# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # 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 XLNet model. """ import warnings from dataclasses import dataclass from typing import List, Optional, Tuple, Union import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...generation import GenerationMixin from ...modeling_utils import PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits, PreTrainedModel, SequenceSummary from ...pytorch_utils import apply_chunking_to_forward from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_xlnet import XLNetConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "xlnet/xlnet-base-cased" _CONFIG_FOR_DOC = "XLNetConfig" def build_tf_xlnet_to_pytorch_map(model, config, tf_weights=None): """ A map of modules from TF to PyTorch. I use a map to keep the PyTorch model as identical to the original PyTorch model as possible. """ tf_to_pt_map = {} if hasattr(model, "transformer"): if hasattr(model, "lm_loss"): # We will load also the output bias tf_to_pt_map["model/lm_loss/bias"] = model.lm_loss.bias if hasattr(model, "sequence_summary") and "model/sequnece_summary/summary/kernel" in tf_weights: # We will load also the sequence summary tf_to_pt_map["model/sequnece_summary/summary/kernel"] = model.sequence_summary.summary.weight tf_to_pt_map["model/sequnece_summary/summary/bias"] = model.sequence_summary.summary.bias if ( hasattr(model, "logits_proj") and config.finetuning_task is not None and f"model/regression_{config.finetuning_task}/logit/kernel" in tf_weights ): tf_to_pt_map[f"model/regression_{config.finetuning_task}/logit/kernel"] = model.logits_proj.weight tf_to_pt_map[f"model/regression_{config.finetuning_task}/logit/bias"] = model.logits_proj.bias # Now load the rest of the transformer model = model.transformer # Embeddings and output tf_to_pt_map.update( { "model/transformer/word_embedding/lookup_table": model.word_embedding.weight, "model/transformer/mask_emb/mask_emb": model.mask_emb, } ) # Transformer blocks for i, b in enumerate(model.layer): layer_str = f"model/transformer/layer_{i}/" tf_to_pt_map.update( { layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight, layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias, layer_str + "rel_attn/o/kernel": b.rel_attn.o, layer_str + "rel_attn/q/kernel": b.rel_attn.q, layer_str + "rel_attn/k/kernel": b.rel_attn.k, layer_str + "rel_attn/r/kernel": b.rel_attn.r, layer_str + "rel_attn/v/kernel": b.rel_attn.v, layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight, layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias, layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight, layer_str + "ff/layer_1/bias": b.ff.layer_1.bias, layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight, layer_str + "ff/layer_2/bias": b.ff.layer_2.bias, } ) # Relative positioning biases if config.untie_r: r_r_list = [] r_w_list = [] r_s_list = [] seg_embed_list = [] for b in model.layer: r_r_list.append(b.rel_attn.r_r_bias) r_w_list.append(b.rel_attn.r_w_bias) r_s_list.append(b.rel_attn.r_s_bias) seg_embed_list.append(b.rel_attn.seg_embed) else: r_r_list = [model.r_r_bias] r_w_list = [model.r_w_bias] r_s_list = [model.r_s_bias] seg_embed_list = [model.seg_embed] tf_to_pt_map.update( { "model/transformer/r_r_bias": r_r_list, "model/transformer/r_w_bias": r_w_list, "model/transformer/r_s_bias": r_s_list, "model/transformer/seg_embed": seg_embed_list, } ) return tf_to_pt_map def load_tf_weights_in_xlnet(model, config, tf_path): """Load tf checkpoints in a pytorch model""" try: import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise # Load weights from TF model init_vars = tf.train.list_variables(tf_path) tf_weights = {} for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) tf_weights[name] = array # Build TF to PyTorch weights loading map tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights) for name, pointer in tf_to_pt_map.items(): logger.info(f"Importing {name}") if name not in tf_weights: logger.info(f"{name} not in tf pre-trained weights, skipping") continue array = tf_weights[name] # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if "kernel" in name and ("ff" in name or "summary" in name or "logit" in name): logger.info("Transposing") array = np.transpose(array) if isinstance(pointer, list): # Here we will split the TF weights assert len(pointer) == array.shape[0], ( f"Pointer length {len(pointer)} and array length {array.shape[0]} mismatched" ) for i, p_i in enumerate(pointer): arr_i = array[i, ...] try: assert p_i.shape == arr_i.shape, ( f"Pointer shape {p_i.shape} and array shape {arr_i.shape} mismatched" ) except AssertionError as e: e.args += (p_i.shape, arr_i.shape) raise logger.info(f"Initialize PyTorch weight {name} for layer {i}") p_i.data = torch.from_numpy(arr_i) else: try: assert pointer.shape == array.shape, ( f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" ) except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) tf_weights.pop(name, None) tf_weights.pop(name + "/Adam", None) tf_weights.pop(name + "/Adam_1", None) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}") return model class XLNetRelativeAttention(nn.Module): def __init__(self, config): super().