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978 lines
45 KiB
Python
978 lines
45 KiB
Python
# 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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""" PyTorch XLM model.
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"""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import json
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import logging
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import math
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import sys
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from io import open
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import itertools
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.nn import CrossEntropyLoss, MSELoss
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from .modeling_utils import (PretrainedConfig, PreTrainedModel,
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prune_linear_layer, SequenceSummary, SQuADHead)
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logger = logging.getLogger(__name__)
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-pytorch_model.bin",
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'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-pytorch_model.bin",
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'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-pytorch_model.bin",
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'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-pytorch_model.bin",
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'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-pytorch_model.bin",
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'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-pytorch_model.bin",
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'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-pytorch_model.bin",
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'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-pytorch_model.bin",
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}
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XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
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'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
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'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-configl.json",
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'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
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'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
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'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
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'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
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'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
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}
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class XLMConfig(PretrainedConfig):
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"""Configuration class to store the configuration of a `XLMModel`.
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Args:
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vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`.
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d_model: Size of the encoder layers and the pooler layer.
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n_layer: Number of hidden layers in the Transformer encoder.
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n_head: Number of attention heads for each attention layer in
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the Transformer encoder.
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d_inner: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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ff_activation: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
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untie_r: untie relative position biases
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attn_type: 'bi' for XLM, 'uni' for Transformer-XL
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dropout: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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dropatt: The dropout ratio for the attention
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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layer_norm_eps: The epsilon used by LayerNorm.
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dropout: float, dropout rate.
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dropatt: float, dropout rate on attention probabilities.
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init: str, the initialization scheme, either "normal" or "uniform".
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init_range: float, initialize the parameters with a uniform distribution
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in [-init_range, init_range]. Only effective when init="uniform".
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init_std: float, initialize the parameters with a normal distribution
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with mean 0 and stddev init_std. Only effective when init="normal".
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mem_len: int, the number of tokens to cache.
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reuse_len: int, the number of tokens in the currect batch to be cached
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and reused in the future.
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bi_data: bool, whether to use bidirectional input pipeline.
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Usually set to True during pretraining and False during finetuning.
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clamp_len: int, clamp all relative distances larger than clamp_len.
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-1 means no clamping.
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same_length: bool, whether to use the same attention length for each token.
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"""
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pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=30145,
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emb_dim=2048,
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n_layers=12,
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n_heads=16,
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dropout=0.1,
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attention_dropout=0.1,
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gelu_activation=True,
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sinusoidal_embeddings=False,
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causal=False,
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asm=False,
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n_langs=1,
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max_position_embeddings=512,
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embed_init_std=2048 ** -0.5,
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layer_norm_eps=1e-12,
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init_std=0.02,
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bos_index=0,
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eos_index=1,
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pad_index=2,
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unk_index=3,
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mask_index=5,
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is_encoder=True,
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finetuning_task=None,
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num_labels=2,
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summary_type='first',
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summary_use_proj=True,
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summary_activation=None,
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summary_proj_to_labels=True,
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summary_first_dropout=0.1,
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start_n_top=5,
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end_n_top=5,
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**kwargs):
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"""Constructs XLMConfig.
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"""
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super(XLMConfig, self).__init__(**kwargs)
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if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.n_words = vocab_size_or_config_json_file
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self.emb_dim = emb_dim
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.gelu_activation = gelu_activation
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self.sinusoidal_embeddings = sinusoidal_embeddings
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self.causal = causal
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self.asm = asm
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self.n_langs = n_langs
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self.layer_norm_eps = layer_norm_eps
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self.bos_index = bos_index
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self.eos_index = eos_index
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self.pad_index = pad_index
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self.unk_index = unk_index
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self.mask_index = mask_index
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self.is_encoder = is_encoder
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self.max_position_embeddings = max_position_embeddings
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self.embed_init_std = embed_init_std
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self.init_std = init_std
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self.finetuning_task = finetuning_task
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self.num_labels = num_labels
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_proj_to_labels = summary_proj_to_labels
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self.summary_first_dropout = summary_first_dropout
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self.start_n_top = start_n_top
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self.end_n_top = end_n_top
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else:
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raise ValueError("First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)")
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@property
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def vocab_size(self):
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return self.n_words
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@property
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def hidden_size(self):
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return self.emb_dim
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@property
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def num_attention_heads(self):
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return self.n_heads
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@property
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def num_hidden_layers(self):
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return self.n_layers
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def create_sinusoidal_embeddings(n_pos, dim, out):
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position_enc = np.array([
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[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)]
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for pos in range(n_pos)
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])
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out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
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out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
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out.detach_()
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out.requires_grad = False
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def gelu(x):
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"""
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GELU activation
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https://arxiv.org/abs/1606.08415
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https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/model_pytorch.py#L14
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https://github.com/huggingface/pytorch-transformers/blob/master/modeling.py
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"""
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# return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
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def get_masks(slen, lengths, causal, padding_mask=None):
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"""
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Generate hidden states mask, and optionally an attention mask.
