codegen2-1B_P / configuration_codegen.py

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	# coding=utf-8
	# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. 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.
	""" CodeGen model configuration"""
	from collections import OrderedDict
	from typing import Any, List, Mapping, Optional

	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__)


	class CodeGenConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a
	CodeGen 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 CodeGen
	[Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) 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 50400):
	Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`CodeGenModel`].
	n_positions (`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).
	n_embd (`int`, optional, defaults to 4096):
	Dimensionality of the embeddings and hidden states.
	n_layer (`int`, optional, defaults to 28):
	Number of hidden layers in the Transformer encoder.
	n_head (`int`, optional, defaults to 16):
	Number of attention heads for each attention layer in the Transformer encoder.
	rotary_dim (`int`, optional, defaults to 64):
	Number of dimensions in the embedding that Rotary Position Embedding is applied to.
	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_new"`):
	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.
	scale_attn_weights (`bool`, optional, defaults to `True`):
	Scale attention weights by dividing by sqrt(hidden_size).
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models).

	Example:

	```python
	>>> from transformers import CodeGenModel, CodeGenConfig

	>>> # Initializing a CodeGen 6B configuration
	>>> configuration = CodeGenConfig()

	>>> # Initializing a model from the configuration
	>>> model = CodeGenModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "codegen"
	attribute_map = {
	"max_position_embeddings": "n_positions",
	"hidden_size": "n_embd",
	"num_attention_heads": "n_head",
	"num_hidden_layers": "n_layer",
	}

	def __init__(
	self,
	vocab_size=50400,
	n_positions=2048,
	n_ctx=2048,
	n_embd=4096,
	n_layer=28,
	n_head=16,
	rotary_dim=64,
	n_inner=None,
	activation_function="gelu_new",
	resid_pdrop=0.0,
	embd_pdrop=0.0,
	attn_pdrop=0.0,
	layer_norm_epsilon=1e-5,
	initializer_range=0.02,
	scale_attn_weights=True,
	use_cache=True,
	bos_token_id=50256,
	eos_token_id=50256,
	tie_word_embeddings=False,
	**kwargs
	):
	self.vocab_size = vocab_size
	self.n_ctx = n_ctx
	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.rotary_dim = rotary_dim
	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.scale_attn_weights = scale_attn_weights
	self.use_cache = use_cache

	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, tie_word_embeddings=tie_word_embeddings, **kwargs
	)


	# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
	class CodeGenOnnxConfig(OnnxConfigWithPast):
	def __init__(
	self,
	config: PretrainedConfig,
	task: str = "default",
	patching_specs: List[PatchingSpec] = None,
	use_past: bool = False,
	):
	super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
	if not getattr(self._config, "pad_token_id", None):
	# TODO: how to do that better?
	self._config.pad_token_id = 0

	@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_layers(self) -> int:
	return self._config.n_layer

	@property
	def num_attention_heads(self) -> int:
	return self._config.n_head

	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