__init__() if config.d_model % config.n_head != 0: raise ValueError( f"The hidden size ({config.d_model}) is not a multiple of the number of attention " f"heads ({config.n_head}" ) self.n_head = config.n_head self.d_head = config.d_head self.d_model = config.d_model self.scale = 1 / (config.d_head**0.5) self.q = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head)) self.k = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head)) self.v = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head)) self.o = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head)) self.r = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head)) self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) self.r_s_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head)) self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.dropout) def prune_heads(self, heads): raise NotImplementedError @staticmethod def rel_shift(x, klen=-1): """perform relative shift to form the relative attention score.""" x_size = x.shape x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3]) x = x[1:, ...] x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3]) # x = x[:, 0:klen, :, :] x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long)) return x @staticmethod def rel_shift_bnij(x, klen=-1): x_size = x.shape x = x.reshape(x_size[0], x_size[1], x_size[3], x_size[2]) x = x[:, :, 1:, :] x = x.reshape(x_size[0], x_size[1], x_size[2], x_size[3] - 1) # Note: the tensor-slice form was faster in my testing than torch.index_select # However, tracing doesn't like the nature of the slice, and if klen changes # during the run then it'll fail, whereas index_select will be fine. x = torch.index_select(x, 3, torch.arange(klen, device=x.device, dtype=torch.long)) # x = x[:, :, :, :klen] return x def rel_attn_core( self, q_head, k_head_h, v_head_h, k_head_r, seg_mat=None, attn_mask=None, head_mask=None, output_attentions=False, ): """Core relative positional attention operations.""" # content based attention score ac = torch.einsum("ibnd,jbnd->bnij", q_head + self.r_w_bias, k_head_h) # position based attention score bd = torch.einsum("ibnd,jbnd->bnij", q_head + self.r_r_bias, k_head_r) bd = self.rel_shift_bnij(bd, klen=ac.shape[3]) # segment based attention score if seg_mat is None: ef = 0 else: ef = torch.einsum("ibnd,snd->ibns", q_head + self.r_s_bias, self.seg_embed) ef = torch.einsum("ijbs,ibns->bnij", seg_mat, ef) # merge attention scores and perform masking attn_score = (ac + bd + ef) * self.scale if attn_mask is not None: # attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask if attn_mask.dtype == torch.float16: attn_score = attn_score - 65500 * torch.einsum("ijbn->bnij", attn_mask) else: attn_score = attn_score - 1e30 * torch.einsum("ijbn->bnij", attn_mask) # attention probability attn_prob = nn.functional.softmax(attn_score, dim=3) attn_prob = self.dropout(attn_prob) # Mask heads if we want to if head_mask is not None: attn_prob = attn_prob * torch.einsum("ijbn->bnij", head_mask) # attention output attn_vec = torch.einsum("bnij,jbnd->ibnd", attn_prob, v_head_h) if output_attentions: return attn_vec, torch.einsum("bnij->ijbn", attn_prob) return attn_vec def post_attention(self, h, attn_vec, residual=True): """Post-attention processing.""" # post-attention projection (back to `d_model`) attn_out = torch.einsum("ibnd,hnd->ibh", attn_vec, self.o) attn_out = self.dropout(attn_out) if residual: attn_out = attn_out + h output = self.layer_norm(attn_out) return output def forward( self, h, g, attn_mask_h, attn_mask_g, r, seg_mat, mems=None, target_mapping=None, head_mask=None, output_attentions=False, ): if g is not None: # Two-stream attention with relative positional encoding. # content based attention score if mems is not None and mems.dim() > 1: cat = torch.cat([mems, h], dim=0) else: cat = h # content-based key head k_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.k) # content-based value head v_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.v) # position-based key head k_head_r = torch.einsum("ibh,hnd->ibnd", r, self.r) # h-stream # content-stream query head q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q) # core attention ops attn_vec_h = self.rel_attn_core( q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask, output_attentions=output_attentions, ) if output_attentions: attn_vec_h, attn_prob_h = attn_vec_h # post processing output_h = self.post_attention(h, attn_vec_h) # g-stream # query-stream query head q_head_g = torch.einsum("ibh,hnd->ibnd", g, self.q) # core attention ops if target_mapping is not None: q_head_g = torch.einsum("mbnd,mlb->lbnd", q_head_g, target_mapping) attn_vec_g = self.rel_attn_core( q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask, output_attentions=output_attentions, ) if output_attentions: attn_vec_g, attn_prob_g = attn_vec_g attn_vec_g = torch.einsum("lbnd,mlb->mbnd", attn_vec_g, target_mapping) else: attn_vec_g = self.rel_attn_core( q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask, output_attentions=output_attentions, ) if output_attentions: attn_vec_g, attn_prob_g = attn_vec_g # post processing output_g = self.post_attention(g, attn_vec_g) if output_attentions: attn_prob = attn_prob_h, attn_prob_g else: # Multi-head attention with relative positional encoding if mems is not None and mems.dim() > 1: cat = torch.cat([mems, h], dim=0) else: cat = h # content heads q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q) k_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.k) v_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.v) # positional heads # type casting for fp16 support k_head_r = torch.einsum("ibh,hnd->ibnd", r.type(self.r.dtype), self.r) # core attention ops attn_vec = self.rel_attn_core( q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask, output_attentions=output_attentions, ) if output_attentions: attn_vec, attn_prob = attn_vec # post processing output_h = self.post_attention(h, attn_vec) output_g = None outputs = (output_h, output_g) if output_attentions: outputs = outputs + (attn_prob,) return outputs class XLNetFeedForward(nn.Module): def __init__(self, config): super().__init__() self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps) self.layer_1 = nn.Linear(config.d_model, config.d_inner) self.layer_2 = nn.Linear(config.d_inner, config.d_model) self.dropout = nn.Dropout(config.dropout) if isinstance(config.ff_activation, str): self.activation_function = ACT2FN[config.ff_activation] else: self.activation_function = config.ff_activation def forward(self, inp): output = inp output = self.layer_1(output) output = self.activation_function(output) output = self.dropout(output) output = self.layer_2(output) output = self.dropout(output) output = self.layer_norm(output + inp) return output class XLNetLayer(nn.Module): def __init__(self, config): super().__init__() self.rel_attn = XLNetRelativeAttention(config) self.ff = XLNetFeedForward(config) self.dropout = nn.Dropout(config.dropout) self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 def forward( self, output_h, output_g, attn_mask_h, attn_mask_g, r, seg_mat, mems=None, target_mapping=None, head_mask=None, output_attentions=False, ): outputs = self.rel_attn( output_h, output_g, attn_mask_h, attn_mask_g, r, seg_mat, mems=mems, target_mapping=target_mapping, head_mask=head_mask, output_attentions=output_attentions, ) output_h, output_g = outputs[:2] if output_g is not None: output_g = apply_chunking_to_forward( self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, output_g ) output_h = apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, output_h) outputs = (output_h, output_g) + outputs[2:] # Add again attentions if there are there return outputs def ff_chunk(self, output_x): output_x = self.ff(output_x) return output_x class XLNetPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = XLNetConfig load_tf_weights = load_tf_weights_in_xlnet base_model_prefix = "transformer" def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, nn.Linear): # 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) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, XLNetRelativeAttention): 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) elif isinstance(module, XLNetModel): module.mask_emb.data.normal_(mean=0.0, std=self.config.initializer_range) @dataclass class XLNetModelOutput(ModelOutput): """ Output type of [`XLNetModel`]. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`): Sequence of hidden-states at the last layer of the model. `num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict` corresponds to `sequence_length`. mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ last_hidden_state: torch.FloatTensor mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class XLNetLMHeadModelOutput(ModelOutput): """ Output type of [`XLNetLMHeadModel`]. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided) Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, num_predict, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). `num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict` corresponds to `sequence_length`. mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class XLNetForSequenceClassificationOutput(ModelOutput): """ Output type of [`XLNetForSequenceClassification`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided): 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). mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class XLNetForTokenClassificationOutput(ModelOutput): """ Output type of [`XLNetForTokenClassificationOutput`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class XLNetForMultipleChoiceOutput(ModelOutput): """ Output type of [`XLNetForMultipleChoice`]. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class XLNetForQuestionAnsweringSimpleOutput(ModelOutput): """ Output type of [`XLNetForQuestionAnsweringSimple`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length,)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length,)`): Span-end scores (before SoftMax). mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None start_logits: Optional[torch.FloatTensor] = None end_logits: Optional[torch.FloatTensor] = None mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class XLNetForQuestionAnsweringOutput(ModelOutput): """ Output type of [`XLNetForQuestionAnswering`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided): Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses. start_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided): Log probabilities for the top config.start_n_top start token possibilities (beam-search). start_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided): Indices for the top config.start_n_top start token possibilities (beam-search). end_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided): Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search). end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided): Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search). cls_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided): Log probabilities for the `is_impossible` label of the answers. mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None start_top_log_probs: Optional[torch.FloatTensor] = None start_top_index: Optional[torch.LongTensor] = None end_top_log_probs: Optional[torch.FloatTensor] = None end_top_index: Optional[torch.LongTensor] = None cls_logits: Optional[torch.FloatTensor] = None mems: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None XLNET_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`XLNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ XLNET_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states (see `mems` output below) . Can be used to speed up sequential decoding. The token ids which have their past given to this model should not be passed as `input_ids` as they have already been computed. `use_mems` has to be set to `True` to make use of `mems`. perm_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length)`, *optional*): Mask to indicate the attention pattern for each input token with values selected in `[0, 1]`: - 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 not set, each token attends to all the others (full bidirectional attention). Only used during pretraining (to define factorization order) or for sequential decoding (generation). target_mapping (`torch.FloatTensor` of shape `(batch_size, num_predict, sequence_length)`, *optional*): Mask to indicate the output tokens to use. 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 or for sequential decoding (generation). token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) input_mask (`torch.FloatTensor` of shape `{0}`, *optional*): Mask to avoid performing attention on padding token indices. Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding which is kept for compatibility with the original code base. Mask values selected in `[0, 1]`: - 1 for tokens that are **masked**, - 0 for tokens that are **not masked**. You can only uses one of `input_mask` and `attention_mask`. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): 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**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.", XLNET_START_DOCSTRING, ) class XLNetModel(XLNetPreTrainedModel): def __init__(self, config): super().__init__(config) 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.n_layer = config.n_layer self.word_embedding = nn.Embedding(config.vocab_size, config.d_model) self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, config.d_model)) self.layer = nn.ModuleList([XLNetLayer(config) for _ in range(config.n_layer)]) self.dropout = nn.Dropout(config.dropout) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.word_embedding def set_input_embeddings(self, new_embeddings): self.word_embedding = new_embeddings def _prune_heads(self, heads_to_prune): raise NotImplementedError def create_mask(self, qlen, mlen): """ Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked. Args: qlen: Sequence length mlen: Mask length :: 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] """ mask = torch.ones((qlen, qlen + mlen), device=self.device) if self.same_length: mask_lo = mask[:, :qlen].tril(-1) mask.triu_(mlen + 1) mask[:, :qlen] += mask_lo else: mask.triu_(mlen + 1) return mask def cache_mem(self, curr_out, prev_mem): # cache hidden states into memory. if self.reuse_len is not None and self.reuse_len > 0: curr_out = curr_out[: self.reuse_len] if self.mem_len is None or self.mem_len == 0: # If `use_mems` is active but no `mem_len` is defined, the model behaves like GPT-2 at inference time # and returns all of the past and current hidden states. cutoff = 0 else: # If `use_mems` is active and `mem_len` is defined, the model returns the last `mem_len` hidden # states. This is the preferred setting for training and long-form generation. cutoff = -self.mem_len if prev_mem is None: # if `use_mems` is active and `mem_len` is defined, the model new_mem = curr_out[cutoff:] else: new_mem = torch.cat([prev_mem, curr_out], dim=0)[cutoff:] 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.int64).float() inv_freq = 1 / torch.pow(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(f"Unknown `attn_type` {self.attn_type}.") if self.bi_data: fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.int64).float() bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.int64).