mirror of
https://github.com/huggingface/transformers.git
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1291 lines
62 KiB
Python
1291 lines
62 KiB
Python
# coding=utf-8
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch XLNet model.
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"""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import json
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import logging
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import math
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import os
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import sys
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from io import open
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.nn import CrossEntropyLoss, MSELoss
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from .modeling_utils import (CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel,
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SequenceSummary, PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits,
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add_start_docstrings)
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logger = logging.getLogger(__name__)
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XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-pytorch_model.bin",
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'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",
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}
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XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
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'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
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}
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def build_tf_xlnet_to_pytorch_map(model, config, tf_weights=None):
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""" A map of modules from TF to PyTorch.
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I use a map to keep the PyTorch model as
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identical to the original PyTorch model as possible.
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"""
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tf_to_pt_map = {}
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if hasattr(model, 'transformer'):
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if hasattr(model, 'lm_loss'):
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# We will load also the output bias
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tf_to_pt_map['model/lm_loss/bias'] = model.lm_loss.bias
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if hasattr(model, 'sequence_summary') and 'model/sequnece_summary/summary/kernel' in tf_weights:
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# We will load also the sequence summary
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tf_to_pt_map['model/sequnece_summary/summary/kernel'] = model.sequence_summary.summary.weight
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tf_to_pt_map['model/sequnece_summary/summary/bias'] = model.sequence_summary.summary.bias
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if hasattr(model, 'logits_proj') and config.finetuning_task is not None \
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and 'model/regression_{}/logit/kernel'.format(config.finetuning_task) in tf_weights:
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tf_to_pt_map['model/regression_{}/logit/kernel'.format(config.finetuning_task)] = model.logits_proj.weight
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tf_to_pt_map['model/regression_{}/logit/bias'.format(config.finetuning_task)] = model.logits_proj.bias
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# Now load the rest of the transformer
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model = model.transformer
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# Embeddings and output
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tf_to_pt_map.update({'model/transformer/word_embedding/lookup_table': model.word_embedding.weight,
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'model/transformer/mask_emb/mask_emb': model.mask_emb})
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# Transformer blocks
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for i, b in enumerate(model.layer):
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layer_str = "model/transformer/layer_%d/" % i
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tf_to_pt_map.update({
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layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight,
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layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias,
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layer_str + "rel_attn/o/kernel": b.rel_attn.o,
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layer_str + "rel_attn/q/kernel": b.rel_attn.q,
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layer_str + "rel_attn/k/kernel": b.rel_attn.k,
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layer_str + "rel_attn/r/kernel": b.rel_attn.r,
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layer_str + "rel_attn/v/kernel": b.rel_attn.v,
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layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight,
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layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias,
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layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight,
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layer_str + "ff/layer_1/bias": b.ff.layer_1.bias,
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layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight,
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layer_str + "ff/layer_2/bias": b.ff.layer_2.bias,
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})
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# Relative positioning biases
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if config.untie_r:
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r_r_list = []
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r_w_list = []
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r_s_list = []
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seg_embed_list = []
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for b in model.layer:
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r_r_list.append(b.rel_attn.r_r_bias)
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r_w_list.append(b.rel_attn.r_w_bias)
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r_s_list.append(b.rel_attn.r_s_bias)
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seg_embed_list.append(b.rel_attn.seg_embed)
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else:
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r_r_list = [model.r_r_bias]
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r_w_list = [model.r_w_bias]
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r_s_list = [model.r_s_bias]
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seg_embed_list = [model.seg_embed]
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tf_to_pt_map.update({
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'model/transformer/r_r_bias': r_r_list,
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'model/transformer/r_w_bias': r_w_list,
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'model/transformer/r_s_bias': r_s_list,
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'model/transformer/seg_embed': seg_embed_list})
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return tf_to_pt_map
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def load_tf_weights_in_xlnet(model, config, tf_path):
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""" Load tf checkpoints in a pytorch model
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"""
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try:
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import numpy as np
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import tensorflow as tf
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except ImportError:
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logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
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"https://www.tensorflow.org/install/ for installation instructions.")
