# 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) from __future__ import absolute_import, division, print_function, unicode_literals import copy import json import logging import math import os import sys from io import open import math 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 .file_utils import cached_path from .model_utils import CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel logger = logging.getLogger(__name__) PRETRAINED_MODEL_ARCHIVE_MAP = { 'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-pytorch_model.bin", } PRETRAINED_CONFIG_ARCHIVE_MAP = { 'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json", } DECODER_ONLY_PARAMS = [ 'layer_norm15.%i.weight', 'layer_norm15.%i.bias', 'encoder_attn.%i.q_lin.weight', 'encoder_attn.%i.q_lin.bias', 'encoder_attn.%i.k_lin.weight', 'encoder_attn.%i.k_lin.bias', 'encoder_attn.%i.v_lin.weight', 'encoder_attn.%i.v_lin.bias', 'encoder_attn.%i.out_lin.weight', 'encoder_attn.%i.out_lin.bias' ] TRANSFORMER_LAYER_PARAMS = [ 'attentions.%i.q_lin.weight', 'attentions.%i.q_lin.bias', 'attentions.%i.k_lin.weight', 'attentions.%i.k_lin.bias', 'attentions.%i.v_lin.weight', 'attentions.%i.v_lin.bias', 'attentions.%i.out_lin.weight', 'attentions.%i.out_lin.bias', 'layer_norm1.%i.weight', 'layer_norm1.%i.bias', 'ffns.%i.lin1.weight', 'ffns.%i.lin1.bias', 'ffns.%i.lin2.weight', 'ffns.%i.lin2.bias', 'layer_norm2.%i.weight', 'layer_norm2.%i.bias' ] class XLMConfig(PretrainedConfig): """Configuration class to store the configuration of a `XLMModel`. """ pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP def __init__(self, vocab_size_or_config_json_file, d_model=1024, n_layer=24, n_head=16, d_inner=4096, ff_activation="gelu", untie_r=True, attn_type="bi", max_position_embeddings=512, initializer_range=0.02, layer_norm_eps=1e-12, dropout=0.1, dropatt=0.1, init="normal", init_range=0.1, init_std=0.02, mem_len=None, reuse_len=None, bi_data=False, clamp_len=-1, same_length=False): """Constructs XLMConfig. 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. """ 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_token = vocab_size_or_config_json_file self.d_model = d_model self.n_layer = n_layer self.n_head = n_head assert d_model % n_head == 0 self.d_head = d_model // n_head self.ff_activation = ff_activation self.d_inner = d_inner self.untie_r = untie_r self.attn_type = attn_type self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.init = init self.init_range = init_range self.init_std = init_std self.dropout = dropout self.dropatt = dropatt self.mem_len = mem_len self.reuse_len = reuse_len self.bi_data = bi_data self.clamp_len = clamp_len self.same_length = same_length else: raise ValueError("First argument must be either a vocabulary size (int)" "or the path to a pretrained model config file (str)") try: from apex.normalization.fused_layer_norm import FusedLayerNorm as XLMLayerNorm except ImportError: logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .") class XLMLayerNorm(nn.Module): def __init__(self, d_model, eps=1e-12): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(XLMLayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(d_model)) self.bias = nn.Parameter(torch.zeros(d_model)) self.variance_epsilon = eps def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias def Embedding(num_embeddings, embedding_dim, padding_idx=None): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) if padding_idx is not None: nn.init.constant_(m.weight[padding_idx], 0) return m def Linear(in_features, out_features, bias=True): m = nn.Linear(in_features, out_features, bias) # nn.init.normal_(m.weight, mean=0, std=1) # nn.init.xavier_uniform_(m.weight) # nn.init.constant_(m.bias, 0.) return m 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-pretrained-BERT/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): """ Generate hidden states mask, and optionally an attention mask. """ assert lengths.max().item() <= slen bs = lengths.size(0) 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, dropout): super().__init__() self.layer_id = next(MultiHeadAttention.NEW_ID) self.dim = dim self.n_heads = n_heads self.dropout = dropout assert self.dim % self.n_heads == 0 self.q_lin = Linear(dim, dim) self.k_lin = Linear(dim, dim) self.v_lin = Linear(dim, dim) self.out_lin = Linear(dim, dim) def forward(self, input, mask, kv=None, cache=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 = 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) context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head) context = unshape(context) # (bs, qlen, dim) return self.out_lin(context) class TransformerFFN(nn.Module): def __init__(self, in_dim, dim_hidden, out_dim, dropout, gelu_activation): super().