wehub-resource-sync
121 lines
5.1 KiB
Python
121 lines
5.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. 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 __future__ import annotations
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from paddlenlp.transformers.configuration_utils import PretrainedConfig
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__all__ = ["CODEGEN_PRETRAINED_INIT_CONFIGURATION", "CodeGenConfig", "CODEGEN_PRETRAINED_RESOURCE_FILES_MAP"]
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CODEGEN_PRETRAINED_INIT_CONFIGURATION = {}
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CODEGEN_PRETRAINED_RESOURCE_FILES_MAP = {"model_state": {}}
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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-350M-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):
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Vocabulary size of `inputs_ids` in `CodeGenModel`. Also is the vocab size of token embedding matrix.
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Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `CodeGenModel`.
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Defaulta to `50400`.
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n_embed (int, optional):
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Dimensionality of the embedding layer, decoder layer. Defaults to `4096`.
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n_layer (int, optional):
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Number of hidden layers. Defaults to `28`.
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n_head (int, optional):
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Number of attention heads for each attention layer in the Transformer decoder.
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Defaults to `16`.
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n_ctx (int, optional):
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Dimensionality of the causal mask (usually same as n_positions).
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Defaults to `2048`.
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n_positions (int, optional):
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The maximum sequence length that this model might ever be used with.
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Defaults to `2048`.
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attn_pdrop (float, optional):
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The dropout probability used in MultiHeadAttention in all decoder layers to drop some attention target.
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Defaults to `0.0`.
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resid_pdrop (float, optional):
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The dropout probability for all residual layers in the decoder.
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Defaults to `0.0`.
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embd_pdrop (float, optional):
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The dropout probability used in embedding layers. Defaults to `0.0`.
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rotary_dim (int, optional):
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Dimensionality of rotay position embeddings.
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Defaults to `64`.
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activation_function (str, optional):
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The non-linear activation function in the feed-forward layer.
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``"gelu"``, ``"relu"`` and any other paddle supported activation functions are supported.
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Defaults to `"gelu_new"`.
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layer_norm_epsilon (float, optional):
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The epsilon to use in the layer normalization layers.
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Defaults to `1e-05`.
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initializer_range (float, optional):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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Default to `0.02`.
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```"""
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model_type = "codegen"
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pretrained_init_configuration = CODEGEN_PRETRAINED_INIT_CONFIGURATION
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def __init__(
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self,
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vocab_size: int = 50400,
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bos_token_id: int = 1,
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eos_token_id: int = 50256,
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pad_token_id: int = 50256,
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n_embd: int = 4096,
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n_layer: int = 28,
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n_head: int = 16,
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n_ctx: int = 2048,
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n_positions: int = 2048,
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attn_pdrop: float = 0.0,
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resid_pdrop: float = 0.0,
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embd_pdrop: float = 0.0,
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rotary_dim: int = 64,
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activation_function: str = "gelu_new",
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layer_norm_epsilon: float = 1e-05,
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initializer_range: float = 0.02,
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n_inner: int = None,
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tie_word_embeddings: bool = False,
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**kwargs,
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):
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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pad_token_id=pad_token_id,
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tie_word_embeddings=tie_word_embeddings,
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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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