230 lines
9.4 KiB
Python
230 lines
9.4 KiB
Python
# coding=utf-8
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# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. 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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""" CodeGen model 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 ... import PreTrainedTokenizer, TensorType, is_torch_available
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfigWithPast, PatchingSpec
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class CodeGenConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a
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CodeGen model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the CodeGen
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[Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) architecture. Configuration objects
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inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
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[`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50400):
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Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`CodeGenModel`].
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n_positions (`int`, *optional*, defaults to 2048):
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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_ctx (`int`, *optional*, defaults to 2048):
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This attribute is used in `CodeGenModel.__init__` without any real effect.
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n_embd (`int`, *optional*, defaults to 4096):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 28):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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rotary_dim (`int`, *optional*, defaults to 64):
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Number of dimensions in the embedding that Rotary Position Embedding is applied to.
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n_inner (`int`, *optional*):
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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_new"`):
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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 0.0):
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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 0.0):
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The dropout ratio for the embeddings.
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attn_pdrop (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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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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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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bos_token_id (`int`, *optional*, defaults to 50256):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 50256):
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End of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
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model has a output word embedding layer.
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Example:
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```python
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>>> from transformers import CodeGenConfig, CodeGenModel
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>>> # Initializing a CodeGen 6B configuration
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>>> configuration = CodeGenConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = CodeGenModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "codegen"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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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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def __init__(
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self,
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vocab_size=50400,
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n_positions=2048,
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n_ctx=2048,
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n_embd=4096,
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n_layer=28,
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n_head=16,
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rotary_dim=64,
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n_inner=None,
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activation_function="gelu_new",
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attn_pdrop=0.0,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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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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tie_word_embeddings=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.n_ctx = n_ctx
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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.rotary_dim = rotary_dim
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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.use_cache = use_cache
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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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super().__init__(
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bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
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)
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# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
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class CodeGenOnnxConfig(OnnxConfigWithPast):
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def __init__(
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self,
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config: PretrainedConfig,
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task: str = "default",
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patching_specs: List[PatchingSpec] = None,
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use_past: bool = False,
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):
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super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
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if not getattr(self._config, "pad_token_id", None):
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# TODO: how to do that better?
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self._config.pad_token_id = 0
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
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if self.use_past:
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self.fill_with_past_key_values_(common_inputs, direction="inputs")
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common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
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else:
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common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
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return common_inputs
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@property
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def num_layers(self) -> int:
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return self._config.n_layer
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@property
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def num_attention_heads(self) -> int:
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return self._config.n_head
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def generate_dummy_inputs(
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self,
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tokenizer: PreTrainedTokenizer,
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batch_size: int = -1,
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seq_length: int = -1,
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is_pair: bool = False,
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framework: Optional[TensorType] = None,
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) -> Mapping[str, Any]:
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common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
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tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
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)
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# We need to order the input in the way they appears in the forward()
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ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
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# Need to add the past_keys
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if self.use_past:
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if not is_torch_available():
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raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
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else:
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import torch
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batch, seqlen = common_inputs["input_ids"].shape
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# Not using the same length for past_key_values
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past_key_values_length = seqlen + 2
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past_shape = (
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batch,
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self.num_attention_heads,
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past_key_values_length,
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self._config.hidden_size // self.num_attention_heads,
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)
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ordered_inputs["past_key_values"] = [
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(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
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]
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ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
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if self.use_past:
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mask_dtype = ordered_inputs["attention_mask"].dtype
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ordered_inputs["attention_mask"] = torch.cat(
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[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
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)
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return ordered_inputs
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@property
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def default_onnx_opset(self) -> int:
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return 13
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