) to keep only entries in index. Return the pruned layer as a new layer with requires_grad=True. Used to remove heads. """ index = index.to(layer.weight.device) W = layer.weight.index_select(dim, index).clone().detach() if layer.bias is not None: if dim == 1: b = layer.bias.clone().detach() else: b = layer.bias[index].clone().detach() new_size = list(layer.weight.size()) new_size[dim] = len(index) new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device) new_layer.weight.requires_grad = False new_layer.weight.copy_(W.contiguous()) new_layer.weight.requires_grad = True if layer.bias is not None: new_layer.bias.requires_grad = False new_layer.bias.copy_(b.contiguous()) new_layer.bias.requires_grad = True return new_layer def prune_conv1d_layer(layer, index, dim=1): """ Prune a Conv1D layer (a model parameters) to keep only entries in index. A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed. Return the pruned layer as a new layer with requires_grad=True. Used to remove heads. """ index = index.to(layer.weight.device) W = layer.weight.index_select(dim, index).clone().detach() if dim == 0: b = layer.bias.clone().detach() else: b = layer.bias[index].clone().detach() new_size = list(layer.weight.size()) new_size[dim] = len(index) new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device) new_layer.weight.requires_grad = False new_layer.weight.copy_(W.contiguous()) new_layer.weight.requires_grad = True new_layer.bias.requires_grad = False new_layer.bias.copy_(b.contiguous()) new_layer.bias.requires_grad = True return new_layer def prune_layer(layer, index, dim=None): """ Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index. Return the pruned layer as a new layer with requires_grad=True. Used to remove heads. """ if isinstance(layer, nn.Linear): return prune_linear_layer(layer, index, dim=0 if dim is None else dim) elif isinstance(layer, Conv1D): return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim) else: raise ValueError("Can't prune layer of class {}".format(layer.__class__))