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raise
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# Load weights from TF model
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init_vars = tf.train.list_variables(tf_path)
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tf_weights = {}
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for name, shape in init_vars:
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logger.info("Loading TF weight {} with shape {}".format(name, shape))
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array = tf.train.load_variable(tf_path, name)
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tf_weights[name] = array
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# Build TF to PyTorch weights loading map
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tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)
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for name, pointer in tf_to_pt_map.items():
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logger.info("Importing {}".format(name))
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if name not in tf_weights:
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logger.info("{} not in tf pre-trained weights, skipping".format(name))
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continue
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array = tf_weights[name]
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# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
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# which are not required for using pretrained model
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if 'kernel' in name and ('ff' in name or 'summary' in name or 'logit' in name):
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logger.info("Transposing")
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array = np.transpose(array)
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if isinstance(pointer, list):
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# Here we will split the TF weigths
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assert len(pointer) == array.shape[0]
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for i, p_i in enumerate(pointer):
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arr_i = array[i, ...]
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try:
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assert p_i.shape == arr_i.shape
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except AssertionError as e:
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e.args += (p_i.shape, arr_i.shape)
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raise
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logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))
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p_i.data = torch.from_numpy(arr_i)
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else:
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try:
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assert pointer.shape == array.shape
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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logger.info("Initialize PyTorch weight {}".format(name))
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pointer.data = torch.from_numpy(array)
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tf_weights.pop(name, None)
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tf_weights.pop(name + '/Adam', None)
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tf_weights.pop(name + '/Adam_1', None)
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logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
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return model
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def gelu(x):
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""" Implementation of the gelu activation function.
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XLNet is using OpenAI GPT's gelu (not exactly the same as BERT)
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Also see https://arxiv.org/abs/1606.08415
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"""
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cdf = 0.5 * (1.0 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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return x * cdf
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def swish(x):
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return x * torch.sigmoid(x)
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ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
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class XLNetConfig(PretrainedConfig):
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"""Configuration class to store the configuration of a ``XLNetModel``.
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Args:
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vocab_size_or_config_json_file: Vocabulary size of ``inputs_ids`` in ``XLNetModel``.
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d_model: Size of the encoder layers and the pooler layer.
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n_layer: Number of hidden layers in the Transformer encoder.
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n_head: Number of attention heads for each attention layer in
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the Transformer encoder.
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d_inner: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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ff_activation: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
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untie_r: untie relative position biases
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attn_type: 'bi' for XLNet, 'uni' for Transformer-XL
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dropout: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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dropatt: The dropout ratio for the attention
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probabilities.
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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layer_norm_eps: The epsilon used by LayerNorm.
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dropout: float, dropout rate.
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dropatt: float, dropout rate on attention probabilities.
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init: str, the initialization scheme, either "normal" or "uniform".
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init_range: float, initialize the parameters with a uniform distribution
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in [-init_range, init_range]. Only effective when init="uniform".
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init_std: float, initialize the parameters with a normal distribution
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with mean 0 and stddev init_std. Only effective when init="normal".
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mem_len: int, the number of tokens to cache.
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reuse_len: int, the number of tokens in the currect batch to be cached
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and reused in the future.
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bi_data: bool, whether to use bidirectional input pipeline.
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Usually set to True during pretraining and False during finetuning.
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clamp_len: int, clamp all relative distances larger than clamp_len.
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-1 means no clamping.
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same_length: bool, whether to use the same attention length for each token.