__init__() self.dropout = dropout self.lin1 = Linear(in_dim, dim_hidden) self.lin2 = Linear(dim_hidden, out_dim) self.act = gelu if 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 BeamHypotheses(object): def __init__(self, n_hyp, max_len, length_penalty, early_stopping): """ Initialize n-best list of hypotheses. """ self.max_len = max_len - 1 # ignoring self.length_penalty = length_penalty self.early_stopping = early_stopping self.n_hyp = n_hyp self.hyp = [] self.worst_score = 1e9 def __len__(self): """ Number of hypotheses in the list. """ return len(self.hyp) def add(self, hyp, sum_logprobs): """ Add a new hypothesis to the list. """ score = sum_logprobs / len(hyp) ** self.length_penalty if len(self) < self.n_hyp or score > self.worst_score: self.hyp.append((score, hyp)) if len(self) > self.n_hyp: sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.hyp)]) del self.hyp[sorted_scores[0][1]] self.worst_score = sorted_scores[1][0] else: self.worst_score = min(score, self.worst_score) def is_done(self, best_sum_logprobs): """ If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst one in the heap, then we are done with this sentence. """ if len(self) < self.n_hyp: return False elif self.early_stopping: return True else: return self.worst_score >= best_sum_logprobs / self.max_len ** self.length_penalty 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 = PRETRAINED_MODEL_ARCHIVE_MAP pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP load_tf_weights = None base_model_prefix = "xlm" def __init__(self, *inputs, **kwargs): super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs) def init_weights(self, module): """ Initialize the weights. """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, XLMLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, XLMRelativeAttention): for param in [module.q, module.k, module.v, module.o, module.r, module.r_r_bias, module.r_s_bias, module.r_w_bias, module.seg_embed]: param.data.normal_(mean=0.0, std=self.config.initializer_range) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() class XLMModel(XLMPreTrainedModel): 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, params, output_attentions=False, keep_multihead_output=False): #, dico, is_encoder, with_output): """ Transformer model (encoder or decoder). """ super(XLMModel, self).__init__(params) self.output_attentions = output_attentions # encoder / decoder, output layer # self.is_encoder = is_encoder # self.is_decoder = not is_encoder # self.with_output = with_output # dictionary / languages self.n_langs = params.n_langs self.n_words = params.n_words self.eos_index = params.eos_index self.pad_index = params.pad_index # self.dico = dico self.id2lang = params.id2lang self.lang2id = params.lang2id # assert len(self.dico) == self.n_words assert len(self.id2lang) == len(self.lang2id) == self.n_langs # model parameters self.dim = params.emb_dim # 512 by default self.hidden_dim = self.dim * 4 # 2048 by default self.n_heads = params.n_heads # 8 by default self.n_layers = params.n_layers self.dropout = params.dropout self.attention_dropout = params.attention_dropout assert self.dim % self.n_heads == 0, 'transformer dim must be a multiple of n_heads' # embeddings self.position_embeddings = Embedding(params.max_position_embeddings, self.dim) if params.sinusoidal_embeddings: create_sinusoidal_embeddings(params.max_position_embeddings, self.dim, out=self.position_embeddings.weight) if params.n_langs > 1: self.lang_embeddings = Embedding(self.n_langs, self.dim) self.embeddings = Embedding(self.n_words, self.dim, padding_idx=self.pad_index) self.layer_norm_emb = nn.LayerNorm(self.dim, eps=1e-12) # 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, dropout=self.attention_dropout)) self.layer_norm1.append(nn.LayerNorm(self.dim, eps=1e-12)) if self.is_decoder: self.layer_norm15.append(nn.LayerNorm(self.dim, eps=1e-12)) 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, dropout=self.dropout, gelu_activation=params.gelu_activation)) self.layer_norm2.append(nn.LayerNorm(self.dim, eps=1e-12)) # output layer # if self.with_output: # self.pred_layer = PredLayer(params) # if params.share_inout_emb: # self.pred_layer.proj.weight = self.embeddings.weight # def forward(self, mode, **kwargs): # """ # Forward function with different forward modes. # ### Small hack to handle PyTorch distributed. # """ # if mode == 'fwd': # return self.fwd(**kwargs) # elif mode == 'predict': # return self.predict(**kwargs) # else: # raise Exception("Unknown mode: %s" % mode) def forward(self, x, lengths, causal, src_enc=None, src_len=None, positions=None, langs=None, cache=None): """ Inputs: `x` LongTensor(slen, bs), containing word indices `lengths` LongTensor(bs), containing the length of each sentence `causal` Boolean, if True, the attention is only done over previous hidden states `positions` LongTensor(slen, bs), containing word positions `langs` LongTensor(slen, bs), containing language IDs """ # lengths = (x != self.pad_index).float().sum(dim=1) # mask = x != self.pad_index # check inputs slen, bs = x.size() assert lengths.size(0) == bs assert lengths.max().item() <= slen x = x.