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"""
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bs = lengths.size(0)
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if padding_mask is not None:
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mask = padding_mask
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else:
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assert lengths.max().item() <= slen
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alen = torch.arange(slen, dtype=torch.long, device=lengths.device)
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mask = alen < lengths[:, None]
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# attention mask is the same as mask, or triangular inferior attention (causal)
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if causal:
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attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None]
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else:
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attn_mask = mask
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# sanity check
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assert mask.size() == (bs, slen)
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assert causal is False or attn_mask.size() == (bs, slen, slen)
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return mask, attn_mask
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class MultiHeadAttention(nn.Module):
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NEW_ID = itertools.count()
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def __init__(self, n_heads, dim, config):
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super(MultiHeadAttention, self).__init__()
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self.layer_id = next(MultiHeadAttention.NEW_ID)
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self.output_attentions = config.output_attentions
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self.dim = dim
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self.n_heads = n_heads
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self.dropout = config.attention_dropout
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assert self.dim % self.n_heads == 0
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self.q_lin = nn.Linear(dim, dim)
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self.k_lin = nn.Linear(dim, dim)
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self.v_lin = nn.Linear(dim, dim)
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self.out_lin = nn.Linear(dim, dim)
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def prune_heads(self, heads):
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attention_head_size = self.dim // self.n_heads
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if len(heads) == 0:
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return
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mask = torch.ones(self.n_heads, attention_head_size)
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for head in heads:
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mask[head] = 0
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mask = mask.view(-1).contiguous().eq(1)
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index = torch.arange(len(mask))[mask].long()
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# Prune linear layers
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self.q_lin = prune_linear_layer(self.q_lin, index)
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self.k_lin = prune_linear_layer(self.k_lin, index)
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self.v_lin = prune_linear_layer(self.v_lin, index)
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self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
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# Update hyper params
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self.n_heads = self.n_heads - len(heads)
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self.dim = attention_head_size * self.n_heads
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def forward(self, input, mask, kv=None, cache=None, head_mask=None):
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"""
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Self-attention (if kv is None) or attention over source sentence (provided by kv).