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finetuning_task: name of the glue task on which the model was fine-tuned if any
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"""
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pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=32000,
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d_model=1024,
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n_layer=24,
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n_head=16,
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d_inner=4096,
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ff_activation="gelu",
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untie_r=True,
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attn_type="bi",
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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dropout=0.1,
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mem_len=None,
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reuse_len=None,
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bi_data=False,
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clamp_len=-1,
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same_length=False,
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finetuning_task=None,
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num_labels=2,
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summary_type='last',
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summary_use_proj=True,
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summary_activation='tanh',
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summary_last_dropout=0.1,
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start_n_top=5,
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end_n_top=5,
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**kwargs):
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"""Constructs XLNetConfig.
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"""
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super(XLNetConfig, self).__init__(**kwargs)
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if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.n_token = vocab_size_or_config_json_file
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self.d_model = d_model
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self.n_layer = n_layer
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self.n_head = n_head
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assert d_model % n_head == 0
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self.d_head = d_model // n_head
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self.ff_activation = ff_activation
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self.d_inner = d_inner
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self.untie_r = untie_r
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self.attn_type = attn_type
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.dropout = dropout
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self.mem_len = mem_len
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self.reuse_len = reuse_len
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self.bi_data = bi_data
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self.clamp_len = clamp_len
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self.same_length = same_length
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self.finetuning_task = finetuning_task
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self.num_labels = num_labels
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_last_dropout = summary_last_dropout
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self.start_n_top = start_n_top
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self.end_n_top = end_n_top
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else:
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raise ValueError("First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)")
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@property
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def max_position_embeddings(self):
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return -1
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@property
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def vocab_size(self):
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return self.n_token
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@vocab_size.setter
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def vocab_size(self, value):
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self.n_token = value
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@property
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def hidden_size(self):
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return self.d_model
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@property
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def num_attention_heads(self):
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return self.n_head
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@property
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def num_hidden_layers(self):
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return self.n_layer
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try:
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from apex.normalization.fused_layer_norm import FusedLayerNorm as XLNetLayerNorm
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except ImportError:
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logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
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class XLNetLayerNorm(nn.Module):
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def __init__(self, d_model, eps=1e-12):
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"""Construct a layernorm module in the TF style (epsilon inside the square root).
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"""
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super(XLNetLayerNorm, self).__init__()
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self.weight = nn.Parameter(torch.ones(d_model))
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self.bias = nn.Parameter(torch.zeros(d_model))
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self.variance_epsilon = eps
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def forward(self, x):
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u = x.mean(-1, keepdim=True)
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s = (x - u).pow(2).mean(-1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.variance_epsilon)
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return self.weight * x + self.bias
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class XLNetRelativeAttention(nn.Module):
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def __init__(self, config):
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super(XLNetRelativeAttention, self).__init__()
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self.output_attentions = config.output_attentions
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if config.d_model % config.n_head != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.d_model, config.n_head))
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self.n_head = config.n_head
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self.d_head = config.d_head
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self.d_model = config.d_model
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self.scale = 1 / (config.d_head ** 0.5)
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self.q = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
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self.k = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
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self.v = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
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self.o = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
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self.r = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
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self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
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self.r_s_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
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self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
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self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head))
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self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.dropout)
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def prune_heads(self, heads):
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raise NotImplementedError
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@staticmethod
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def rel_shift(x, klen=-1):
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"""perform relative shift to form the relative attention score."""
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x_size = x.shape
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x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3])
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x = x[1:, ...]
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x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3])
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# x = x[:, 0:klen, :, :]
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x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long))
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return x
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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):
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"""Core relative positional attention operations."""