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, causal) 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 = x.new(slen).long() positions = torch.arange(slen, out=positions).unsqueeze(0) else: assert positions.size() == (slen, bs) positions = positions.transpose(0, 1) # langs if langs is not None: assert langs.size() == (slen, bs) langs = langs.transpose(0, 1) # do not recompute cached elements if cache is not None: _slen = slen - cache['slen'] x = x[:, -_slen:] positions = positions[:, -_slen:] if langs is not None: langs = langs[:, -_slen:] mask = mask[:, -_slen:] attn_mask = attn_mask[:, -_slen:] # embeddings tensor = self.embeddings(x) 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 for i in range(self.n_layers): # self attention attn = self.attentions[i](tensor, attn_mask, cache=cache) 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) # 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) return tensor def predict(self, tensor, pred_mask, y, get_scores): """ Given the last hidden state, compute word scores and/or the loss. `pred_mask` is a ByteTensor of shape (slen, bs), filled with 1 when we need to predict a word `y` is a LongTensor of shape (pred_mask.sum(),) `get_scores` is a boolean specifying whether we need to return scores """ masked_tensor = tensor[pred_mask.unsqueeze(-1).expand_as(tensor)].view(-1, self.dim) scores, loss = self.pred_layer(masked_tensor, y, get_scores) return scores, loss def generate(self, src_enc, src_len, tgt_lang_id, max_len=200, sample_temperature=None): """ Decode a sentence given initial start. `x`: - LongTensor(bs, slen) W1 W2 W3 W1 W2 W3 W4 `lengths`: - LongTensor(bs) [5, 6] `positions`: - False, for regular "arange" positions (LM) - True, to reset positions from the new generation (MT) `langs`: - must be None if the model only supports one language - lang_id if only one language is involved (LM) - (lang_id1, lang_id2) if two languages are involved (MT) """ # input batch bs = len(src_len) assert src_enc.size(0) == bs # generated sentences generated = src_len.new(max_len, bs) # upcoming output generated.fill_(self.pad_index) # fill upcoming ouput with generated[0].fill_(self.eos_index) # we use for everywhere # positions positions = src_len.new(max_len).long() positions = torch.arange(max_len, out=positions).unsqueeze(1).expand(max_len, bs) # language IDs langs = src_len.new(max_len).long().fill_(tgt_lang_id) langs = langs.unsqueeze(1).expand(max_len, bs) # current position / max lengths / length of generated sentences / unfinished sentences cur_len = 1 gen_len = src_len.clone().fill_(1) unfinished_sents = src_len.clone().fill_(1) # cache compute states cache = {'slen': 0} while cur_len < max_len: # compute word scores tensor = self.forward( 'fwd', x=generated[:cur_len], lengths=gen_len, positions=positions[:cur_len], langs=langs[:cur_len], causal=True, src_enc=src_enc, src_len=src_len, cache=cache ) assert tensor.size() == (1, bs, self.dim) tensor = tensor.data[-1, :, :] # (bs, dim) scores = self.pred_layer.get_scores(tensor) # (bs, n_words) # select next words: sample or greedy if sample_temperature is None: next_words = torch.topk(scores, 1)[1].squeeze(1) else: next_words = torch.multinomial(F.softmax(scores / sample_temperature, dim=1), 1).squeeze(1) assert next_words.size() == (bs,) # update generations / lengths / finished sentences / current length generated[cur_len] = next_words * unfinished_sents + self.pad_index * (1 - unfinished_sents) gen_len.add_(unfinished_sents) unfinished_sents.mul_(next_words.ne(self.eos_index).long()) cur_len = cur_len + 1 # stop when there is a in each sentence, or if we exceed the maximul length if unfinished_sents.max() == 0: break # add to unfinished sentences if cur_len == max_len: generated[-1].masked_fill_(unfinished_sents.byte(), self.eos_index) # sanity check assert (generated == self.eos_index).sum() == 2 * bs return generated[:cur_len], gen_len def generate_beam(self, src_enc, src_len, tgt_lang_id, beam_size, length_penalty, early_stopping, max_len=200): """ Decode a sentence given initial start. `x`: - LongTensor(bs, slen) W1 W2 W3 W1 W2 W3 W4 `lengths`: - LongTensor(bs) [5, 6] `positions`: - False, for regular "arange" positions (LM) - True, to reset positions from the new generation (MT) `langs`: - must be None if the model only supports one language - lang_id if only one language is involved (LM) - (lang_id1, lang_id2) if two languages are involved (MT) """ # check inputs assert src_enc.size(0) == src_len.size(0) assert beam_size >= 1 # batch size / number of words bs = len(src_len) n_words = self.n_words # expand to beam size the source latent representations / source lengths src_enc = src_enc.unsqueeze(1).expand((bs, beam_size) + src_enc.shape[1:]).contiguous().view((bs * beam_size,) + src_enc.shape[1:]) src_len = src_len.unsqueeze(1).expand(bs, beam_size).contiguous().view(-1) # generated sentences (batch with beam current hypotheses) generated = src_len.new(max_len, bs * beam_size) # upcoming output generated.fill_(self.pad_index) # fill upcoming ouput with generated[0].fill_(self.eos_index) # we use for everywhere # generated hypotheses generated_hyps = [BeamHypotheses(beam_size, max_len, length_penalty, early_stopping) for _ in range(bs)] # positions positions = src_len.new(max_len).long() positions = torch.arange(max_len, out=positions).unsqueeze(1).expand_as(generated) # language IDs langs = positions.clone().fill_(tgt_lang_id) # scores for each sentence in the beam beam_scores = src_enc.new(bs, beam_size).fill_(0) beam_scores[:, 1:] = -1e9 beam_scores = beam_scores.view(-1) # current position cur_len = 1 # cache compute states cache = {'slen': 0} # done sentences done = [False for _ in range(bs)] while cur_len < max_len: # compute word scores tensor = self.forward( 'fwd', x=generated[:cur_len], lengths=src_len.new(bs * beam_size).fill_(cur_len), positions=positions[:cur_len], langs=langs[:cur_len], causal=True, src_enc=src_enc, src_len=src_len, cache=cache ) assert tensor.size() == (1, bs * beam_size, self.dim) tensor = tensor.data[-1, :, :] # (bs * beam_size, dim) scores = self.pred_layer.get_scores(tensor) # (bs * beam_size, n_words) scores = F.log_softmax(scores, dim=-1) # (bs * beam_size, n_words) assert scores.size() == (bs * beam_size, n_words) # select next words with scores _scores = scores + beam_scores[:, None].expand_as(scores) # (bs * beam_size, n_words) _scores = _scores.view(bs, beam_size * n_words) # (bs, beam_size * n_words) next_scores, next_words = torch.topk(_scores, 2 * beam_size, dim=1, largest=True, sorted=True) assert next_scores.size() == next_words.size() == (bs, 2 * beam_size) # next batch beam content # list of (bs * beam_size) tuple(next hypothesis score, next word, current position in the batch) next_batch_beam = [] # for each sentence for sent_id in range(bs): # if we are done with this sentence done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item()) if done[sent_id]: next_batch_beam.extend([(0, self.pad_index, 0)] * beam_size) # pad the batch continue # next sentence beam content next_sent_beam = [] # next words for this sentence for idx, value in zip(next_words[sent_id], next_scores[sent_id]): # get beam and word IDs beam_id = idx // n_words word_id = idx % n_words # end of sentence, or next word if word_id == self.eos_index or cur_len + 1 == max_len: generated_hyps[sent_id].add(generated[:cur_len, sent_id * beam_size + beam_id].clone(), value.item()) else: next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id)) # the beam for next step is full if len(next_sent_beam) == beam_size: break # update next beam content assert len(next_sent_beam) == 0 if cur_len + 1 == max_len else beam_size if len(next_sent_beam) == 0: next_sent_beam = [(0, self.pad_index, 0)] * beam_size # pad the batch next_batch_beam.extend(next_sent_beam) assert len(next_batch_beam) == beam_size * (sent_id + 1) # sanity check / prepare next batch assert len(next_batch_beam) == bs * beam_size beam_scores = beam_scores.new([x[0] for x in next_batch_beam]) beam_words = generated.new([x[1] for x in next_batch_beam]) beam_idx = src_len.new([x[2] for x in next_batch_beam]) # re-order batch and internal states generated = generated[:, beam_idx] generated[cur_len] = beam_words for k in cache.keys(): if k != 'slen': cache[k] = (cache[k][0][beam_idx], cache[k][1][beam_idx]) # update current length cur_len = cur_len + 1 # stop when we are done with each sentence if all(done): break # visualize hypotheses # print([len(x) for x in generated_hyps], cur_len) # globals().update( locals() ); # !import code; code.interact(local=vars()) # for ii in range(bs): # for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True): # print("%.3f " % ss + " ".join(self.dico[x] for x in ww.tolist())) # print("") # select the best hypotheses tgt_len = src_len.new(bs) best = [] for i, hypotheses in enumerate(generated_hyps): best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1] tgt_len[i] = len(best_hyp) + 1 # +1 for the symbol best.append(best_hyp) # generate target batch decoded = src_len.new(tgt_len.max().item(), bs).fill_(self.pad_index) for i, hypo in enumerate(best): decoded[:tgt_len[i] - 1, i] = hypo decoded[tgt_len[i] - 1, i] = self.eos_index # sanity check assert (decoded == self.eos_index).sum() == 2 * bs return decoded, tgt_len class XLMModel(XLMPreTrainedModel): def __init__(self, config, output_attentions=False, keep_multihead_output=False): super(XLMModel, self).__init__(config) self.output_attentions = output_attentions self.mem_len = config.mem_len self.reuse_len = config.reuse_len self.d_model = config.d_model self.same_length = config.same_length self.attn_type = config.attn_type self.bi_data = config.bi_data self.clamp_len = config.clamp_len self.word_embedding = nn.Embedding(config.n_token, config.d_model) self.mask_emb = nn.Parameter(torch.Tensor(1, 1, config.d_model)) layer = XLMLayer(config, output_attentions=output_attentions, keep_multihead_output=keep_multihead_output) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.n_layer)]) self.dropout = nn.Dropout(config.dropout) 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} """ for layer, heads in heads_to_prune.items(): self.layer[layer].attention.prune_heads(heads) def get_multihead_outputs(self): """ Gather all multi-head outputs. Return: list (layers) of multihead module outputs with gradients """ return [layer.attention.self.multihead_output for layer in self.layer] def create_mask(self, qlen, mlen): """ create causal attention mask. float mask where 1.0 indicate masked, 0.0 indicated not-masked. same_length=False: same_length=True: < qlen > < qlen > ^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1] qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1] [0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1] v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0] """ attn_mask = torch.ones([qlen, qlen]) mask_up = torch.triu(attn_mask, diagonal=1) attn_mask_pad = torch.zeros([qlen, mlen]) ret = torch.cat([attn_mask_pad, mask_up], dim=1) if self.same_length: mask_lo = torch.tril(attn_mask, diagonal=-1) ret = torch.cat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], dim=1) ret = ret.to(next(self.parameters())) return ret def cache_mem(self, curr_out, prev_mem): """cache hidden states into memory.""" if self.mem_len is None or self.mem_len == 0: return None else: if self.reuse_len is not None and self.reuse_len > 0: curr_out = curr_out[:self.reuse_len] if prev_mem is None: new_mem = curr_out[-self.mem_len:] else: new_mem = torch.cat([prev_mem, curr_out], dim=0)[-self.mem_len:] return new_mem.detach() @staticmethod def positional_embedding(pos_seq, inv_freq, bsz=None): sinusoid_inp = torch.einsum('i,d->id', pos_seq, inv_freq) pos_emb = torch.cat([torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)], dim=-1) pos_emb = pos_emb[:, None, :] if bsz is not None: pos_emb = pos_emb.expand(-1, bsz, -1) return pos_emb def relative_positional_encoding(self, qlen, klen, bsz=None): """create relative positional encoding.""" freq_seq = torch.arange(0, self.d_model, 2.0, dtype=torch.float) inv_freq = 1 / (10000 ** (freq_seq / self.d_model)) if self.attn_type == 'bi': # beg, end = klen - 1, -qlen beg, end = klen, -qlen elif self.attn_type == 'uni': # beg, end = klen - 1, -1 beg, end = klen, -1 else: raise ValueError('Unknown `attn_type` {}.'.format(self.attn_type)) if self.bi_data: fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.float) bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.float) if self.clamp_len > 0: fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len) bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len) if bsz is not None: fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz//2) bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz//2) else: fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq) bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq) pos_emb = torch.cat([fwd_pos_emb, bwd_pos_emb], dim=1) else: fwd_pos_seq = torch.arange(beg, end, -1.0) if self.clamp_len > 0: fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len) pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz) pos_emb = pos_emb.to(next(self.parameters())) return pos_emb def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None, inp_q=None, output_all_encoded_layers=True, head_mask=None): """ Args: inp_k: int32 Tensor in shape [bsz, len], the input token IDs. token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs. input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask. 0 for real tokens and 1 for padding. 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 BERT model (which uses this negative masking). You can only uses one among `input_mask` and `attention_mask` mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory from previous batches. The length of the list equals n_layer. If None, no memory is used. perm_mask: [optional] float32 Tensor in shape [bsz, len, len]. If perm_mask[k, i, j] = 0, i attend to j in batch k; if perm_mask[k, i, j] = 1, i does not attend to j in batch k. If None, each position attends to all the others. target_mapping: [optional] float32 Tensor in shape [bsz, num_predict, len]. If target_mapping[k, i, j] = 1, the i-th predict in batch k is on the j-th token. Only used during pretraining for partial prediction. Set to None during finetuning. inp_q: [optional] float32 Tensor in shape [bsz, len]. 1 for tokens with losses and 0 for tokens without losses. Only used during pretraining for two-stream attention. Set to None during finetuning. 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. summary_type: str, "last", "first", "mean", or "attn". The method to pool the input to get a vector representation. """ # the original code for XLM uses shapes [len, bsz] with the batch dimension at the end # but we want a unified interface in the library with the batch size on the first dimension # so we move here the first dimension (batch) to the end inp_k = inp_k.transpose(0, 1).contiguous() token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None inp_q = inp_q.transpose(0, 1).contiguous() if inp_q is not None else None qlen, bsz = inp_k.shape[0], inp_k.shape[1] mlen = mems[0].shape[0] if mems is not None else 0 klen = mlen + qlen dtype_float = next(self.parameters()).dtype device = next(self.parameters()).device ##### Attention mask # causal attention mask if self.attn_type == 'uni': attn_mask = self.create_mask(qlen, mlen) attn_mask = attn_mask[:, :, None, None] elif self.attn_type == 'bi': attn_mask = None else: raise ValueError('Unsupported attention type: {}'.format(self.attn_type)) # data mask: input mask & perm mask assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) " "or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one." if input_mask is None and attention_mask is not None: input_mask = 1.0 - attention_mask if input_mask is not None and perm_mask is not None: data_mask = input_mask[None] + perm_mask elif input_mask is not None and perm_mask is None: data_mask = input_mask[None] elif input_mask is None and perm_mask is not None: data_mask = perm_mask else: data_mask = None if data_mask is not None: # all mems can be attended to mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask) data_mask = torch.cat([mems_mask, data_mask], dim=1) if attn_mask