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, dtype=torch.int64).float() 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) return pos_emb @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=XLNetModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete after depreciation warning is removed ) -> Union[Tuple, XLNetModelOutput]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if "use_cache" in kwargs: warnings.warn( "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems`" " instead.", FutureWarning, ) use_mems = kwargs["use_cache"] if self.training: use_mems = use_mems if use_mems is not None else self.config.use_mems_train else: use_mems = use_mems if use_mems is not None else self.config.use_mems_eval # the original code for XLNet 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 if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_ids = input_ids.transpose(0, 1).contiguous() qlen, bsz = input_ids.shape[0], input_ids.shape[1] elif inputs_embeds is not None: inputs_embeds = inputs_embeds.transpose(0, 1).contiguous() qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") 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 mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0 klen = mlen + qlen dtype_float = self.dtype device = self.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(f"Unsupported attention type: {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 compatibility 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 if mlen > 0: 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) if mlen > 0: 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 if inputs_embeds is not None: word_emb_k = inputs_embeds else: word_emb_k = self.word_embedding(input_ids) output_h = self.dropout(word_emb_k) if target_mapping is not None: word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1) # else: # We removed the inp_q input which was same as target mapping # 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` if mlen > 0: mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device) cat_ids = torch.cat([mem_pad, token_type_ids], dim=0) else: cat_ids = token_type_ids # `1` indicates not in the same segment [qlen x klen x bsz] seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long() seg_mat = nn.functional.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 = pos_emb.to(output_h.device) pos_emb = self.dropout(pos_emb) # 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] (a head_mask for each layer) # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head] if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0) head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1) head_mask = head_mask.to( dtype=next(self.parameters()).dtype ) # switch to float if need + fp16 compatibility else: head_mask = [None] * self.n_layer new_mems = () if mems is None: mems = [None] * len(self.layer) attentions = [] if output_attentions else None hidden_states = [] if output_hidden_states else None for i, layer_module in enumerate(self.layer): if use_mems: # cache new mems new_mems = new_mems + (self.cache_mem(output_h, mems[i]),) if output_hidden_states: hidden_states.append((output_h, output_g) if output_g is not None else output_h) outputs = 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[i], output_attentions=output_attentions, ) output_h, output_g = outputs[:2] if output_attentions: attentions.append(outputs[2]) # Add last hidden state if output_hidden_states: hidden_states.append((output_h, output_g) if output_g is not None else output_h) output = self.dropout(output_g if output_g is not None else output_h) # Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method) output = output.permute(1, 0, 2).contiguous() if not use_mems: new_mems = None if output_hidden_states: if output_g is not None: hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs) else: hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states) if output_attentions: if target_mapping is not None: # when target_mapping is provided, there are 2-tuple of attentions attentions = tuple( tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions ) else: attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions) if not return_dict: return tuple(v for v in [output, new_mems, hidden_states, attentions] if v is not None) return XLNetModelOutput( last_hidden_state=output, mems=new_mems, hidden_states=hidden_states, attentions=attentions ) @add_start_docstrings( """ XLNet Model with a language modeling head on top (linear layer with weights tied to the input embeddings). """, XLNET_START_DOCSTRING, ) class XLNetLMHeadModel(XLNetPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_loss.weight"] def __init__(self, config): super().