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"""
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# Input is (bs, qlen, dim)
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# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
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bs, qlen, dim = input.size()
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if kv is None:
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klen = qlen if cache is None else cache['slen'] + qlen
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else:
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klen = kv.size(1)
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# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
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n_heads = self.n_heads
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dim_per_head = self.dim // n_heads
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mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen)
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def shape(x):
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""" projection """
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return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
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def unshape(x):
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""" compute context """
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return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
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q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
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if kv is None:
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k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head)
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v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head)
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elif cache is None or self.layer_id not in cache:
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k = v = kv
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k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head)
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v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head)
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if cache is not None:
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if self.layer_id in cache:
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if kv is None:
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k_, v_ = cache[self.layer_id]
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k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head)
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v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head)
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else:
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k, v = cache[self.layer_id]
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cache[self.layer_id] = (k, v)
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q = q / math.sqrt(dim_per_head) # (bs, n_heads, qlen, dim_per_head)
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scores = torch.matmul(q, k.transpose(2, 3)) # (bs, n_heads, qlen, klen)
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mask = (mask == 0).view(mask_reshape).expand_as(scores) # (bs, n_heads, qlen, klen)
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scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen)
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weights = F.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen)
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weights = F.dropout(weights, p=self.dropout, training=self.training) # (bs, n_heads, qlen, klen)
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# Mask heads if we want to
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if head_mask is not None:
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weights = weights * head_mask
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context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
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context = unshape(context) # (bs, qlen, dim)
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outputs = (self.out_lin(context),)
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if self.output_attentions:
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outputs = outputs + (weights,)
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return outputs
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class TransformerFFN(nn.Module):
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def __init__(self, in_dim, dim_hidden, out_dim, config):
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super(TransformerFFN, self).__init__()
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self.dropout = config.dropout
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self.lin1 = nn.Linear(in_dim, dim_hidden)
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self.lin2 = nn.Linear(dim_hidden, out_dim)
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self.act = gelu if config.gelu_activation else F.relu
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def forward(self, input):
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x = self.lin1(input)
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x = self.act(x)
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x = self.lin2(x)
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x = F.dropout(x, p=self.dropout, training=self.training)
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return x
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class XLMPreTrainedModel(PreTrainedModel):
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""" An abstract class to handle weights initialization and
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a simple interface for dowloading and loading pretrained models.
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"""
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config_class = XLMConfig
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pretrained_model_archive_map = XLM_PRETRAINED_MODEL_ARCHIVE_MAP
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load_tf_weights = None
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base_model_prefix = "transformer"
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def __init__(self, *inputs, **kwargs):
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super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs)
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def init_weights(self, module):
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""" Initialize the weights. """
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if isinstance(module, nn.Embedding):
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if self.config is not None and self.config.embed_init_std is not None:
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nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)
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if isinstance(module, nn.Linear):