is None: attn_mask = data_mask[:, :, :, None] else: attn_mask += data_mask[:, :, :, None] if attn_mask is not None: attn_mask = (attn_mask > 0).to(dtype_float) if attn_mask is not None: non_tgt_mask = -torch.eye(qlen).to(attn_mask) non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1) non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask) else: non_tgt_mask = None ##### Word embeddings and prepare h & g hidden states word_emb_k = self.word_embedding(inp_k) output_h = self.dropout(word_emb_k) if inp_q is not None: if target_mapping is not None: word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1) else: inp_q_ext = inp_q[:, :, None] word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k output_g = self.dropout(word_emb_q) else: output_g = None ##### Segment embedding if token_type_ids is not None: # Convert `token_type_ids` to one-hot `seg_mat` mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device) cat_ids = torch.cat([mem_pad, token_type_ids], dim=0) # `1` indicates not in the same segment [qlen x klen x bsz] seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long() seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float) else: seg_mat = None ##### Positional encoding pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz) pos_emb = self.dropout(pos_emb) ##### Head mask if needed (for bertology/pruning) # 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 [n_layer x num_heads] # and head_mask is converted to shape [n_layer x batch x num_heads x seq_length x seq_length] 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.config.n_layer, -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.config.n_layer new_mems = [] if mems is None: mems = [None] * len(self.layer) hidden_states = [] for i, layer_module in enumerate(self.layer): # cache new mems new_mems.append(self.cache_mem(output_h, mems[i])) output_h, output_g = layer_module(output_h, output_g, attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask, r=pos_emb, seg_mat=seg_mat, mems=mems[i], target_mapping=target_mapping, head_mask=head_mask) hidden_states.append(output_h) output = self.dropout(output_g if output_g is not None else output_h) # We transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method) output = output.permute(1, 0, 2).contiguous() hidden_states = [hs.permute(1, 0, 2).contiguous() for hs in hidden_states] return output, hidden_states, new_mems class XLMPredLayer(nn.Module): """ Prediction layer (cross_entropy or adaptive_softmax). """ def __init__(self, params): super().__init__() self.asm = params.asm self.n_words = params.n_words self.pad_index = params.pad_index dim = params.emb_dim if params.asm is False: self.proj = Linear(dim, params.n_words, bias=True) else: self.proj = nn.AdaptiveLogSoftmaxWithLoss( in_features=dim, n_classes=params.n_words, cutoffs=params.asm_cutoffs, div_value=params.asm_div_value, head_bias=True, # default is False ) def forward(self, x, y, get_scores=False): """ Compute the loss, and optionally the scores. """ assert (y == self.pad_index).sum().item() == 0 if self.asm is False: scores = self.proj(x).view(-1, self.n_words) loss = F.cross_entropy(scores, y, reduction='elementwise_mean') else: _, loss = self.proj(x, y) scores = self.proj.log_prob(x) if get_scores else None return scores, loss def get_scores(self, x): """ Compute scores. """ assert x.dim() == 2 return self.proj.log_prob(x) if self.asm else self.proj(x) class XLMLMHeadModel(XLMPreTrainedModel): """XLM model ("XLM: Generalized Autoregressive Pretraining for Language Understanding"). Params: `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 Inputs: inp_k: int32 Tensor in shape [bsz, len], the input token IDs. token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs. input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask. 0 for real tokens and 1 for padding. 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 BERT model (which uses this negative masking). You can only uses one among `input_mask` and `attention_mask` mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory from previous batches. The length of the list equals n_layer. If None, no memory is used. perm_mask: [optional] float32 Tensor in shape [bsz, len, len]. If perm_mask[k, i, j] = 0, i attend to j in batch k; if perm_mask[k, i, j] = 1, i does not attend to j in batch k. If None, each position attends to all the others. target_mapping: [optional] float32 Tensor in shape [bsz, num_predict, len]. If target_mapping[k, i, j] = 1, the i-th predict in batch k is on the j-th token. Only used during pretraining for partial prediction. Set to None during finetuning. inp_q: [optional] float32 Tensor in shape [bsz, len]. 1 for tokens with losses and 0 for tokens without losses. Only used during pretraining for two-stream attention. Set to None during finetuning. Outputs: Tuple of (encoded_layers, pooled_output) `encoded_layers`: controled 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, d_model], - `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, d_model], `pooled_output`: a torch.FloatTensor of size [batch_size, d_model] which is the output of a classifier pretrained 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 usage: ```python # 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]]) 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) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, output_attentions=False, keep_multihead_output=False): super(XLMLMHeadModel, self).