# 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. """ PyTorch XLM model. """ from __future__ import absolute_import, division, print_function, unicode_literals import json import logging import math import sys from io import open import itertools import numpy as np import torch from torch import nn from torch.nn import functional as F from torch.nn import CrossEntropyLoss, MSELoss from .modeling_utils import (PretrainedConfig, PreTrainedModel, prune_linear_layer, SequenceSummary, SQuADHead) logger = logging.getLogger(__name__) XLM_PRETRAINED_MODEL_ARCHIVE_MAP = { 'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-pytorch_model.bin", 'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-pytorch_model.bin", 'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-pytorch_model.bin", 'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-pytorch_model.bin", 'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-pytorch_model.bin", 'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-pytorch_model.bin", 'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-pytorch_model.bin", 'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-pytorch_model.bin", } XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = { 'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json", 'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json", 'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-configl.json", 'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json", 'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json", 'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json", 'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json", 'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json", } class XLMConfig(PretrainedConfig): """Configuration class to store the configuration of a `XLMModel`. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`. d_model: Size of the encoder layers and the pooler layer. n_layer: Number of hidden layers in the Transformer encoder. n_head: Number of attention heads for each attention layer in the Transformer encoder. d_inner: The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. ff_activation: The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu" and "swish" are supported. untie_r: untie relative position biases attn_type: 'bi' for XLM, 'uni' for Transformer-XL dropout: The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. dropatt: The dropout ratio for the attention probabilities. max_position_embeddings: 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). initializer_range: The sttdev of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps: The epsilon used by LayerNorm. dropout: float, dropout rate. dropatt: float, dropout rate on attention probabilities. init: str, the initialization scheme, either "normal" or "uniform". init_range: float, initialize the parameters with a uniform distribution in [-init_range, init_range]. Only effective when init="uniform". init_std: float, initialize the parameters with a normal distribution with mean 0 and stddev init_std. Only effective when init="normal". mem_len: int, the number of tokens to cache. reuse_len: int, the number of tokens in the currect batch to be cached and reused in the future. bi_data: bool, whether to use bidirectional input pipeline. Usually set to True during pretraining and False during finetuning. clamp_len: int, clamp all relative distances larger than clamp_len. -1 means no clamping. same_length: bool, whether to use the same attention length for each token. """ pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP def __init__(self, vocab_size_or_config_json_file=30145, emb_dim=2048, n_layers=12, n_heads=16, dropout=0.1, attention_dropout=0.1, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=1, max_position_embeddings=512, embed_init_std=2048 ** -0.5, layer_norm_eps=1e-12, init_std=0.02, bos_index=0, eos_index=1, pad_index=2, unk_index=3, mask_index=5, is_encoder=True, finetuning_task=None, num_labels=2, summary_type='first', summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, start_n_top=5, end_n_top=5, **kwargs): """Constructs XLMConfig. """ super(XLMConfig, self).__init__(**kwargs) if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2 and isinstance(vocab_size_or_config_json_file, unicode)): with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: json_config = json.loads(reader.read()) for key, value in json_config.items(): self.__dict__[key] = value elif isinstance(vocab_size_or_config_json_file, int): self.n_words = vocab_size_or_config_json_file self.emb_dim = emb_dim self.n_layers = n_layers self.n_heads = n_heads self.dropout = dropout self.attention_dropout = attention_dropout self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.causal = causal self.asm = asm self.n_langs = n_langs self.layer_norm_eps = layer_norm_eps self.bos_index = bos_index self.eos_index = eos_index self.pad_index = pad_index self.unk_index = unk_index self.mask_index = mask_index self.is_encoder = is_encoder self.max_position_embeddings = max_position_embeddings self.embed_init_std = embed_init_std self.init_std = init_std self.finetuning_task = finetuning_task self.num_labels = num_labels self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_proj_to_labels = summary_proj_to_labels self.summary_first_dropout = summary_first_dropout self.start_n_top = start_n_top self.end_n_top = end_n_top else: raise ValueError("First argument must be either a vocabulary size (int)" "or the path to a pretrained model config file (str)") @property def vocab_size(self): return self.n_words @property def hidden_size(self): return self.emb_dim @property def num_attention_heads(self): return self.n_heads @property def num_hidden_layers(self): return self.n_layers def create_sinusoidal_embeddings(n_pos, dim, out): position_enc = np.array([ [pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos) ]) out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) out.detach_() out.requires_grad = False def gelu(x): """ GELU activation https://arxiv.org/abs/1606.08415 https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/model_pytorch.py#L14 https://github.com/huggingface/pytorch-transformers/blob/master/modeling.py """ # return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0))) def get_masks(slen, lengths, causal, padding_mask=None): """ Generate hidden states mask, and optionally an attention mask. """ bs = lengths.size(0) if padding_mask is not None: mask = padding_mask else: assert lengths.max().item() <= slen alen = torch.arange(slen, dtype=torch.long, device=lengths.device) mask = alen < lengths[:, None] # attention mask is the same as mask, or triangular inferior attention (causal) if causal: attn_mask = alen[None, None, :].repeat(bs, slen, 1) <= alen[None, :, None] else: attn_mask = mask # sanity check assert mask.size() == (bs, slen) assert causal is False or attn_mask.size() == (bs, slen, slen) return mask, attn_mask class MultiHeadAttention(nn.Module): NEW_ID = itertools.count() def __init__(self, n_heads, dim, config): super(MultiHeadAttention, self).