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if self.config is not None and self.config.init_std is not None:
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nn.init.normal_(module.weight, mean=0, std=self.config.init_std)
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if hasattr(module, 'bias') and module.bias is not None:
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nn.init.constant_(module.bias, 0.)
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if isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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class XLMModel(XLMPreTrainedModel):
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"""
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XLM model from: "Cross-lingual Language Model Pretraining" by Guillaume Lample, Alexis Conneau
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Paper: https://arxiv.org/abs/1901.07291
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Original code: https://github.com/facebookresearch/XLM
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Args:
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`config`: a XLMConfig class instance with the configuration to build a new model
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`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
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`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
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This can be used to compute head importance metrics. Default: False
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Example::
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config = modeling.XLMConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
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num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
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model = modeling.XLMModel(config=config)
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"""
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ATTRIBUTES = ['encoder', 'eos_index', 'pad_index', # 'with_output',
|
|
'n_langs', 'n_words', 'dim', 'n_layers', 'n_heads',
|
|
'hidden_dim', 'dropout', 'attention_dropout', 'asm',
|
|
'asm_cutoffs', 'asm_div_value']
|
|
|
|
def __init__(self, config): #, dico, is_encoder, with_output):
|
|
super(XLMModel, self).__init__(config)
|
|
self.output_attentions = config.output_attentions
|
|
self.output_hidden_states = config.output_hidden_states
|
|
|
|
# encoder / decoder, output layer
|
|
self.is_encoder = config.is_encoder
|
|
self.is_decoder = not config.is_encoder
|
|
if self.is_decoder:
|
|
raise NotImplementedError("Currently XLM can only be used as an encoder")
|
|
# self.with_output = with_output
|
|
self.causal = config.causal
|
|
|
|
# dictionary / languages
|
|
self.n_langs = config.n_langs
|
|
self.n_words = config.n_words
|
|
self.eos_index = config.eos_index
|
|
self.pad_index = config.pad_index
|
|
# self.dico = dico
|
|
# self.id2lang = config.id2lang
|
|
# self.lang2id = config.lang2id
|
|
# assert len(self.dico) == self.n_words
|
|
# assert len(self.id2lang) == len(self.lang2id) == self.n_langs
|
|
|
|
# model parameters
|
|
self.dim = config.emb_dim # 512 by default
|
|
self.hidden_dim = self.dim * 4 # 2048 by default
|
|
self.n_heads = config.n_heads # 8 by default
|
|
self.n_layers = config.n_layers
|
|
self.dropout = config.dropout
|
|
self.attention_dropout = config.attention_dropout
|
|
assert self.dim % self.n_heads == 0, 'transformer dim must be a multiple of n_heads'
|
|
|
|
# embeddings
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
|
|
if config.sinusoidal_embeddings:
|
|
create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
|
|
if config.n_langs > 1:
|
|
self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
|
|
self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
|
|
self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)
|
|
|
|
# transformer layers
|
|
self.attentions = nn.ModuleList()
|
|
self.layer_norm1 = nn.ModuleList()
|
|
self.ffns = nn.ModuleList()
|
|
self.layer_norm2 = nn.ModuleList()
|
|
# if self.is_decoder:
|
|
# self.layer_norm15 = nn.ModuleList()
|
|
# self.encoder_attn = nn.ModuleList()
|
|
|
|
for _ in range(self.n_layers):
|
|
self.attentions.append(MultiHeadAttention(self.n_heads, self.dim, config=config))
|
|
self.layer_norm1.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
|
|
# if self.is_decoder:
|
|
# self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
|
|
# self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
|
|
self.ffns.append(TransformerFFN(self.dim, self.hidden_dim, self.dim, config=config))
|
|
self.layer_norm2.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
|
|
|
|
self.apply(self.init_weights)
|
|
|
|
def _resize_token_embeddings(self, new_num_tokens):
|
|
self.embeddings = self._get_resized_embeddings(self.embeddings, new_num_tokens)
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
""" Prunes heads of the model.
|
|
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
|
|
See base class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.attentions[layer].prune_heads(heads)
|
|
|
|
def forward(self, input_ids, lengths=None, positions=None, langs=None,
|
|
token_type_ids=None, attention_mask=None, cache=None, head_mask=None): # src_enc=None, src_len=None,
|
|
"""
|
|
Performs a model forward pass. **Can be called by calling the class directly, once it has been instantiated.**
|
|
|
|
Parameters:
|
|
`input_ids`: a ``torch.LongTensor`` of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
|
|
`lengths`: ``torch.LongTensor`` of size ``bs``, containing the length of each sentence
|
|
`positions`: ``torch.LongTensor`` of size ``(bs, slen)``, containing word positions
|
|
`langs`: ``torch.LongTensor`` of size ``(bs, slen)``, containing language IDs
|
|
`token_type_ids`: an optional ``torch.LongTensor`` of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see XLM paper for more details).
|
|
`attention_mask`: an optional ``torch.LongTensor`` of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`cache`: TODO
|
|
`head_mask`: an optional ``torch.Tensor`` of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
|
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
|
|
|
|
|
Returns:
|
|
A ``tuple(encoded_layers, pooled_output)``, with
|
|
|
|
``encoded_layers``: controlled by ``output_all_encoded_layers`` argument:
|
|
|
|
- ``output_all_encoded_layers=True``: outputs a list of the full sequences of encoded-hidden-states at the end \
|
|
of each attention block (i.e. 12 full sequences for XLM-base, 24 for XLM-large), each \
|
|
encoded-hidden-state is a ``torch.FloatTensor`` of size [batch_size, sequence_length, hidden_size],
|
|
|
|
- ``output_all_encoded_layers=False``: outputs only the full sequence of hidden-states corresponding \
|
|
to the last attention block of shape [batch_size, sequence_length, hidden_size],
|
|
|
|
``pooled_output``: a ``torch.FloatTensor`` of size [batch_size, hidden_size] which is the output of a
|
|
classifier pre-trained on top of the hidden state associated to the first character of the
|
|
input (`CLS`) to train on the Next-Sentence task (see XLM's paper).