__init__(config) self.output_attentions = output_attentions self.attn_type = config.attn_type self.same_length = config.same_length self.transformer = XLMModel(config, output_attentions=output_attentions, keep_multihead_output=keep_multihead_output) self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True) # Tie weights self.apply(self.init_weights) self.tie_weights() def tie_weights(self): """ Make sure we are sharing the embeddings """ self.lm_loss.weight = self.transformer.word_embedding.weight def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None, inp_q=None, labels=None, output_all_encoded_layers=True, head_mask=None): """ Args: inp_k: int32 Tensor in shape [bsz, len], the input token IDs. token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs. input_mask: float32 Tensor in shape [bsz, len], the input mask. 0 for real tokens and 1 for padding. 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 BERT model (which uses this negative masking). You can only uses one among `input_mask` and `attention_mask` mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory from previous batches. The length of the list equals n_layer. If None, no memory is used. perm_mask: float32 Tensor in shape [bsz, len, len]. If perm_mask[k, i, j] = 0, i attend to j in batch k; if perm_mask[k, i, j] = 1, i does not attend to j in batch k. If None, each position attends to all the others. target_mapping: float32 Tensor in shape [bsz, num_predict, len]. If target_mapping[k, i, j] = 1, the i-th predict in batch k is on the j-th token. Only used during pretraining for partial prediction. Set to None during finetuning. inp_q: float32 Tensor in shape [bsz, len]. 1 for tokens with losses and 0 for tokens without losses. Only used during pretraining for two-stream attention. Set to None during finetuning. summary_type: str, "last", "first", "mean", or "attn". The method to pool the input to get a vector representation. """ output, hidden_states, new_mems = self.transformer(inp_k, token_type_ids, input_mask, attention_mask, mems, perm_mask, target_mapping, inp_q, output_all_encoded_layers, head_mask) logits = self.lm_loss(output) if labels is not None: # Flatten the tokens loss_fct = CrossEntropyLoss(ignore_index=-1) loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1)) return loss, new_mems # if self.output_attentions: # all_attentions, encoded_layers = encoded_layers # sequence_output = encoded_layers[-1] # pooled_output = self.pooler(sequence_output) # if not output_all_encoded_layers: # encoded_layers = encoded_layers[-1] # if self.output_attentions: return logits, new_mems # return all_attentions, encoded_layers, pooled_output class XLMSequenceSummary(nn.Module): def __init__(self, config, summary_type="last", use_proj=True, output_attentions=False, keep_multihead_output=False): super(XLMSequenceSummary, self).__init__() self.summary_type = summary_type if use_proj: self.summary = nn.Linear(config.d_model, config.d_model) else: self.summary = None if summary_type == 'attn': # We should use a standard multi-head attention module with absolute positional embedding for that. # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 # We can probably just use the multi-head attention module of PyTorch >=1.1.0 raise NotImplementedError self.dropout = nn.Dropout(config.dropout) self.activation = nn.Tanh() def forward(self, hidden_states): """ hidden_states: float Tensor in shape [bsz, seq_len, d_model], the hidden-states of the last layer.""" if self.summary_type == 'last': output = hidden_states[:, -1] elif self.summary_type == 'first': output = hidden_states[:, 0] elif self.summary_type == 'mean': output = hidden_states.mean(dim=1) elif summary_type == 'attn': raise NotImplementedError output = self.summary(output) output = self.activation(output) output = self.dropout(output) return output class XLMForSequenceClassification(XLMPreTrainedModel): """XLM model ("XLM: Generalized Autoregressive Pretraining for Language Understanding"). Params: `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 Inputs: inp_k: int32 Tensor in shape [bsz, len], the input token IDs. token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs. input_mask: float32 Tensor in shape [bsz, len], the input mask. 0 for real tokens and 1 for padding. 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 BERT model (which uses this negative masking). You can only uses one among `input_mask` and `attention_mask` mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory from previous batches. The length of the list equals n_layer. If None, no memory is used. perm_mask: float32 Tensor in shape [bsz, len, len]. If perm_mask[k, i, j] = 0, i attend to j in batch k; if perm_mask[k, i, j] = 1, i does not attend to j in batch k. If None, each position attends to all the others. target_mapping: float32 Tensor in shape [bsz, num_predict, len]. If target_mapping[k, i, j] = 1, the i-th predict in batch k is on the j-th token. Only used during pretraining for partial prediction. Set to None during finetuning. inp_q: float32 Tensor in shape [bsz, len]. 