194 lines
8.6 KiB
Python
194 lines
8.6 KiB
Python
# coding=utf-8
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# Copyright 2020, Microsoft 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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""" DeBERTa model configuration"""
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from collections import OrderedDict
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from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
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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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if TYPE_CHECKING:
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from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class DebertaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DebertaModel`] or a [`TFDebertaModel`]. It is
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used to instantiate a DeBERTa model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the DeBERTa
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[microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) 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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Arguments:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`].
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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"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` and `"gelu_new"`
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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 [`DebertaModel`] or [`TFDebertaModel`].
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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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The epsilon used by the layer normalization layers.
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relative_attention (`bool`, *optional*, defaults to `False`):
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Whether use relative position encoding.
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max_relative_positions (`int`, *optional*, defaults to 1):
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The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
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as `max_position_embeddings`.
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pad_token_id (`int`, *optional*, defaults to 0):
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The value used to pad input_ids.
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position_biased_input (`bool`, *optional*, defaults to `True`):
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Whether add absolute position embedding to content embedding.
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pos_att_type (`List[str]`, *optional*):
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The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
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`["p2c", "c2p"]`.
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layer_norm_eps (`float`, optional, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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Example:
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```python
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>>> from transformers import DebertaConfig, DebertaModel
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>>> # Initializing a DeBERTa microsoft/deberta-base style configuration
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>>> configuration = DebertaConfig()
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>>> # Initializing a model (with random weights) from the microsoft/deberta-base style configuration
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>>> model = DebertaModel(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 = "deberta"
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def __init__(
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self,
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vocab_size=50265,
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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=0,
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initializer_range=0.02,
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layer_norm_eps=1e-7,
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relative_attention=False,
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max_relative_positions=-1,
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pad_token_id=0,
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position_biased_input=True,
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pos_att_type=None,
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pooler_dropout=0,
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pooler_hidden_act="gelu",
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**kwargs,
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):
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super().__init__(**kwargs)
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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.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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.relative_attention = relative_attention
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self.max_relative_positions = max_relative_positions
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self.pad_token_id = pad_token_id
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self.position_biased_input = position_biased_input
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# Backwards compatibility
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if isinstance(pos_att_type, str):
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pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")]
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self.pos_att_type = pos_att_type
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self.vocab_size = vocab_size
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self.layer_norm_eps = layer_norm_eps
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self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
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self.pooler_dropout = pooler_dropout
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self.pooler_hidden_act = pooler_hidden_act
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# Copied from transformers.models.deberta_v2.configuration_deberta_v2.DebertaV2OnnxConfig
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class DebertaOnnxConfig(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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if self._config.type_vocab_size > 0:
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return OrderedDict(
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[("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]
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)
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else:
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return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])
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@property
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def default_onnx_opset(self) -> int:
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return 12
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def generate_dummy_inputs(
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self,
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preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
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batch_size: int = -1,
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seq_length: int = -1,
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num_choices: int = -1,
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is_pair: bool = False,
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framework: Optional["TensorType"] = None,
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num_channels: int = 3,
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image_width: int = 40,
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image_height: int = 40,
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tokenizer: "PreTrainedTokenizerBase" = None,
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) -> Mapping[str, Any]:
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dummy_inputs = super().generate_dummy_inputs(preprocessor=preprocessor, framework=framework)
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if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
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del dummy_inputs["token_type_ids"]
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return dummy_inputs
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