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# content based attention score
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ac = torch.einsum('ibnd,jbnd->ijbn', q_head + self.r_w_bias, k_head_h)
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# position based attention score
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bd = torch.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r)
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bd = self.rel_shift(bd, klen=ac.shape[1])
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# segment based attention score
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if seg_mat is None:
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ef = 0
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else:
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ef = torch.einsum('ibnd,snd->ibns', q_head + self.r_s_bias, self.seg_embed)
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ef = torch.einsum('ijbs,ibns->ijbn', seg_mat, ef)
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# merge attention scores and perform masking
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attn_score = (ac + bd + ef) * self.scale
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if attn_mask is not None:
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# attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
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attn_score = attn_score - 1e30 * attn_mask
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# attention probability
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attn_prob = F.softmax(attn_score, dim=1)
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attn_prob = self.dropout(attn_prob)
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# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attn_prob = attn_prob * head_mask
|
|
|
|
# attention output
|
|
attn_vec = torch.einsum('ijbn,jbnd->ibnd', attn_prob, v_head_h)
|
|
|
|
if self.output_attentions:
|
|
return attn_vec, 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):
|
|
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)
|
|
|
|
if self.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)
|
|
|
|
if self.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)
|
|
|
|
if self.output_attentions:
|
|
attn_vec_g, attn_prob_g = attn_vec_g
|
|
|
|
# post processing
|
|
output_g = self.post_attention(g, attn_vec_g)
|
|
|
|
if self.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
|
|
k_head_r = torch.einsum('ibh,hnd->ibnd', r, 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)
|
|
|
|
if self.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 self.output_attentions:
|
|
outputs = outputs + (attn_prob,)
|
|
return outputs
|
|
|
|
class XLNetFeedForward(nn.Module):
|
|
def __init__(self, config):
|
|
super(XLNetFeedForward, self).__init__()
|
|
self.layer_norm = XLNetLayerNorm(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) or \
|
|
(sys.version_info[0] == 2 and isinstance(config.ff_activation, unicode)):
|
|
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(XLNetLayer, self).__init__()
|
|
self.rel_attn = XLNetRelativeAttention(config)
|
|
self.ff = XLNetFeedForward(config)
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
|
|
def forward(self, output_h, output_g,
|
|
attn_mask_h, attn_mask_g,
|
|
r, seg_mat, mems=None, target_mapping=None, head_mask=None):
|
|
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_h, output_g = outputs[:2]
|
|
|
|
if output_g is not None:
|
|
output_g = self.ff(output_g)
|
|
output_h = self.ff(output_h)
|
|
|
|
outputs = (output_h, output_g) + outputs[2:] # Add again attentions if there are there
|
|
return outputs
|
|
|
|
|
|
class XLNetPreTrainedModel(PreTrainedModel):
|
|
""" An abstract class to handle weights initialization and
|
|
a simple interface for dowloading and loading pretrained models.
|
|
"""
|
|
config_class = XLNetConfig
|
|
pretrained_model_archive_map = XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
|
|
load_tf_weights = load_tf_weights_in_xlnet
|
|
base_model_prefix = "transformer"
|
|
|
|
def __init__(self, *inputs, **kwargs):
|
|
super(XLNetPreTrainedModel, 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)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, XLNetLayerNorm):
|
|
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)
|
|
|
|
|
|
XLNET_START_DOCSTRING = r""" The XLNet model was proposed in
|
|
`XLNet: Generalized Autoregressive Pretraining for Language Understanding`_
|
|
by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
|
XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
|
|
to learn bidirectional contexts by maximizing the expected likelihood over all permutations
|
|
of the input sequence factorization order.
|
|
|
|
The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
|
|
|
|
Do to the difficulty of training a fully auto-regressive model over various factorization order,
|
|
XLNet is pretrained using only a sub-set of the output tokens as target which are selected
|
|
with the `target_mapping` input.
|
|
|
|
To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
|
|
`target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)
|
|
|
|
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
|
|
refer to the PyTorch documentation for all matter related to general usage and behavior.
|
|
|
|
.. _`XLNet: Generalized Autoregressive Pretraining for Language Understanding`:
|
|
http://arxiv.org/abs/1906.08237
|
|
|
|
.. _`torch.nn.Module`:
|
|
https://pytorch.org/docs/stable/nn.html#module
|
|
|
|
Parameters:
|
|
config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
|
|
"""
|
|
|
|
XLNET_INPUTS_DOCSTRING = r"""
|
|
Inputs:
|
|
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
|
Indices of input sequence tokens in the vocabulary.