|
|
|
|
Example::
|
|
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
|
# or
|
|
all_encoder_layers, pooled_output = model.forward(input_ids, token_type_ids, input_mask)
|
|
"""
|
|
if lengths is None:
|
|
lengths = (input_ids != self.pad_index).sum(dim=1).long()
|
|
# mask = input_ids != self.pad_index
|
|
|
|
# check inputs
|
|
bs, slen = input_ids.size()
|
|
assert lengths.size(0) == bs
|
|
assert lengths.max().item() <= slen
|
|
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
|
|
# assert (src_enc is None) == (src_len is None)
|
|
# if src_enc is not None:
|
|
# assert self.is_decoder
|
|
# assert src_enc.size(0) == bs
|
|
|
|
# generate masks
|
|
mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
|
|
# if self.is_decoder and src_enc is not None:
|
|
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
|
|
|
|
# positions
|
|
if positions is None:
|
|
positions = input_ids.new((slen,)).long()
|
|
positions = torch.arange(slen, out=positions).unsqueeze(0)
|
|
else:
|
|
assert positions.size() == (bs, slen) # (slen, bs)
|
|
# positions = positions.transpose(0, 1)
|
|
|
|
# langs
|
|
assert langs is None or token_type_ids is None, "You can only use one among langs and token_type_ids"
|
|
if token_type_ids is not None:
|
|
langs = token_type_ids
|
|
if langs is not None:
|
|
assert langs.size() == (bs, slen) # (slen, bs)
|
|
# langs = langs.transpose(0, 1)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x qlen x klen]
|
|
if head_mask is not None:
|
|
if head_mask.dim() == 1:
|
|
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
|
|
head_mask = head_mask.expand(self.n_layers, -1, -1, -1, -1)
|
|
elif head_mask.dim() == 2:
|
|
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
|
|
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
|
|
else:
|
|
head_mask = [None] * self.n_layers
|
|
|
|
# do not recompute cached elements
|
|
if cache is not None:
|
|
_slen = slen - cache['slen']
|
|
input_ids = input_ids[:, -_slen:]
|
|
positions = positions[:, -_slen:]
|
|
if langs is not None:
|
|
langs = langs[:, -_slen:]
|
|
mask = mask[:, -_slen:]
|
|
attn_mask = attn_mask[:, -_slen:]
|
|
|
|
# embeddings
|
|
tensor = self.embeddings(input_ids)
|
|
tensor = tensor + self.position_embeddings(positions).expand_as(tensor)
|
|
if langs is not None:
|
|
tensor = tensor + self.lang_embeddings(langs)
|
|
tensor = self.layer_norm_emb(tensor)
|
|
tensor = F.dropout(tensor, p=self.dropout, training=self.training)
|
|
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
|
|
|
|
# transformer layers
|
|
hidden_states = ()
|
|
attentions = ()
|
|
for i in range(self.n_layers):
|
|
if self.output_hidden_states:
|
|
hidden_states = hidden_states + (tensor,)
|
|
|
|
# self attention
|
|
attn_outputs = self.attentions[i](tensor, attn_mask, cache=cache, head_mask=head_mask[i])
|
|
attn = attn_outputs[0]
|
|
if self.output_attentions:
|
|
attentions = attentions + (attn_outputs[1],)
|
|
attn = F.dropout(attn, p=self.dropout, training=self.training)
|
|
tensor = tensor + attn
|
|
tensor = self.layer_norm1[i](tensor)
|
|
|
|
# encoder attention (for decoder only)
|
|
# if self.is_decoder and src_enc is not None:
|
|
# attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
|
|
# attn = F.dropout(attn, p=self.dropout, training=self.training)
|
|
# tensor = tensor + attn
|
|
# tensor = self.layer_norm15[i](tensor)
|
|
|
|
# FFN
|
|
tensor = tensor + self.ffns[i](tensor)
|
|
tensor = self.layer_norm2[i](tensor)
|
|
tensor *= mask.unsqueeze(-1).to(tensor.dtype)
|
|
|
|
# Add last hidden state
|
|
if self.output_hidden_states:
|
|
hidden_states = hidden_states + (tensor,)
|
|
|
|
# update cache length
|
|
if cache is not None:
|
|
cache['slen'] += tensor.size(1)
|
|
|
|
# move back sequence length to dimension 0
|
|
# tensor = tensor.transpose(0, 1)
|
|
|
|
outputs = (tensor,)
|
|
if self.output_hidden_states:
|
|
outputs = outputs + (hidden_states,)
|
|
if self.output_attentions:
|
|
outputs = outputs + (attentions,)
|
|
return outputs # outputs, (hidden_states), (attentions)
|
|
|
|
|
|
class XLMPredLayer(nn.Module):
|
|
"""
|
|
Prediction layer (cross_entropy or adaptive_softmax).
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLMPredLayer, self).__init__()
|
|
self.asm = config.asm
|
|
self.n_words = config.n_words
|
|
self.pad_index = config.pad_index
|
|
dim = config.emb_dim
|
|
|
|
if config.asm is False:
|
|
self.proj = nn.Linear(dim, config.n_words, bias=True)
|
|
else:
|
|
self.proj = nn.AdaptiveLogSoftmaxWithLoss(
|
|
in_features=dim,
|
|
n_classes=config.n_words,
|
|
cutoffs=config.asm_cutoffs,
|
|
div_value=config.asm_div_value,
|
|
head_bias=True, # default is False
|
|
)
|
|
|
|
def forward(self, x, y=None):
|
|
""" Compute the loss, and optionally the scores.
|
|
"""
|
|
outputs = ()
|
|
if self.asm is False:
|
|
scores = self.proj(x).view(-1, self.n_words)
|
|
outputs = (scores,) + outputs
|
|
if y is not None:
|
|
loss = F.cross_entropy(scores, y, reduction='elementwise_mean')
|
|
outputs = (loss,) + outputs
|
|
else:
|
|
scores = self.proj.log_prob(x)
|
|
outputs = (scores,) + outputs
|
|
if y is not None:
|
|
_, loss = self.proj(x, y)
|
|
outputs = (loss,) + outputs
|
|
|
|
return outputs
|
|
|
|
|
|
class XLMWithLMHeadModel(XLMPreTrainedModel):
|
|
""" XLM model from: "Cross-lingual Language Model Pretraining" by Guillaume Lample, Alexis Conneau
|
|
|
|
Paper: https://arxiv.org/abs/1901.07291
|
|
|
|
Original code: https://github.com/facebookresearch/XLM
|
|
|
|
Args:
|
|
`config`: a XLMConfig class instance with the configuration to build a new model
|
|
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
|
`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
|
|
This can be used to compute head importance metrics. Default: False
|
|
|
|
Example::
|
|
|
|
config = modeling.XLMConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = modeling.XLMModel(config=config)
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLMWithLMHeadModel, self).__init__(config)
|
|
self.transformer = XLMModel(config)
|
|
self.pred_layer = XLMPredLayer(config)
|
|
|
|
self.apply(self.init_weights)
|
|
self.tie_weights()
|
|
|
|
def tie_weights(self):
|
|
""" Make sure we are sharing the embeddings
|
|
"""
|
|
if self.config.torchscript:
|
|
self.pred_layer.proj.weight = nn.Parameter(self.transformer.embeddings.weight.clone())
|
|
else:
|
|
self.pred_layer.proj.weight = self.transformer.embeddings.weight
|
|
|
|
def forward(self, input_ids, lengths=None, positions=None, langs=None, token_type_ids=None,
|
|
attention_mask=None, cache=None, labels=None, head_mask=None):
|
|
"""
|
|
Args:
|
|
`input_ids`: a ``torch.LongTensor`` of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
|
|
`lengths`: TODO
|
|
`positions`: TODO
|
|
`langs`: TODO
|
|
`token_type_ids`: an optional ``torch.LongTensor`` of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see XLM paper for more details).
|
|
`attention_mask`: an optional ``torch.LongTensor`` of shape [batch_size, sequence_length] with indices
|
|
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
|
input sequence length in the current batch. It's the mask that we typically use for attention when
|
|
a batch has varying length sentences.
