santacoder / configuration_gpt2_mq.py

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	# coding=utf-8
	# Copyright 2018 The OpenAI Team Authors and Hugging Face Inc. team.
	# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" Custom GPT-2 configuration"""
	from collections import OrderedDict
	from typing import Any, List, Mapping, Optional
	from enum import Enum

	from transformers import PreTrainedTokenizer, TensorType, is_torch_available

	from transformers.configuration_utils import PretrainedConfig
	from transformers.onnx import OnnxConfigWithPast, PatchingSpec
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
	"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
	"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
	"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
	"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
	}

	MULTI_HEAD = "multihead"
	MULTI_QUERY = "multiquery"


	class GPT2CustomConfig(PretrainedConfig):
	"""
	This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
	instantiate a GPT-2 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 GPT-2
	[gpt2](https://huggingface.co/gpt2) 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-2 model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].
	n_positions (`int`, optional, defaults to 1024):
	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).
	n_embd (`int`, optional, defaults to 768):
	Dimensionality of the embeddings and hidden states.
	n_layer (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	n_head (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer encoder.
	n_inner (`int`, optional, defaults to None):
	Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
	activation_function (`str`, optional, defaults to `"gelu"`):
	Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
	resid_pdrop (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	embd_pdrop (`int`, optional, defaults to 0.1):
	The dropout ratio for the embeddings.
	attn_pdrop (`float`, optional, defaults to 0.1):
	The dropout ratio for the attention.
	layer_norm_epsilon (`float`, optional, defaults to 1e-5):
	The epsilon to use in 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.
	summary_type (`string`, optional, defaults to `"cls_index"`):
	Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
	[`TFGPT2DoubleHeadsModel`].

	Has to be one of the following options:

	- `"last"`: 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 (like GPT/GPT-2).
	- `"attn"`: Not implemented now, use multi-head attention.
	summary_use_proj (`bool`, optional, defaults to `True`):
	Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
	[`TFGPT2DoubleHeadsModel`].

	Whether or not to add a projection after the vector extraction.
	summary_activation (`str`, optional):
	Argument used when doing sequence summary. Used in for the multiple choice head in
	[`GPT2DoubleHeadsModel`].

	Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
	summary_proj_to_labels (`bool`, optional, defaults to `True`):
	Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
	[`TFGPT2DoubleHeadsModel`].

	Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
	summary_first_dropout (`float`, optional, defaults to 0.1):
	Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
	[`TFGPT2DoubleHeadsModel`].

	The dropout ratio to be used after the projection and activation.
	scale_attn_weights (`bool`, optional, defaults to `True`):
	Scale attention weights by dividing by sqrt(head_dim)..
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models).
	scale_attn_by_inverse_layer_idx (`bool`, optional, defaults to `False`):
	Whether to additionally scale attention weights by `1 / layer_idx + 1`.
	reorder_and_upcast_attn (`bool`, optional, defaults to `False`):
	Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
	dot-product/softmax to float() when training with mixed precision.

	Example:

	```python
	>>> from transformers import GPT2Config, GPT2Model

	>>> # Initializing a GPT2 configuration
	>>> configuration = GPT2Config()

	>>> # Initializing a model (with random weights) from the configuration
	>>> model = GPT2Model(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""

	model_type = "gpt2"
	keys_to_ignore_at_inference = ["past_key_values"]
	attribute_map = {
	"hidden_size": "n_embd",
	"max_position_embeddings": "n_positions",
	"num_attention_heads": "n_head",
	"num_hidden_layers": "n_layer",
	}

	def __init__(
	self,
	vocab_size=50257,
	n_positions=1024,
	n_embd=768,
	n_layer=12,
	n_head=12,
	n_inner=None,
	activation_function="gelu_new",
	resid_pdrop=0.1,
	embd_pdrop=0.1,
	attn_pdrop=0.1,
	layer_norm_epsilon=1e-5,
	initializer_range=0.02,
	summary_type="cls_index",
	summary_use_proj=True,
	summary_activation=None,
	summary_proj_to_labels=True,
	summary_first_dropout=0.1,
	scale_attn_weights=True,
	use_cache=True,
	bos_token_id=50256,
	eos_token_id=50256,
	scale_attn_by_inverse_layer_idx=False,
	reorder_and_upcast_attn=False,
	attention_head_type=MULTI_HEAD,
	**kwargs,
	):
	self.vocab_size = vocab_size
	self.n_positions = n_positions
	self.n_embd = n_embd
	self.n_layer = n_layer
	self.n_head = n_head
	self.n_inner = n_inner
	self.activation_function = activation_function
	self.resid_pdrop = resid_pdrop
	self.embd_pdrop = embd_pdrop
	self.attn_pdrop = attn_pdrop
	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.scale_attn_weights = scale_attn_weights
	self.use_cache = use_cache
	self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
	self.reorder_and_upcast_attn = reorder_and_upcast_attn
	self.attention_head_type = attention_head_type
	# assert attention_head_type in [AttentionType.MULTI_HEAD, AttentionType.MULTI_QUERY]
	assert attention_head_type in [MULTI_HEAD, MULTI_QUERY]

	self.bos_token_id = bos_token_id
	self.eos_token_id = eos_token_id

	super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)