FM / fsmt

/usr/local/lib/python3.6/site-packages/transformers/models/fsmt/

/usr/local/lib/python3.6/site-packages/transformers/models/fsmt

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_fsmt.py	9894	0644	edit dl rm
convert_fsmt_original_pytorch_checkpoint_to_pytorch.py	11226	0644	edit dl rm
modeling_fsmt.py	53346	0644	edit dl rm
tokenization_fsmt.py	19335	0644	edit dl rm
__init__.py	1566	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/fsmt/configuration_fsmt.py (9894B)

# coding=utf-8
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" FSMT configuration"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP = {}

class DecoderConfig(PretrainedConfig):
    r"""
    Configuration class for FSMT's decoder specific things. note: this is a private helper class
    """
    model_type = "fsmt_decoder"

def __init__(self, vocab_size=0, bos_token_id=0):
        super().__init__()
        self.vocab_size = vocab_size
        self.bos_token_id = bos_token_id

class FSMTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FSMTModel`]. It is used to instantiate a FSMT
    model according to the specified arguments, defining the model architecture.

Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

Args:
        langs (`List[str]`):
            A list with source language and target_language (e.g., ['en', 'ru']).
        src_vocab_size (`int`):
            Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed to the forward method in the encoder.
        tgt_vocab_size (`int`):
            Vocabulary size of the decoder. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed to the forward method in the decoder.
        d_model (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        encoder_layers (`int`, *optional*, defaults to 12):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `Callable`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        max_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        scale_embedding (`bool`, *optional*, defaults to `True`):
            Scale embeddings by diving by sqrt(d_model).
        bos_token_id (`int`, *optional*, defaults to 0)
            Beginning of stream token id.
        pad_token_id (`int`, *optional*, defaults to 1)
            Padding token id.
        eos_token_id (`int`, *optional*, defaults to 2)
            End of stream token id.
        decoder_start_token_id (`int`, *optional*):
            This model starts decoding with `eos_token_id`
        encoder_layerdrop: (`float`, *optional*, defaults to 0.0):
            Google "layerdrop arxiv", as its not explainable in one line.
        decoder_layerdrop: (`float`, *optional*, defaults to 0.0):
            Google "layerdrop arxiv", as its not explainable in one line.
        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
            Whether this is an encoder/decoder model.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie input and output embeddings.
        num_beams (`int`, *optional*, defaults to 5)
            Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means
            no beam search.
        length_penalty (`float`, *optional*, defaults to 1)
            Exponential penalty to the length that will be used by default in the `generate` method of the model.
        early_stopping (`bool`, *optional*, defaults to `False`)
            Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search
            when at least `num_beams` sentences are finished per batch or not.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*, defaults to 2):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.

Examples:

```python
    >>> from transformers import FSMTConfig, FSMTModel

>>> config = FSMTConfig.from_pretrained("facebook/wmt19-en-ru")
    >>> model = FSMTModel(config)
    ```"""
    model_type = "fsmt"
    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

# update the defaults from config file
    def __init__(
        self,
        langs=["en", "de"],
        src_vocab_size=42024,
        tgt_vocab_size=42024,
        activation_function="relu",
        d_model=1024,
        max_length=200,
        max_position_embeddings=1024,
        encoder_ffn_dim=4096,
        encoder_layers=12,
        encoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_ffn_dim=4096,
        decoder_layers=12,
        decoder_attention_heads=16,
        decoder_layerdrop=0.0,
        attention_dropout=0.0,
        dropout=0.1,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        is_encoder_decoder=True,
        scale_embedding=True,
        tie_word_embeddings=False,
        num_beams=5,
        length_penalty=1.0,
        early_stopping=False,
        use_cache=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        forced_eos_token_id=2,
        **common_kwargs
    ):
        self.langs = langs
        self.src_vocab_size = src_vocab_size
        self.tgt_vocab_size = tgt_vocab_size
        self.d_model = d_model  # encoder_embed_dim and decoder_embed_dim

self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = self.num_hidden_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.init_std = init_std  # Normal(0, this parameter)
        self.activation_function = activation_function

self.decoder = DecoderConfig(vocab_size=tgt_vocab_size, bos_token_id=eos_token_id)
        if "decoder" in common_kwargs:
            del common_kwargs["decoder"]

self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True

# 3 Types of Dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.dropout = dropout

self.use_cache = use_cache
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            decoder_start_token_id=decoder_start_token_id,
            is_encoder_decoder=is_encoder_decoder,
            tie_word_embeddings=tie_word_embeddings,
            forced_eos_token_id=forced_eos_token_id,
            max_length=max_length,
            num_beams=num_beams,
            length_penalty=length_penalty,
            early_stopping=early_stopping,
            **common_kwargs,
        )

def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default *to_dict()* from *PretrainedConfig*.

Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["decoder"] = self.decoder.to_dict()
        output["model_type"] = self.__class__.model_type
        return output

cmd: