143 lines
6.5 KiB
Python
143 lines
6.5 KiB
Python
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# coding=utf-8
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# Copyright 2022 The Metaseq Authors and The HuggingFace Inc. team. 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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""" OPT model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class OPTConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT model
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according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the OPT
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[facebook/opt-350m](https://huggingface.co/facebook/opt-350m) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50272):
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Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`OPTModel`]
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of decoder layers.
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ffn_dim (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer decoder.
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activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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max_position_embeddings (`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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do_layer_norm_before (`bool`, *optional*, defaults to `True`):
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Whether to perform layer normalization before the attention block.
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word_embed_proj_dim (`int`, *optional*):
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`word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to
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`hidden_size`.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
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details.
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init_std (`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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enable_bias (`bool`, *optional*, defaults to `True`):
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Whether or not if the linear layers in the attention blocks should use the bias term.
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layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
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Whether or not if the layer norms should have learnable parameters.
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Example:
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```python
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>>> from transformers import OPTConfig, OPTModel
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>>> # Initializing a OPT facebook/opt-large style configuration
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>>> configuration = OPTConfig()
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>>> # Initializing a model (with random weights) from the facebook/opt-large style configuration
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>>> model = OPTModel(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 = "opt"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=50272,
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hidden_size=768,
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num_hidden_layers=12,
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ffn_dim=3072,
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max_position_embeddings=2048,
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do_layer_norm_before=True,
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_remove_final_layer_norm=False,
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word_embed_proj_dim=None,
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dropout=0.1,
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attention_dropout=0.0,
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num_attention_heads=12,
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activation_function="relu",
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layerdrop=0.0,
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init_std=0.02,
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use_cache=True,
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pad_token_id=1,
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bos_token_id=2,
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eos_token_id=2,
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enable_bias=True,
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layer_norm_elementwise_affine=True,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.num_attention_heads = num_attention_heads
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self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
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self.ffn_dim = ffn_dim
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_function = activation_function
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self.init_std = init_std
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self.layerdrop = layerdrop
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self.use_cache = use_cache
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self.do_layer_norm_before = do_layer_norm_before
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# We keep these variables at `True` for backward compatibility.
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self.enable_bias = enable_bias
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self.layer_norm_elementwise_affine = layer_norm_elementwise_affine
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# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
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# with checkpoints that have been fine-tuned before transformers v4.20.1
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# see https://github.com/facebookresearch/metaseq/pull/164
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self._remove_final_layer_norm = _remove_final_layer_norm
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