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Create configuration_gpt2_mq.py

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+ # coding=utf-8
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+ # Copyright 2018 The OpenAI Team Authors and Hugging Face Inc. team.
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+ # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ Custom GPT-2 configuration"""
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+ from collections import OrderedDict
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+ from typing import Any, List, Mapping, Optional
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+ from enum import Enum
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+
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+ from transformers import PreTrainedTokenizer, TensorType, is_torch_available
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.onnx import OnnxConfigWithPast, PatchingSpec
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+ GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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+ "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
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+ "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
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+ "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
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+ "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
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+ "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
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+ }
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+
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+ MULTI_HEAD = "multihead"
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+ MULTI_QUERY = "multiquery"
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+
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+
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+ class GPT2CustomConfig(PretrainedConfig):
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+ """
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+ This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
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+ instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
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+ configuration with the defaults will yield a similar configuration to that of the GPT-2
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+ [gpt2](https://huggingface.co/gpt2) architecture.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 50257):
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+ Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].
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+ n_positions (`int`, *optional*, defaults to 1024):
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+ The maximum sequence length that this model might ever be used with. Typically set this to something large
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+ just in case (e.g., 512 or 1024 or 2048).
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+ n_embd (`int`, *optional*, defaults to 768):
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+ Dimensionality of the embeddings and hidden states.
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+ n_layer (`int`, *optional*, defaults to 12):
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+ Number of hidden layers in the Transformer encoder.
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+ n_head (`int`, *optional*, defaults to 12):
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+ Number of attention heads for each attention layer in the Transformer encoder.
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+ n_inner (`int`, *optional*, defaults to None):
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+ Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
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+ activation_function (`str`, *optional*, defaults to `"gelu"`):
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+ Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
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+ resid_pdrop (`float`, *optional*, defaults to 0.1):
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+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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+ embd_pdrop (`int`, *optional*, defaults to 0.1):
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+ The dropout ratio for the embeddings.
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+ attn_pdrop (`float`, *optional*, defaults to 0.1):
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+ The dropout ratio for the attention.
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+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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+ The epsilon to use in the layer normalization layers.
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+ initializer_range (`float`, *optional*, defaults to 0.02):
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+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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+ summary_type (`string`, *optional*, defaults to `"cls_index"`):
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+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
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+ [`TFGPT2DoubleHeadsModel`].
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+
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+ Has to be one of the following options:
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+
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+ - `"last"`: Take the last token hidden state (like XLNet).
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+ - `"first"`: Take the first token hidden state (like BERT).
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+ - `"mean"`: Take the mean of all tokens hidden states.
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+ - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
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+ - `"attn"`: Not implemented now, use multi-head attention.
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+ summary_use_proj (`bool`, *optional*, defaults to `True`):
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+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
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+ [`TFGPT2DoubleHeadsModel`].
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+
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+ Whether or not to add a projection after the vector extraction.
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+ summary_activation (`str`, *optional*):
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+ Argument used when doing sequence summary. Used in for the multiple choice head in
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+ [`GPT2DoubleHeadsModel`].
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+
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+ Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
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+ summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
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+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
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+ [`TFGPT2DoubleHeadsModel`].
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+
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+ Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
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+ summary_first_dropout (`float`, *optional*, defaults to 0.1):
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+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
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+ [`TFGPT2DoubleHeadsModel`].
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+
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+ The dropout ratio to be used after the projection and activation.
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+ scale_attn_weights (`bool`, *optional*, defaults to `True`):
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+ Scale attention weights by dividing by sqrt(head_dim)..
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+ use_cache (`bool`, *optional*, defaults to `True`):
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+ Whether or not the model should return the last key/values attentions (not used by all models).
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+ scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
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+ Whether to additionally scale attention weights by `1 / layer_idx + 1`.
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+ reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
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+ Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
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+ dot-product/softmax to float() when training with mixed precision.
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+
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+ Example:
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+
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+ ```python
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+ >>> from transformers import GPT2Config, GPT2Model
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+
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+ >>> # Initializing a GPT2 configuration
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+ >>> configuration = GPT2Config()
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+
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+ >>> # Initializing a model (with random weights) from the configuration
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+ >>> model = GPT2Model(configuration)
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+
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+ >>> # Accessing the model configuration
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+ >>> configuration = model.config
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+ ```"""
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+
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+ model_type = "gpt2"
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+ attribute_map = {
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+ "hidden_size": "n_embd",
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+ "max_position_embeddings": "n_positions",
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+ "num_attention_heads": "n_head",
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+ "num_hidden_layers": "n_layer",
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+ }
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+
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+ def __init__(
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+ self,
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+ vocab_size=50257,
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+ n_positions=1024,
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+ n_embd=768,
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+ n_layer=12,
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+ n_head=12,
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+ n_inner=None,
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+ activation_function="gelu_new",
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+ resid_pdrop=0.1,
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+ embd_pdrop=0.1,
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+ attn_pdrop=0.1,
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+ layer_norm_epsilon=1e-5,
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+ initializer_range=0.02,
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+ summary_type="cls_index",
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+ summary_use_proj=True,
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+ summary_activation=None,
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+ summary_proj_to_labels=True,
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+ summary_first_dropout=0.1,
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+ scale_attn_weights=True,
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+ use_cache=True,
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+ bos_token_id=50256,
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+ eos_token_id=50256,
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+ scale_attn_by_inverse_layer_idx=False,
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+ reorder_and_upcast_attn=False,
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+ attention_head_type=MULTI_HEAD,
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+ **kwargs,
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+ ):
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+ self.vocab_size = vocab_size
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+ self.n_positions = n_positions
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+ self.n_embd = n_embd
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+ self.n_layer = n_layer
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+ self.n_head = n_head
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+ self.n_inner = n_inner
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+ self.activation_function = activation_function
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+ self.resid_pdrop = resid_pdrop
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+ self.embd_pdrop = embd_pdrop
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+ self.attn_pdrop = attn_pdrop
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+ self.layer_norm_epsilon = layer_norm_epsilon
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+ self.initializer_range = initializer_range
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+ self.summary_type = summary_type
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+ self.summary_use_proj = summary_use_proj
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+ self.summary_activation = summary_activation
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+ self.summary_first_dropout = summary_first_dropout
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+ self.summary_proj_to_labels = summary_proj_to_labels
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+ self.scale_attn_weights = scale_attn_weights
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+ self.use_cache = use_cache
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+ self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
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+ self.reorder_and_upcast_attn = reorder_and_upcast_attn
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+ self.attention_head_type = attention_head_type
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+ # assert attention_head_type in [AttentionType.MULTI_HEAD, AttentionType.MULTI_QUERY]
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+ assert attention_head_type in [MULTI_HEAD, MULTI_QUERY]
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+
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+ self.bos_token_id = bos_token_id
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+ self.eos_token_id = eos_token_id
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+
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+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)