__init__(config) self.attn_type = config.attn_type self.same_length = config.same_length self.transformer = XLNetModel(config) self.lm_loss = nn.Linear(config.d_model, config.vocab_size, bias=True) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_loss def set_output_embeddings(self, new_embeddings): self.lm_loss = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, use_mems=None, **kwargs): # Overwritten -- this model has unique input preparation # Add dummy token at the end (no attention on this one) effective_batch_size = input_ids.shape[0] dummy_token = torch.zeros((effective_batch_size, 1), dtype=torch.long, device=input_ids.device) # At every pass, the attention values for the new token and the two last generated tokens # are computed, the rest is reloaded from the `past` cache. A purely auto-regressive model would have # offset = 1; offset = 2 seems to have slightly better computation. offset = 2 if past_key_values: input_ids = torch.cat([input_ids[:, -offset:], dummy_token], dim=1) else: input_ids = torch.cat([input_ids, dummy_token], dim=1) # Build permutation mask so that previous tokens don't see last token sequence_length = input_ids.shape[1] perm_mask = torch.zeros( (effective_batch_size, sequence_length, sequence_length), dtype=torch.float, device=input_ids.device ) perm_mask[:, :, -1] = 1.0 # We'll only predict the last token target_mapping = torch.zeros( (effective_batch_size, 1, sequence_length), dtype=torch.float, device=input_ids.device ) target_mapping[:, 0, -1] = 1.0 inputs = { "input_ids": input_ids, "perm_mask": perm_mask, "target_mapping": target_mapping, "use_mems": use_mems, } # if past is defined in model kwargs then use it for faster decoding if past_key_values: inputs["mems"] = tuple(layer_past[:-offset, :, :] for layer_past in past_key_values) return inputs @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=XLNetLMHeadModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete when `use_cache` is removed in XLNetModel ) -> Union[Tuple, XLNetLMHeadModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, num_predict)`, *optional*): Labels for masked language modeling. `num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict` corresponds to `sequence_length`. The labels should correspond to the masked input words that should be predicted and depends on `target_mapping`. Note in order to perform standard auto-regressive language modeling a ** token has to be added to the `input_ids` (see the `prepare_inputs_for_generation` function and examples below) Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored, the loss is only computed for labels in `[0, ..., config.vocab_size]` Return: Examples: ```python >>> from transformers import AutoTokenizer, XLNetLMHeadModel >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("xlnet/xlnet-large-cased") >>> model = XLNetLMHeadModel.from_pretrained("xlnet/xlnet-large-cased") >>> # We show how to setup inputs to predict a next token using a bi-directional context. >>> input_ids = torch.tensor( ... tokenizer.encode("Hello, my dog is very ", add_special_tokens=False) ... ).unsqueeze( ... 0 ... ) # We will predict the masked token >>> perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float) >>> perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token >>> target_mapping = torch.zeros( ... (1, 1, input_ids.shape[1]), dtype=torch.float ... ) # Shape [1, 1, seq_length] => let's predict one token >>> target_mapping[ ... 0, 0, -1 ... ] = 1.0 # Our first (and only) prediction will be the last token of the sequence (the masked token) >>> outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping) >>> next_token_logits = outputs[ ... 0 ... ] # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size] >>> # The same way can the XLNetLMHeadModel be used to be trained by standard auto-regressive language modeling. >>> input_ids = torch.tensor( ... tokenizer.encode("Hello, my dog is very ", add_special_tokens=False) ... ).unsqueeze( ... 0 ... ) # We will predict the masked token >>> labels = torch.tensor(tokenizer.encode("cute", add_special_tokens=False)).unsqueeze(0) >>> assert labels.shape[0] == 1, "only one word will be predicted" >>> perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float) >>> perm_mask[ ... :, :, -1 ... ] = 1.0 # Previous tokens don't see last token as is done in standard auto-regressive lm training >>> target_mapping = torch.zeros( ... (1, 1, input_ids.shape[1]), dtype=torch.float ... ) # Shape [1, 1, seq_length] => let's predict one token >>> target_mapping[ ... 