# 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, add_start_docstrings, 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-config.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 @vocab_size.setter def vocab_size(self, value): self.n_words = value @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) XLM_START_DOCSTRING = r""" The XLM model was proposed in `Cross-lingual Language Model Pretraining`_ by Guillaume Lample*, Alexis Conneau*. It's a transformer pre-trained using one of the following objectives: - a causal language modeling (CLM) objective (next token prediction), - a masked language modeling (MLM) objective (Bert-like), or - a Translation Language Modeling (TLM) object (extension of Bert's MLM to multiple language inputs) Original code can be found `here`_. This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. .. _`Cross-lingual Language Model Pretraining`: https://arxiv.org/abs/1901.07291 .. _`torch.nn.Module`: https://pytorch.org/docs/stable/nn.html#module .. _`here`: https://github.com/facebookresearch/XLM Parameters: config (:class:`~pytorch_transformers.XLMConfig`): Model configuration class with all the parameters of the model. """ XLM_INPUTS_DOCSTRING = r""" Inputs: **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`pytorch_transformers.XLMTokenizer`. See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, config.max_position_embeddings - 1[``. **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: A parallel sequence of tokens (can be used to indicate various portions of the inputs). The embeddings from these tokens will be summed with the respective token embeddings. Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices). **langs**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are selected in the pre-trained language vocabulary, i.e. in the range ``[0, config.n_langs - 1[``. **attention_mask**: (`optional`) ``torch.Tensor`` of shape ``(batch_size, sequence_length)``: Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. **lengths**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use `attention_mask` for the same result (see above), kept here for compatbility. Indices selected in ``[0, ..., input_ids.size(-1)]``: **cache**: dictionary with ``torch.FloatTensor`` that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential decoding. The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states. **head_mask**: (`optional`) ``torch.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``: Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**. """ @add_start_docstrings("The bare XLM Model transformer outputing raw hidden-states without any specific head on top.", XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING) class XLMModel(XLMPreTrainedModel): r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the last layer of the model. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: >>> config = XLMConfig.from_pretrained('xlm-mlm-en-2048') >>> tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048') >>> model = XLMModel(config) >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 >>> outputs = model(input_ids) >>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple """ 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) return self.embeddings 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, position_ids=None, langs=None, token_type_ids=None, attention_mask=None, cache=None, head_mask=None): # src_enc=None, src_len=None, 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] # position_ids if position_ids is None: position_ids = input_ids.new((slen,)).long() position_ids = torch.arange(slen, out=position_ids).unsqueeze(0) else: assert position_ids.size() == (bs, slen) # (slen, bs) # position_ids = position_ids.transpose(0, 1) # langs 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:] position_ids = position_ids[:, -_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(position_ids).expand_as(tensor) if langs is not None: tensor = tensor + self.lang_embeddings(langs) if token_type_ids is not None: tensor = tensor + self.embeddings(token_type_ids) 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 @add_start_docstrings("""The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING) class XLMWithLMHeadModel(XLMPreTrainedModel): r""" **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids`` Indices are selected in ``[-1, 0, ..., config.vocab_size]`` All labels set to ``-1`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]`` Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Language modeling loss. **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: >>> config = XLMConfig.from_pretrained('xlm-mlm-en-2048') >>> tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048') >>> model = XLMWithLMHeadModel(config) >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 >>> outputs = model(input_ids) >>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple """ 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 """ self._tie_or_clone_weights(self.pred_layer.proj, self.transformer.embeddings) def forward(self, input_ids, lengths=None, position_ids=None, langs=None, token_type_ids=None, attention_mask=None, cache=None, labels=None, head_mask=None): transformer_outputs = self.transformer(input_ids, lengths=lengths, position_ids=position_ids, 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 @add_start_docstrings("""XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING) class XLMForSequenceClassification(XLMPreTrainedModel): r""" **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ..., config.num_labels]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss), If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy). Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Classification (or regression if config.num_labels==1) loss. **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)`` Classification (or regression if config.num_labels==1) scores (before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: >>> config = XLMConfig.from_pretrained('xlm-mlm-en-2048') >>> tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048') >>> >>> model = XLMForSequenceClassification(config) >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 >>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 >>> outputs = model(input_ids, labels=labels) >>> loss, logits = outputs[:2] """ 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, position_ids=None, langs=None, token_type_ids=None, attention_mask=None, cache=None, labels=None, head_mask=None): transformer_outputs = self.transformer(input_ids, lengths=lengths, position_ids=position_ids, 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 @add_start_docstrings("""XLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING) class XLMForQuestionAnswering(XLMPreTrainedModel): r""" **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. **is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Labels whether a question has an answer or no answer (SQuAD 2.0) **cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Labels for position (index) of the classification token to use as input for computing plausibility of the answer. **p_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...) Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` Span-start scores (before SoftMax). **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` Span-end scores (before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: >>> config = XLMConfig.from_pretrained('xlm-mlm-en-2048') >>> tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048') >>> >>> model = XLMForQuestionAnswering(config) >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 >>> start_positions = torch.tensor([1]) >>> end_positions = torch.tensor([3]) >>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions) >>> loss, start_scores, end_scores = outputs[:2] """ 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, position_ids=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): transformer_outputs = self.transformer(input_ids, lengths=lengths, position_ids=position_ids, 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