1 for tokens with losses and 0 for tokens without losses. Only used during pretraining for two-stream attention. Set to None during finetuning. `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. Outputs: Tuple of (logits or loss, mems) `logits or loss`: if labels is None: Token logits with shape [batch_size, sequence_length] else: CrossEntropy loss with the targets `new_mems`: 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 usage: ```python # 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]]) 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) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, summary_type="last", use_proj=True, num_labels=2, output_attentions=False, keep_multihead_output=False): super(XLMForSequenceClassification, self).__init__(config) self.output_attentions = output_attentions self.attn_type = config.attn_type self.same_length = config.same_length self.summary_type = summary_type self.num_labels = num_labels self.transformer = XLMModel(config, output_attentions=output_attentions, keep_multihead_output=keep_multihead_output) self.sequence_summary = XLMSequenceSummary(config, summary_type=summary_type, use_proj=use_proj, output_attentions=output_attentions, keep_multihead_output=keep_multihead_output) self.logits_proj = nn.Linear(config.d_model, num_labels) self.apply(self.init_weights) def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None, inp_q=None, labels=None, output_all_encoded_layers=True, head_mask=None): """ Args: inp_k: int32 Tensor in shape [bsz, len], the input token IDs. token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs. input_mask: float32 Tensor in shape [bsz, len], the input mask. 0 for real tokens and 1 for padding. 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 BERT model (which uses this negative masking). You can only uses one among `input_mask` and `attention_mask` mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory from previous batches. The length of the list equals n_layer. If None, no memory is used. perm_mask: float32 Tensor in shape [bsz, len, len]. If perm_mask[k, i, j] = 0, i attend to j in batch k; if perm_mask[k, i, j] = 1, i does not attend to j in batch k. If None, each position attends to all the others. target_mapping: float32 Tensor in shape [bsz, num_predict, len]. If target_mapping[k, i, j] = 1, the i-th predict in batch k is on the j-th token. Only used during pretraining for partial prediction. Set to None during finetuning. inp_q: float32 Tensor in shape [bsz, len]. 1 for tokens with losses and 0 for tokens without losses. Only used during pretraining for two-stream attention. Set to None during finetuning. """ output, _, new_mems = self.transformer(inp_k, token_type_ids, input_mask, attention_mask, mems, perm_mask, target_mapping, inp_q, output_all_encoded_layers, head_mask) output = self.sequence_summary(output) logits = self.logits_proj(output) 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)) return loss, new_mems # if self.output_attentions: # all_attentions, encoded_layers = encoded_layers # sequence_output = encoded_layers[-1] # pooled_output = self.pooler(sequence_output) # if not output_all_encoded_layers: # encoded_layers = encoded_layers[-1] # if self.output_attentions: return logits, new_mems # return all_attentions, encoded_layers, pooled_output 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 Params: `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 Inputs: `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`) `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 BERT model (which uses this negative masking). You can only uses one among `input_mask` and `attention_mask` `input_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. `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. `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. Outputs: 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 of start_logits, end_logits which are the logits respectively for the start and end position tokens of shape [batch_size, sequence_length]. Example usage: ```python # 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]]) 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) start_logits, end_logits = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, output_attentions=False, keep_multihead_output=False): super(XLMForQuestionAnswering, self).__init__(config) self.output_attentions = output_attentions self.transformer = XLMModel(config, output_attentions=output_attentions, keep_multihead_output=keep_multihead_output) self.qa_outputs = nn.Linear(config.hidden_size, 2) self.apply(self.init_weights) def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None, inp_q=None, start_positions=None, end_positions=None, output_all_encoded_layers=True, head_mask=None): output, _, new_mems = self.transformer(inp_k, token_type_ids, input_mask, attention_mask, mems, perm_mask, target_mapping, inp_q, output_all_encoded_layers, head_mask) logits = self.qa_outputs(output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 return total_loss elif self.output_attentions: return all_attentions, start_logits, end_logits return start_logits, end_logits