0, 0, -1 ... ] = 1.0 # Our first (and only) prediction will be the last token of the sequence (the masked token) >>> outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping, labels=labels) >>> loss = outputs.loss >>> next_token_logits = ( ... outputs.logits ... ) # Logits have shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs, ) logits = self.lm_loss(transformer_outputs[0]) loss = None if labels is not None: # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return XLNetLMHeadModelOutput( loss=loss, logits=logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache(mems: List[torch.Tensor], beam_idx: torch.Tensor) -> List[torch.Tensor]: """ This function is used to re-order the `mems` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `mems` with the correct beam_idx at every generation step. """ return [layer_past.index_select(1, beam_idx.to(layer_past.device)) for layer_past in mems] @add_start_docstrings( """ XLNet Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, XLNET_START_DOCSTRING, ) class XLNetForSequenceClassification(XLNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.transformer = XLNetModel(config) self.sequence_summary = SequenceSummary(config) self.logits_proj = nn.Linear(config.d_model, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=XLNetForSequenceClassificationOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete when `use_cache` is removed in XLNetModel ) -> Union[Tuple, XLNetForSequenceClassificationOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. 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). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs, ) output = transformer_outputs[0] output = self.sequence_summary(output) logits = self.logits_proj(output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return XLNetForSequenceClassificationOutput( loss=loss, logits=logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ XLNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, XLNET_START_DOCSTRING, ) class XLNetForTokenClassification(XLNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = XLNetModel(config) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=XLNetForTokenClassificationOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete when `use_cache` is removed in XLNetModel ) -> Union[Tuple, XLNetForTokenClassificationOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return XLNetForTokenClassificationOutput( loss=loss, logits=logits, mems=outputs.mems, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ XLNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RACE/SWAG tasks. """, XLNET_START_DOCSTRING, ) class XLNetForMultipleChoice(XLNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.transformer = XLNetModel(config) self.sequence_summary = SequenceSummary(config) self.logits_proj = nn.Linear(config.d_model, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=XLNetForMultipleChoiceOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete when `use_cache` is removed in XLNetModel ) -> Union[Tuple, XLNetForMultipleChoiceOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_input_mask = input_mask.view(-1, input_mask.size(-1)) if input_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) transformer_outputs = self.transformer( flat_input_ids, token_type_ids=flat_token_type_ids, input_mask=flat_input_mask, attention_mask=flat_attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs, ) output = transformer_outputs[0] output = self.sequence_summary(output) logits = self.logits_proj(output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels.view(-1)) if not return_dict: output = (reshaped_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return XLNetForMultipleChoiceOutput( loss=loss, logits=reshaped_logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ XLNet 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`). """, XLNET_START_DOCSTRING, ) class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = XLNetModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=XLNetForQuestionAnsweringSimpleOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete when `use_cache` is removed in XLNetModel ) -> Union[Tuple, XLNetForQuestionAnsweringSimpleOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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 (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None 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 = start_positions.clamp(0, ignored_index) end_positions = 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 if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return XLNetForQuestionAnsweringSimpleOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, mems=outputs.mems, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ XLNet 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`). """, XLNET_START_DOCSTRING, ) class XLNetForQuestionAnswering(XLNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.start_n_top = config.start_n_top self.end_n_top = config.end_n_top self.transformer = XLNetModel(config) self.start_logits = PoolerStartLogits(config) self.end_logits = PoolerEndLogits(config) self.answer_class = PoolerAnswerClass(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=XLNetForQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, mems: Optional[torch.Tensor] = None, perm_mask: Optional[torch.Tensor] = None, target_mapping: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, input_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, is_impossible: Optional[torch.Tensor] = None, cls_index: Optional[torch.Tensor] = None, p_mask: Optional[torch.Tensor] = None, use_mems: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # delete when `use_cache` is removed in XLNetModel ) -> Union[Tuple, XLNetForQuestionAnsweringOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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 (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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 (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels whether a question has an answer or no answer (SQuAD 2.0) cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the classification token to use as input for computing plausibility of the answer. p_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...). 1.0 means token should be masked. 0.0 mean token is not masked. Returns: Example: ```python >>> from transformers import AutoTokenizer, XLNetForQuestionAnswering >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("xlnet/xlnet-base-cased") >>> model = XLNetForQuestionAnswering.from_pretrained("xlnet/xlnet-base-cased") >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).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 = outputs.loss ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, attention_mask=attention_mask, mems=mems, perm_mask=perm_mask, target_mapping=target_mapping, token_type_ids=token_type_ids, input_mask=input_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_mems=use_mems, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs, ) hidden_states = transformer_outputs[0] start_logits = self.start_logits(hidden_states, p_mask=p_mask) outputs = transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it if start_positions is not None and end_positions is not None: # If we are on multi-GPU, let's remove the dimension added by batch splitting for x in (start_positions, end_positions, cls_index, is_impossible): if x is not None and x.dim() > 1: x.squeeze_(-1) # during training, compute the end logits based on the ground truth of the start position end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask) loss_fct = CrossEntropyLoss() start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if cls_index is not None and is_impossible is not None: # Predict answerability from the representation of CLS and START cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index) loss_fct_cls = nn.BCEWithLogitsLoss() cls_loss = loss_fct_cls(cls_logits, is_impossible) # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss total_loss += cls_loss * 0.5 if not return_dict: return (total_loss,) + transformer_outputs[1:] else: return XLNetForQuestionAnsweringOutput( loss=total_loss, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) else: # during inference, compute the end logits based on beam search bsz, slen, hsz = hidden_states.size() start_log_probs = nn.functional.softmax(start_logits, dim=-1) # shape (bsz, slen) start_top_log_probs, start_top_index = torch.topk( start_log_probs, self.start_n_top, dim=-1 ) # shape (bsz, start_n_top) start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz) start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz) start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz) hidden_states_expanded = hidden_states.unsqueeze(2).expand_as( start_states ) # shape (bsz, slen, start_n_top, hsz) p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask) end_log_probs = nn.functional.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top) end_top_log_probs, end_top_index = torch.topk( end_log_probs, self.end_n_top, dim=1 ) # shape (bsz, end_n_top, start_n_top) end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top) end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top) start_states = torch.einsum( "blh,bl->bh", hidden_states, start_log_probs ) # get the representation of START as weighted sum of hidden states cls_logits = self.answer_class( hidden_states, start_states=start_states, cls_index=cls_index ) # Shape (batch size,): one single `cls_logits` for each sample if not return_dict: outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) return outputs + transformer_outputs[1:] else: return XLNetForQuestionAnsweringOutput( start_top_log_probs=start_top_log_probs, start_top_index=start_top_index, end_top_log_probs=end_top_log_probs, end_top_index=end_top_index, cls_logits=cls_logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) __all__ = [ "XLNetForMultipleChoice", "XLNetForQuestionAnswering", "XLNetForQuestionAnsweringSimple", "XLNetForSequenceClassification", "XLNetForTokenClassification", "XLNetLMHeadModel", "XLNetModel", "XLNetPreTrainedModel", "load_tf_weights_in_xlnet", ]