|
|
Indices can be obtained using :class:`pytorch_transformers.XLNetTokenizer`.
|
|
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
|
|
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
|
|
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
|
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
|
|
The embeddings from these tokens will be summed with the respective token embeddings.
|
|
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
|
|
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
|
|
Mask to avoid performing attention on padding token indices.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
|
**input_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
|
|
Mask to avoid performing attention on padding token indices.
|
|
Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding.
|
|
Kept for compatibility with the original code base.
|
|
You can only uses one of `input_mask` and `attention_mask`
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` for tokens that are MASKED, ``0`` for tokens that are NOT MASKED.
|
|
**mems**: (`optional`)
|
|
list of ``torch.FloatTensor`` (one for each layer):
|
|
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
|
(see `mems` output below). Can be used to speed up sequential decoding and attend to longer context.
|
|
**perm_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, sequence_length)``:
|
|
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 None, 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**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_predict, sequence_length)``:
|
|
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).
|
|
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
|
|
Mask to nullify selected heads of the self-attention modules.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
|
|
"""
|
|
|
|
@add_start_docstrings("The bare XLNet Model transformer outputing raw hidden-states without any specific head on top.",
|
|
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
|
|
class XLNetModel(XLNetPreTrainedModel):
|
|
r"""
|
|
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
|
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
|
|
Sequence of hidden-states at the last layer of the model.
|
|
**mems**:
|
|
list of ``torch.FloatTensor`` (one for each layer):
|
|
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
|
(see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
|
|
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
|
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
|
of shape ``(batch_size, sequence_length, hidden_size)``:
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
|
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
|
|
|
Examples::
|
|
|
|
>>> config = XLNetConfig.from_pretrained('xlnet-large-cased')
|
|
>>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
|
|
>>> model = XLNetModel(config)
|
|
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
|
>>> outputs = model(input_ids)
|
|
>>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
|
|
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLNetModel, self).__init__(config)
|
|
self.output_attentions = config.output_attentions
|
|
self.output_hidden_states = config.output_hidden_states
|
|
|
|
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.n_token, 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)
|
|
|
|
self.apply(self.init_weights)
|
|
|
|
def _resize_token_embeddings(self, new_num_tokens):
|
|
self.word_embedding = self._get_resized_embeddings(self.word_embedding, new_num_tokens)
|
|
return self.word_embedding
|
|
|
|
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: TODO Lysandre didn't fill
|
|
mlen: TODO Lysandre didn't fill
|
|
|
|
::
|
|
|
|
same_length=False: same_length=True:
|
|
<mlen > < qlen > <mlen > < 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 / 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('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, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
|
|
mems=None, perm_mask=None, target_mapping=None, head_mask=None):
|
|
# 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
|
|
input_ids = input_ids.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
|
|
|
|
qlen, bsz = input_ids.shape[0], input_ids.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(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`
|
|
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)
|
|
|
|
# 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 fload if need + fp16 compatibility
|
|
else:
|
|
head_mask = [None] * self.n_layer
|
|
|
|
new_mems = ()
|
|
if mems is None:
|
|
mems = [None] * len(self.layer)
|
|
|
|
attentions = []
|
|
hidden_states = []
|
|
for i, layer_module in enumerate(self.layer):
|
|
# cache new mems
|
|
new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
|
|
if self.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_h, output_g = outputs[:2]
|
|
if self.output_attentions:
|
|
attentions.append(outputs[2])
|
|
|
|
# Add last hidden state
|
|
if self.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)
|
|
outputs = (output.permute(1, 0, 2).contiguous(), new_mems)
|
|
if self.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)
|
|
outputs = outputs + (hidden_states,)
|
|
if self.output_attentions:
|
|
attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
|
|
outputs = outputs + (attentions,)
|
|
|
|
return outputs # outputs, new_mems, (hidden_states), (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, XLNET_INPUTS_DOCSTRING)
|
|
class XLNetLMHeadModel(XLNetPreTrainedModel):
|
|
r"""
|
|
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
|
Labels for language modeling.