__init__() self.layer_id = next(MultiHeadAttention.NEW_ID) self.output_attentions = config.output_attentions self.dim = dim self.n_heads = n_heads self.dropout = config.attention_dropout assert self.dim % self.n_heads == 0 self.q_lin = nn.Linear(dim, dim) self.k_lin = nn.Linear(dim, dim) self.v_lin = nn.Linear(dim, dim) self.out_lin = nn.Linear(dim, dim) def prune_heads(self, heads): attention_head_size = self.dim // self.n_heads if len(heads) == 0: return mask = torch.ones(self.n_heads, attention_head_size) for head in heads: mask[head] = 0 mask = mask.view(-1).contiguous().eq(1) index = torch.arange(len(mask))[mask].long() # Prune linear layers self.q_lin = prune_linear_layer(self.q_lin, index) self.k_lin = prune_linear_layer(self.k_lin, index) self.v_lin = prune_linear_layer(self.v_lin, index) self.out_lin = prune_linear_layer(self.out_lin, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.dim = attention_head_size * self.n_heads def forward(self, input, mask, kv=None, cache=None, head_mask=None): """ Self-attention (if kv is None) or attention over source sentence (provided by kv). """ # Input is (bs, qlen, dim) # Mask is (bs, klen) (non-causal) or (bs, klen, klen) bs, qlen, dim = input.size() if kv is None: klen = qlen if cache is None else cache['slen'] + qlen else: klen = kv.size(1) # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim) n_heads = self.n_heads dim_per_head = self.dim // n_heads mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen) def shape(x): """ projection """ return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2) def unshape(x): """ compute context """ return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head) q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head) if kv is None: k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head) v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head) elif cache is None or self.layer_id not in cache: k = v = kv k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head) v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head) if cache is not None: if self.layer_id in cache: if kv is None: k_, v_ = cache[self.layer_id] k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head) v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head) else: k, v = cache[self.layer_id] cache[self.layer_id] = (k, v) q = q / math.sqrt(dim_per_head) # (bs, n_heads, qlen, dim_per_head) scores = torch.matmul(q, k.transpose(2, 3)) # (bs, n_heads, qlen, klen) mask = (mask == 0).view(mask_reshape).expand_as(scores) # (bs, n_heads, qlen, klen) scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen) weights = F.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen) weights = F.dropout(weights, p=self.dropout, training=self.training) # (bs, n_heads, qlen, klen) # Mask heads if we want to if head_mask is not None: weights = weights * head_mask context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head) context = unshape(context) # (bs, qlen, dim) outputs = (self.out_lin(context),) if self.output_attentions: outputs = outputs + (weights,) return outputs class TransformerFFN(nn.Module): def __init__(self, in_dim, dim_hidden, out_dim, config): super(TransformerFFN, self).__init__() self.dropout = config.dropout self.lin1 = nn.Linear(in_dim, dim_hidden) self.lin2 = nn.Linear(dim_hidden, out_dim) self.act = gelu if config.gelu_activation else F.relu def forward(self, input): x = self.lin1(input) x = self.act(x) x = self.lin2(x) x = F.dropout(x, p=self.dropout, training=self.training) return x class XLMPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models. """ config_class = XLMConfig pretrained_model_archive_map = XLM_PRETRAINED_MODEL_ARCHIVE_MAP load_tf_weights = None base_model_prefix = "transformer" def __init__(self, *inputs, **kwargs): super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs) def init_weights(self, module): """ Initialize the weights. """ if isinstance(module, nn.Embedding): if self.config is not None and self.config.embed_init_std is not None: nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std) if isinstance(module, nn.Linear): if self.config is not None and self.config.init_std is not None: nn.init.normal_(module.weight, mean=0, std=self.config.init_std) if hasattr(module, 'bias') and module.bias is not None: nn.init.constant_(module.bias, 0.) if isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class XLMModel(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) """ 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: input_ids: TODO lengths: TODO positions: TODO langs: TODO token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs. 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 labels: TODO head_mask: TODO Returns: A ``tuple(logits_or_loss, new_mems)``. If ``labels`` is ``None``, return token logits with shape [batch_size, sequence_length]. If it isn't ``None``, return the ``CrossEntropy`` loss with the targets. ``new_mems`` is a list (num layers) of updated mem states at the entry of each layer \ each mem state is a ``torch.FloatTensor`` of size [self.config.mem_len, batch_size, self.config.d_model] \ Note that the first two dimensions are transposed in ``mems`` with regards to ``input_ids`` and ``labels`` 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) """ 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] logits = self.sequence_summary(output) 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: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs return outputs class XLMForQuestionAnswering(XLMPreTrainedModel): """ 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