167 lines
7.2 KiB
Python
167 lines
7.2 KiB
Python
# coding=utf-8
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# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
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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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""" SqueezeBERT model configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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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 SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class SqueezeBertConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a
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SqueezeBERT model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the SqueezeBERT
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[squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) 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 30522):
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Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`SqueezeBertModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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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 encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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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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hidden_dropout_prob (`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_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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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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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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pad_token_id (`int`, *optional*, defaults to 0):
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The ID of the token in the word embedding to use as padding.
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embedding_size (`int`, *optional*, defaults to 768):
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The dimension of the word embedding vectors.
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q_groups (`int`, *optional*, defaults to 4):
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The number of groups in Q layer.
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k_groups (`int`, *optional*, defaults to 4):
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The number of groups in K layer.
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v_groups (`int`, *optional*, defaults to 4):
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The number of groups in V layer.
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post_attention_groups (`int`, *optional*, defaults to 1):
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The number of groups in the first feed forward network layer.
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intermediate_groups (`int`, *optional*, defaults to 4):
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The number of groups in the second feed forward network layer.
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output_groups (`int`, *optional*, defaults to 4):
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The number of groups in the third feed forward network layer.
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Examples:
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```python
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>>> from transformers import SqueezeBertConfig, SqueezeBertModel
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>>> # Initializing a SqueezeBERT configuration
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>>> configuration = SqueezeBertConfig()
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>>> # Initializing a model (with random weights) from the configuration above
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>>> model = SqueezeBertModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "squeezebert"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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embedding_size=768,
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q_groups=4,
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k_groups=4,
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v_groups=4,
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post_attention_groups=1,
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intermediate_groups=4,
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output_groups=4,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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self.vocab_size = vocab_size
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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.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.embedding_size = embedding_size
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self.q_groups = q_groups
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self.k_groups = k_groups
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self.v_groups = v_groups
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self.post_attention_groups = post_attention_groups
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self.intermediate_groups = intermediate_groups
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self.output_groups = output_groups
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# # Copied from transformers.models.bert.configuration_bert.BertOnxxConfig with Bert->SqueezeBert
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class SqueezeBertOnnxConfig(OnnxConfig):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task == "multiple-choice":
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dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
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else:
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dynamic_axis = {0: "batch", 1: "sequence"}
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return OrderedDict(
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[
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("input_ids", dynamic_axis),
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("attention_mask", dynamic_axis),
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("token_type_ids", dynamic_axis),
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]
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)
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