185 lines
9.0 KiB
Python
185 lines
9.0 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Meta AI Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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""" X-MOD 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 XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class XmodConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`XmodModel`]. It is used to instantiate an X-MOD
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model 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
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[facebook/xmod-base](https://huggingface.co/facebook/xmod-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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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the X-MOD model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`XmodModel`].
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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 [`XmodModel`].
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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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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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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). Only
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relevant if `config.is_decoder=True`.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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pre_norm (`bool`, *optional*, defaults to `False`):
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Whether to apply layer normalization before each block.
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adapter_reduction_factor (`int` or `float`, *optional*, defaults to 2):
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The factor by which the dimensionality of the adapter is reduced relative to `hidden_size`.
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adapter_layer_norm (`bool`, *optional*, defaults to `False`):
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Whether to apply a new layer normalization before the adapter modules (shared across all adapters).
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adapter_reuse_layer_norm (`bool`, *optional*, defaults to `True`):
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Whether to reuse the second layer normalization and apply it before the adapter modules as well.
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ln_before_adapter (`bool`, *optional*, defaults to `True`):
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Whether to apply the layer normalization before the residual connection around the adapter module.
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languages (`Iterable[str]`, *optional*, defaults to `["en_XX"]`):
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An iterable of language codes for which adapter modules should be initialized.
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default_language (`str`, *optional*):
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Language code of a default language. It will be assumed that the input is in this language if no language
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codes are explicitly passed to the forward method.
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Examples:
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```python
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>>> from transformers import XmodConfig, XmodModel
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>>> # Initializing an X-MOD facebook/xmod-base style configuration
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>>> configuration = XmodConfig()
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>>> # Initializing a model (with random weights) from the facebook/xmod-base style configuration
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>>> model = XmodModel(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 = "xmod"
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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=1,
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bos_token_id=0,
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eos_token_id=2,
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position_embedding_type="absolute",
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use_cache=True,
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classifier_dropout=None,
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pre_norm=False,
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adapter_reduction_factor=2,
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adapter_layer_norm=False,
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adapter_reuse_layer_norm=True,
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ln_before_adapter=True,
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languages=("en_XX",),
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default_language=None,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_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.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.classifier_dropout = classifier_dropout
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self.pre_norm = pre_norm
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self.adapter_reduction_factor = adapter_reduction_factor
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self.adapter_layer_norm = adapter_layer_norm
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self.adapter_reuse_layer_norm = adapter_reuse_layer_norm
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self.ln_before_adapter = ln_before_adapter
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self.languages = list(languages)
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self.default_language = default_language
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# Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->Xmod
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class XmodOnnxConfig(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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]
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)
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