|
|
`cache`: TODO
|
|
`labels`: TODO
|
|
`head_mask`: an optional ``torch.Tensor`` of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
|
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
|
|
|
|
|
Returns:
|
|
A ``tuple(encoded_layers, pooled_output)``, with
|
|
|
|
``encoded_layers``: controlled by ``output_all_encoded_layers`` argument:
|
|
|
|
If ``output_all_encoded_layers=True``: outputs a list of the full sequences of encoded-hidden-states \
|
|
at the end of each attention block (i.e. 12 full sequences for XLM-base, 24 for XLM-large), each \
|
|
encoded-hidden-state is a ``torch.FloatTensor`` of size [batch_size, sequence_length, hidden_size],
|
|
|
|
If ``output_all_encoded_layers=False``: outputs only the full sequence of hidden-states corresponding \
|
|
to the last attention block of shape [batch_size, sequence_length, hidden_size],
|
|
|
|
``pooled_output``: a ``torch.FloatTensor`` of size [batch_size, hidden_size] which is the output of a \
|
|
classifier pre-trained on top of the hidden state associated to the first character of the \
|
|
input (`CLS`) to train on the Next-Sentence task (see XLM's paper).
|
|
|
|
Example::
|
|
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
|
# or
|
|
all_encoder_layers, pooled_output = model.forward(input_ids, token_type_ids, input_mask)
|
|
"""
|
|
transformer_outputs = self.transformer(input_ids, lengths=lengths, positions=positions, token_type_ids=token_type_ids,
|
|
langs=langs, attention_mask=attention_mask, cache=cache, head_mask=head_mask)
|
|
|
|
output = transformer_outputs[0]
|
|
outputs = self.pred_layer(output, labels)
|
|
outputs = outputs + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
|
|
|
|
return outputs
|
|
|
|
|
|
class XLMForSequenceClassification(XLMPreTrainedModel):
|
|
"""XLM model ("XLM: Generalized Autoregressive Pretraining for Language Understanding").
|
|
|
|
Args:
|
|
`config`: a XLMConfig class instance with the configuration to build a new model
|
|
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
|
`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
|
|
This can be used to compute head importance metrics. Default: False
|
|
`summary_type`: str, "last", "first", "mean", or "attn". The method
|
|
to pool the input to get a vector representation. Default: last
|
|
|
|
|
|
|
|
Example::
|
|
|
|
config = modeling.XLMConfig(vocab_size_or_config_json_file=32000, d_model=768,
|
|
n_layer=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = modeling.XLMModel(config=config)
|
|
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLMForSequenceClassification, self).__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = XLMModel(config)
|
|
self.sequence_summary = SequenceSummary(config)
|
|
|
|
self.apply(self.init_weights)
|
|
|
|
def forward(self, input_ids, lengths=None, positions=None, langs=None, token_type_ids=None,
|
|
attention_mask=None, cache=None, labels=None, head_mask=None):
|
|
"""
|
|
Args:
|
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input_ids: TODO
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lengths: TODO
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positions: TODO
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langs: TODO
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token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
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attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
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but with 1 for real tokens and 0 for padding.
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Added for easy compatibility with the XLM model (which uses this negative masking).
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You can only uses one among `input_mask` and `attention_mask`
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cache: TODO
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labels: TODO
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head_mask: TODO
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|
|
|
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Returns:
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A ``tuple(logits_or_loss, new_mems)``. If ``labels`` is ``None``, return token logits with shape
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[batch_size, sequence_length]. If it isn't ``None``, return the ``CrossEntropy`` loss with the targets.
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|
|
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``new_mems`` is a list (num layers) of updated mem states at the entry of each layer \
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each mem state is a ``torch.FloatTensor`` of size [self.config.mem_len, batch_size, self.config.d_model] \
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Note that the first two dimensions are transposed in ``mems`` with regards to ``input_ids`` and ``labels``
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|
|
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Example::
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|
|
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# Already been converted into WordPiece token ids
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input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
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input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
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token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
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|
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all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
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"""
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transformer_outputs = self.transformer(input_ids, lengths=lengths, positions=positions, token_type_ids=token_type_ids,
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langs=langs, attention_mask=attention_mask, cache=cache, head_mask=head_mask)
|
|
|
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output = transformer_outputs[0]
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logits = self.sequence_summary(output)
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|
|
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outputs = (logits,) + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
|
|
|
|
if labels is not None:
|
|
if self.num_labels == 1:
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# We are doing regression
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|
loss_fct = MSELoss()
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loss = loss_fct(logits.view(-1), labels.view(-1))
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else:
|
|
loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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outputs = (loss,) + outputs
|
|
|
|
return outputs
|
|
|
|
|
|
class XLMForQuestionAnswering(XLMPreTrainedModel):
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"""
|
|
XLM model for Question Answering (span extraction).