|
|
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
|
|
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
|
|
All labels set to ``-1`` are ignored (masked), the loss is only
|
|
computed for labels in ``[0, ..., config.vocab_size]``
|
|
|
|
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
|
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
|
Language modeling loss.
|
|
**prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
**mems**:
|
|
list of ``torch.FloatTensor`` (one for each layer):
|
|
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
|
(see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
|
|
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
|
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
|
of shape ``(batch_size, sequence_length, hidden_size)``:
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
|
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
|
|
|
Examples::
|
|
|
|
>>> config = XLNetConfig.from_pretrained('xlnet-large-cased')
|
|
>>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
|
|
>>> model = XLNetLMHeadModel(config)
|
|
>>> # 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 <mask>")).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]
|
|
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLNetLMHeadModel, self).__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.n_token, bias=True)
|
|
|
|
self.apply(self.init_weights)
|
|
self.tie_weights()
|
|
|
|
def tie_weights(self):
|
|
""" Make sure we are sharing the embeddings
|
|
"""
|
|
self._tie_or_clone_weights(self.lm_loss, self.transformer.word_embedding)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
|
|
mems=None, perm_mask=None, target_mapping=None,
|
|
labels=None, head_mask=None):
|
|
transformer_outputs = self.transformer(input_ids, token_type_ids=token_type_ids,
|
|
input_mask=input_mask, attention_mask=attention_mask,
|
|
mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
|
|
head_mask=head_mask)
|
|
|
|
logits = self.lm_loss(transformer_outputs[0])
|
|
|
|
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
|
|
|
|
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))
|
|
outputs = (loss,) + outputs
|
|
|
|
return outputs # return (loss), logits, mems, (hidden states), (attentions)
|
|
|
|
|
|
@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, XLNET_INPUTS_DOCSTRING)
|
|
class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
|
r"""
|
|
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
|
Labels for computing the sequence classification/regression loss.
|
|
Indices should be in ``[0, ..., config.num_labels]``.
|
|
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
|
|
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
|
|
|
|
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
|
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
**mems**:
|
|
list of ``torch.FloatTensor`` (one for each layer):
|
|
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
|
(see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
|
|
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
|
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
|
of shape ``(batch_size, sequence_length, hidden_size)``:
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
|
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
|
|
|
Examples::
|
|
|
|
>>> config = XLNetConfig.from_pretrained('xlnet-large-cased')
|
|
>>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
|
|
>>>
|
|
>>> model = XLNetForSequenceClassification(config)
|
|
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
|
>>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
|
|
>>> outputs = model(input_ids, labels=labels)
|
|
>>> loss, logits = outputs[:2]
|
|
|
|
"""
|
|
def __init__(self, config):
|
|
super(XLNetForSequenceClassification, self).__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = XLNetModel(config)
|
|
self.sequence_summary = SequenceSummary(config)
|
|
self.logits_proj = nn.Linear(config.d_model, config.num_labels)
|
|
|
|
self.apply(self.init_weights)
|
|
|
|
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
|
|
mems=None, perm_mask=None, target_mapping=None,
|
|
labels=None, head_mask=None):
|
|
transformer_outputs = self.transformer(input_ids, token_type_ids=token_type_ids,
|
|
input_mask=input_mask, attention_mask=attention_mask,
|
|
mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
|
|
head_mask=head_mask)
|
|
output = transformer_outputs[0]
|
|
|
|
output = self.sequence_summary(output)
|
|
logits = self.logits_proj(output)
|
|
|
|
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
|
|
|
|
if labels is not None:
|
|
if self.num_labels == 1:
|
|
# We are doing regression
|
|
loss_fct = MSELoss()
|
|
loss = loss_fct(logits.view(-1), labels.view(-1))
|
|
else:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
outputs = (loss,) + outputs
|
|
|
|
return outputs # return (loss), logits, mems, (hidden states), (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, XLNET_INPUTS_DOCSTRING)
|
|
class XLNetForQuestionAnswering(XLNetPreTrainedModel):
|
|
r"""
|
|
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`).
