Source code for transformers.models.gpt_neo.configuration_gpt_neo

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""" GPT Neo model configuration """

from collections import OrderedDict
from typing import Any, Dict, Iterable, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging

logger = logging.get_logger(__name__)

    "EleutherAI/gpt-neo-1.3B": "",
    # See all GPTNeo models at

[docs]class GPTNeoConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.GPTNeoModel`. It is used to instantiate a GPT Neo 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 GPTNeo `gpt-neo-1.3B <>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 50257): Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.GPTNeoModel`. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.GPTNeoModel`. attention_types (:obj:`List`, `optional`, defaults to :obj:`[[["global", "local"], 12]]`): The type of attention for each layer in a :obj:`List` of the following format :obj:`[[["attention_type"], num_layerss]]` e.g. for a 24 layer model :obj:`[[["global"], 24]]` or :obj:`[[["global", "local"], 12]]` Choose the value of ``attention_type`` from :obj:`["global", "local"]` hidden_size (:obj:`int`, `optional`, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. num_layers (:obj:`int`, `optional`, defaults to 24): Number of hidden layers in the Transformer encoder. num_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 8192): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. embed_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. max_position_embeddings (:obj:`int`, `optional`, defaults to 2048): 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 (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.GPTNeoModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5): The epsilon used by the layer normalization layers. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if ``config.is_decoder=True``. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. Example:: >>> from transformers import GPTNeoModel, GPTNeoConfig >>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration >>> configuration = GPTNeoConfig() >>> # Initializing a model from the EleutherAI/gpt-neo-1.3B style configuration >>> model = GPTNeoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "gpt_neo" def __init__( self, vocab_size=50257, max_position_embeddings=2048, hidden_size=2048, num_layers=24, attention_types=[[["global", "local"], 12]], num_heads=16, intermediate_size=None, window_size=256, activation_function="gelu_new", resid_dropout=0.0, embed_dropout=0.0, attention_dropout=0.0, 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, gradient_checkpointing=False, use_cache=True, bos_token_id=50256, eos_token_id=50256, **kwargs ): super().__init__(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_layers = num_layers self.num_heads = num_heads self.intermediate_size = intermediate_size self.window_size = window_size self.activation_function = activation_function self.resid_dropout = resid_dropout self.embed_dropout = embed_dropout self.attention_dropout = attention_dropout self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels self.gradient_checkpointing = gradient_checkpointing self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.attention_types = attention_types self.attention_layers = self.expand_attention_types_params(attention_types) if len(self.attention_layers) != self.num_layers: raise ValueError( "Configuration for convolutional module is incorrect." "It is required that `len(config.attention_layers)` == `config.num_layers`" f"but is `len(config.attention_layers) = {len(self.attention_layers)}`," f"`config.num_layers = {self.num_layers}`." "`config.attention_layers` is prepared using `config.attention_types`." "Please verify the value of `config.attention_types` argument." ) @staticmethod def expand_attention_types_params(attention_types): attentions = [] for item in attention_types: for _ in range(item[1]): attentions.extend(item[0]) return attentions @property def num_attention_heads(self): return self.num_heads @property def num_hidden_layers(self): return self.num_layers
def custom_unfold(input, dimension, size, step): """Custom torch.Tensor.unfold implementation to enable the export to ONNX.""" import torch shape = input.size() rank = len(shape) sizedim = shape[dimension] low_indices = torch.arange(0, sizedim, step) min_length = torch.div(sizedim - size, step, rounding_mode="floor") + 1 indices = torch.arange(size) + low_indices[:min_length][:, None] s = [slice(None)] * rank s[dimension] = indices sliced = input[s] perm = list(range(0, rank + 1)) perm.append(perm.pop(dimension + 1)) return sliced.permute(perm) def custom_get_block_length_and_num_blocks(seq_length, window_size): """ Custom implementation for GPTNeoAttentionMixin._get_block_length_and_num_blocks to enable the export to ONNX as original implementation uses Python variables and control flow. """ import torch candidates = torch.arange(1, window_size) remainders = torch.remainder(seq_length, candidates) divisor_indices = remainders == 0 divisors = candidates[divisor_indices] largest_divisor = torch.max(divisors) return largest_divisor, torch.div(seq_length, largest_divisor, rounding_mode="floor") class GPTNeoOnnxConfig(OnnxConfigWithPast): def __init__(self, config: PretrainedConfig, task: str = "default", use_past: bool = False): if is_torch_available(): import torch from .modeling_gpt_neo import GPTNeoAttentionMixin patching_specs = [ PatchingSpec(torch.Tensor, name="unfold", custom_op=custom_unfold), PatchingSpec( GPTNeoAttentionMixin, name="_get_block_length_and_num_blocks", custom_op=custom_get_block_length_and_num_blocks, op_wrapper=staticmethod, ), ] super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past) self._num_local_attention = len([type_ for type_ in self._config.attention_layers if type_ == "local"]) self._key_values_dynamic_axis = [] for i in range(self._config.num_layers): if self._config.attention_layers[i] == "local": self._key_values_dynamic_axis.append({0: "batch", 1: "sequence"}) else: self._key_values_dynamic_axis.append({0: "batch", 2: "sequence"}) self._key_values_dynamic_axis.append({0: "batch", 2: "sequence"}) @property def _number_key_values(self): return (self._config.num_layers * 2) - self._num_local_attention @property def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}) if self.use_past: for i in range(self._config.num_layers): if self._config.attention_layers[i] == "local": common_inputs[f"past_key_values.{i}.key_value"] = {0: "batch", 1: "sequence"} else: common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "sequence"} common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "sequence"} common_inputs["attention_mask"] = {0: "batch", 1: "sequence"} return common_inputs @property def outputs(self) -> Mapping[str, Mapping[int, str]]: common_outputs = super().outputs if self.use_past: for i in range(self._config.num_layers): if self._config.attention_layers[i] == "local": common_outputs[f"present.{i}.key_value"] = {0: "batch", 1: "sequence"} else: common_outputs[f"present.{i}.key"] = {0: "batch", 2: "sequence"} common_outputs[f"present.{i}.value"] = {0: "batch", 2: "sequence"} return common_outputs def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: common_inputs = super().generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework) # We need to order the input in the way they appears in the forward() ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]}) batch = common_inputs["input_ids"].shape[0] past_shapes = { "global": (batch, self._config.num_heads, 1, self._config.hidden_size // self._config.num_attention_heads), "local": (batch, 1, self._config.hidden_size), } # Need to add the past_keys if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch ordered_inputs["past_key_values"] = [] for i in range(self._config.num_layers): attention_type = self._config.attention_layers[i] if attention_type == "global": ordered_inputs["past_key_values"].append( ( torch.zeros(past_shapes[attention_type]), torch.zeros(past_shapes[attention_type]), ) ) else: ordered_inputs["past_key_values"].append((torch.zeros(past_shapes[attention_type]),)) ordered_inputs["attention_mask"] = common_inputs["attention_mask"] if self.use_past: ordered_inputs["attention_mask"] = [ordered_inputs["attention_mask"], torch.zeros(batch, 1)], dim=1 ) return ordered_inputs @staticmethod def flatten_output_collection_property(name: str, field: Iterable[Any]) -> Dict[str, Any]: if name in ["present", "past_key_values"]: flatten_output = {} for idx, t in enumerate(field): if len(t) == 1: flatten_output[f"{name}.{idx}.key_value"] = t[0] else: flatten_output[f"{name}.{idx}.key"] = t[0] flatten_output[f"{name}.{idx}.value"] = t[1] return flatten_output return super().flatten_output_collection_property(name, field)