|
|
This module is composed of the XLM model with a linear layer on top of
|
|
the sequence output that computes start_logits and end_logits
|
|
|
|
Args:
|
|
`config`: a XLMConfig class instance with the configuration to build a new model
|
|
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
|
`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
|
|
This can be used to compute head importance metrics. Default: False
|
|
|
|
|
|
|
|
Example::
|
|
|
|
config = XLMConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
|
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = XLMForQuestionAnswering(config)
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLMForQuestionAnswering, self).__init__(config)
|
|
|
|
self.transformer = XLMModel(config)
|
|
self.qa_outputs = SQuADHead(config)
|
|
|
|
self.apply(self.init_weights)
|
|
|
|
def forward(self, input_ids, lengths=None, positions=None, langs=None, token_type_ids=None,
|
|
attention_mask=None, cache=None, start_positions=None, end_positions=None,
|
|
cls_index=None, is_impossible=None, p_mask=None, head_mask=None):
|
|
|
|
"""
|
|
Performs a model forward pass. **Can be called by calling the class directly, once it has been instantiated.**
|
|
|
|
Args:
|
|
input_ids: a ``torch.LongTensor`` of shape [batch_size, sequence_length]
|
|
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
|
`run_bert_extract_features.py`, `run_bert_classifier.py` and `run_bert_squad.py`)
|
|
lengths: TODO
|
|
positions: TODO
|
|
langs: TODO
|
|
token_type_ids: an optional ``torch.LongTensor`` of shape [batch_size, sequence_length] with the token
|
|
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
|
a `sentence B` token (see XLM paper for more details).
|
|
attention_mask: [optional] float32 Tensor, SAME FUNCTION as `input_mask`
|
|
but with 1 for real tokens and 0 for padding.
|
|
Added for easy compatibility with the XLM model (which uses this negative masking).
|
|
You can only uses one among `input_mask` and `attention_mask`
|
|
cache: TODO
|
|
start_positions: position of the first token for the labeled span: ``torch.LongTensor`` of shape [batch_size].
|
|
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
|
|
into account for computing the loss.
|
|
end_positions: position of the last token for the labeled span: ``torch.LongTensor`` of shape [batch_size].
|
|
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
|
|
into account for computing the loss.
|
|
cls_index: TODO
|
|
is_impossible: TODO
|
|
p_mask: TODO
|
|
head_mask: an optional ``torch.Tensor`` of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
|
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
|
|
|
Returns:
|
|
Either the ``total_loss`` or a ``tuple(start_logits, end_logits)``
|
|
|
|
if ``start_positions`` and ``end_positions`` are not ``None``, \
|
|
outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
|
|
|
|
if ``start_positions`` or ``end_positions`` is ``None``:
|
|
Outputs a ``tuple(start_logits, end_logits)`` which are the logits respectively for the start and end
|
|
position tokens of shape [batch_size, sequence_length].
|
|
|
|
Example::
|
|
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
|
|
# or
|
|
start_logits, end_logits = model.forward(input_ids, token_type_ids, input_mask)
|
|
"""
|
|
|
|
transformer_outputs = self.transformer(input_ids, lengths=lengths, positions=positions, token_type_ids=token_type_ids,
|
|
langs=langs, attention_mask=attention_mask, cache=cache, head_mask=head_mask)
|
|
|
|
output = transformer_outputs[0]
|
|
|
|
outputs = self.qa_outputs(output, start_positions=start_positions, end_positions=end_positions,
|
|
cls_index=cls_index, is_impossible=is_impossible, p_mask=p_mask)
|
|
|
|
outputs = outputs + transformer_outputs[1:] # Keep new_mems and attention/hidden states if they are here
|
|
|
|
return outputs
|