aragpt2-mega / configuration_aragpt2.py

Update run_pretraining.py and configuration_aragpt2.py

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	# coding=utf-8
	""" AraGPT2 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__)

	AraGPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"aubmindlab/aragpt2-mega": "https://huggingface.co/aubmindlab/aragpt2-mega/resolve/main/config.json",
	}


	class AraGPT2Config(PretrainedConfig):
	"""
	This is the configuration class to store the configuration of a [`AraGPT2Model`] or a [`TFAraGPT2Model`]. It is used to
	instantiate a AraGPT2 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 AraGPT2
	[aubmindlab/aragpt2-mega](https://huggingface.co/aubmindlab/aragpt2-mega) 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 64000):
	Vocabulary size of the AraGPT2 model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`AraGPT2Model`] or [`TFAraGPT2Model`].
	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):
	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 (`float`, 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-05):
	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/AraGPT2).
	- `"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(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).
	bos_token_id (`int`, optional, defaults to 50256):
	Id of the beginning of sentence token in the vocabulary.
	eos_token_id (`int`, optional, defaults to 50256):
	Id of the end of sentence token in the vocabulary.
	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 AraGPT2Config, AraGPT2Model

	>>> # Initializing a AraGPT2 configuration
	>>> configuration = AraGPT2Config()

	>>> # Initializing a model (with random weights) from the configuration
	>>> model = AraGPT2Model(configuration)

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

	model_type = "aragpt2"
	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=64000,
	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=0,
	eos_token_id=0,
	scale_attn_by_inverse_layer_idx=False,
	reorder_and_upcast_attn=False,
	**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.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)


	class AraGPT2OnnxConfig(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