220 lines
9.9 KiB
Python
220 lines
9.9 KiB
Python
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# coding=utf-8
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# Copyright 2019-present, Facebook, Inc 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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""" FSMT 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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from ..deprecated._archive_maps import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class DecoderConfig(PretrainedConfig):
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r"""
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Configuration class for FSMT's decoder specific things. note: this is a private helper class
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"""
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model_type = "fsmt_decoder"
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def __init__(self, vocab_size=0, bos_token_id=0):
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super().__init__()
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self.vocab_size = vocab_size
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self.bos_token_id = bos_token_id
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class FSMTConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FSMTModel`]. It is used to instantiate a FSMT
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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 FSMT
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[facebook/wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) 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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langs (`List[str]`):
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A list with source language and target_language (e.g., ['en', 'ru']).
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src_vocab_size (`int`):
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Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed to the forward method in the encoder.
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tgt_vocab_size (`int`):
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Vocabulary size of the decoder. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed to the forward method in the decoder.
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d_model (`int`, *optional*, defaults to 1024):
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Dimensionality of the layers and the pooler layer.
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encoder_layers (`int`, *optional*, defaults to 12):
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Number of encoder layers.
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decoder_layers (`int`, *optional*, defaults to 12):
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Number of decoder layers.
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encoder_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer decoder.
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decoder_ffn_dim (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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encoder_ffn_dim (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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activation_function (`str` or `Callable`, *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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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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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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max_position_embeddings (`int`, *optional*, defaults to 1024):
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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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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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scale_embedding (`bool`, *optional*, defaults to `True`):
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Scale embeddings by diving by sqrt(d_model).
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bos_token_id (`int`, *optional*, defaults to 0)
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Beginning of stream token id.
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pad_token_id (`int`, *optional*, defaults to 1)
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Padding token id.
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eos_token_id (`int`, *optional*, defaults to 2)
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End of stream token id.
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decoder_start_token_id (`int`, *optional*):
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This model starts decoding with `eos_token_id`
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encoder_layerdrop (`float`, *optional*, defaults to 0.0):
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Google "layerdrop arxiv", as its not explainable in one line.
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decoder_layerdrop (`float`, *optional*, defaults to 0.0):
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Google "layerdrop arxiv", as its not explainable in one line.
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Whether this is an encoder/decoder model.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie input and output embeddings.
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num_beams (`int`, *optional*, defaults to 5)
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Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means
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no beam search.
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length_penalty (`float`, *optional*, defaults to 1)
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Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
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the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
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likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
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`length_penalty` < 0.0 encourages shorter sequences.
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early_stopping (`bool`, *optional*, defaults to `False`)
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Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search
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when at least `num_beams` sentences are finished per batch or not.
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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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forced_eos_token_id (`int`, *optional*, defaults to 2):
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The id of the token to force as the last generated token when `max_length` is reached. Usually set to
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`eos_token_id`.
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Examples:
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```python
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>>> from transformers import FSMTConfig, FSMTModel
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>>> # Initializing a FSMT facebook/wmt19-en-ru style configuration
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>>> config = FSMTConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = FSMTModel(config)
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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 = "fsmt"
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attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
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# update the defaults from config file
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def __init__(
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self,
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langs=["en", "de"],
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src_vocab_size=42024,
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tgt_vocab_size=42024,
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activation_function="relu",
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d_model=1024,
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max_length=200,
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max_position_embeddings=1024,
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encoder_ffn_dim=4096,
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encoder_layers=12,
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encoder_attention_heads=16,
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encoder_layerdrop=0.0,
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decoder_ffn_dim=4096,
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decoder_layers=12,
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decoder_attention_heads=16,
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decoder_layerdrop=0.0,
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attention_dropout=0.0,
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dropout=0.1,
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activation_dropout=0.0,
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init_std=0.02,
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decoder_start_token_id=2,
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is_encoder_decoder=True,
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scale_embedding=True,
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tie_word_embeddings=False,
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num_beams=5,
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length_penalty=1.0,
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early_stopping=False,
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use_cache=True,
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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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forced_eos_token_id=2,
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**common_kwargs,
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):
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self.langs = langs
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self.src_vocab_size = src_vocab_size
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self.tgt_vocab_size = tgt_vocab_size
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self.d_model = d_model # encoder_embed_dim and decoder_embed_dim
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_layers = self.num_hidden_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.encoder_layerdrop = encoder_layerdrop
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self.decoder_layerdrop = decoder_layerdrop
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.init_std = init_std # Normal(0, this parameter)
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self.activation_function = activation_function
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self.decoder = DecoderConfig(vocab_size=tgt_vocab_size, bos_token_id=eos_token_id)
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if "decoder" in common_kwargs:
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del common_kwargs["decoder"]
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self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
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# 3 Types of Dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.dropout = dropout
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self.use_cache = use_cache
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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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decoder_start_token_id=decoder_start_token_id,
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is_encoder_decoder=is_encoder_decoder,
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tie_word_embeddings=tie_word_embeddings,
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forced_eos_token_id=forced_eos_token_id,
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max_length=max_length,
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num_beams=num_beams,
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length_penalty=length_penalty,
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early_stopping=early_stopping,
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**common_kwargs,
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)
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