# 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. """GPT Neo model configuration""" from collections import OrderedDict from typing import Any, Mapping, Optional from ... import PreTrainedTokenizer, TensorType, is_torch_available from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfigWithPast from ...utils import logging logger = logging.get_logger(__name__) class GPTNeoConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`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 [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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 50257): Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GPTNeoModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`GPTNeoModel`]. max_position_embeddings (`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). hidden_size (`int`, *optional*, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. num_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. attention_types (`List`, *optional*, defaults to `[[['global', 'local'], 12]]`): The type of attention for each layer in a `List` of the following format `[[["attention_type"], num_layerss]]` e.g. for a 24 layer model `[[["global"], 24]]` or `[[["global", "local"], 12]]` Choose the value of `attention_type` from `["global", "local"]` num_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 8192): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. window_size (`int`, *optional*, defaults to 256): The size of the sliding window for local attention. activation_function (`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. resid_dropout (`float`, *optional*, defaults to 0.0): Residual dropout used in the attention pattern. embed_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. classifier_dropout (`float`, *optional*, defaults to 0.1): Argument used when doing token classification, used in the model [`GPTNeoForTokenClassification`]. The dropout ratio for the hidden layer. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `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`. bos_token_id (`int`, *optional*, defaults to 50256): The id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 50256): The id of the end of sentence token in the vocabulary. Example: ```python >>> from transformers import GPTNeoConfig, GPTNeoModel >>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration >>> configuration = GPTNeoConfig() >>> # Initializing a model (with random weights) from the EleutherAI/gpt-neo-1.3B style configuration >>> model = GPTNeoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "gpt_neo" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} 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, classifier_dropout=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, use_cache=True, bos_token_id=50256, eos_token_id=50256, **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.classifier_dropout = classifier_dropout self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range 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." ) super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @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 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): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}) if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"} else: common_inputs["attention_mask"] = {0: "batch", 1: "sequence"} return common_inputs @property def num_attention_heads(self) -> int: return self._config.num_heads 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(OnnxConfigWithPast, self).generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) # We need to order the input in the way they appears in the forward() ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]}) # 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 batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 past_shape = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) ordered_inputs["past_key_values"] = [ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers) ] ordered_inputs["attention_mask"] = common_inputs["attention_mask"] if self.use_past: mask_dtype = ordered_inputs["attention_mask"].dtype ordered_inputs["attention_mask"] = torch.cat( [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1 ) return ordered_inputs @property def default_onnx_opset(self) -> int: return 13 __all__ = ["GPTNeoConfig", "GPTNeoOnnxConfig"]