|
|
Position outside of the sequence are not taken into account for computing the loss.
|
|
**end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`).
|
|
Position outside of the sequence are not taken into account for computing the loss.
|
|
**is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
|
Labels whether a question has an answer or no answer (SQuAD 2.0)
|
|
**cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
|
Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
|
|
**p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
|
|
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.
|
|
|
|
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
|
**loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
|
Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
|
|
**start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
|
|
``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
|
|
Log probabilities for the top config.start_n_top start token possibilities (beam-search).
|
|
**start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
|
|
``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
|
|
Indices for the top config.start_n_top start token possibilities (beam-search).
|
|
**end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
|
|
``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
|
|
Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
|
|
**end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
|
|
``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
|
|
Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
|
|
**cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
|
|
``torch.FloatTensor`` of shape ``(batch_size,)``
|
|
Log probabilities for the ``is_impossible`` label of the answers.
|
|
**mems**:
|
|
list of ``torch.FloatTensor`` (one for each layer):
|
|
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
|
(see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
|
|
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
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list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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of shape ``(batch_size, sequence_length, hidden_size)``:
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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**attentions**: (`optional`, returned when ``config.output_attentions=True``)
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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>>> config = XLMConfig.from_pretrained('xlm-mlm-en-2048')
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>>> tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
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>>>
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>>> model = XLMForQuestionAnswering(config)
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>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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>>> start_positions = torch.tensor([1])
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>>> end_positions = torch.tensor([3])
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>>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
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>>> loss, start_scores, end_scores = outputs[:2]
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"""
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def __init__(self, config):
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super(XLNetForQuestionAnswering, self).__init__(config)
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self.start_n_top = config.start_n_top
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self.end_n_top = config.end_n_top
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self.transformer = XLNetModel(config)
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self.start_logits = PoolerStartLogits(config)
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self.end_logits = PoolerEndLogits(config)
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self.answer_class = PoolerAnswerClass(config)
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self.apply(self.init_weights)
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def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
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mems=None, perm_mask=None, target_mapping=None,
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start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
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head_mask=None):
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transformer_outputs = self.transformer(input_ids, token_type_ids=token_type_ids,
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input_mask=input_mask, attention_mask=attention_mask,
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mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
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head_mask=head_mask)
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hidden_states = transformer_outputs[0]
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start_logits = self.start_logits(hidden_states, p_mask=p_mask)
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outputs = transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
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if start_positions is not None and end_positions is not None:
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# If we are on multi-GPU, let's remove the dimension added by batch splitting
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for x in (start_positions, end_positions, cls_index, is_impossible):
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if x is not None and x.dim() > 1:
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x.squeeze_(-1)
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# during training, compute the end logits based on the ground truth of the start position
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end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
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loss_fct = CrossEntropyLoss()
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start_loss = loss_fct(start_logits, start_positions)
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end_loss = loss_fct(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2
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if cls_index is not None and is_impossible is not None:
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# Predict answerability from the representation of CLS and START
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cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
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loss_fct_cls = nn.BCEWithLogitsLoss()
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cls_loss = loss_fct_cls(cls_logits, is_impossible)
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# note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
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total_loss += cls_loss * 0.5
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outputs = (total_loss,) + outputs
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else:
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# during inference, compute the end logits based on beam search
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bsz, slen, hsz = hidden_states.size()
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start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)
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start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
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start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
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start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
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start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
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hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
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p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
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end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
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end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)
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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)
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end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
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end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
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start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs) # get the representation of START as weighted sum of hidden states
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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
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outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
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# return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
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# or (if labels are provided) (total_loss,)
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return outputs
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