mirror of
https://github.com/huggingface/transformers.git
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365 lines
16 KiB
Python
365 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import unittest
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import json
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import random
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import shutil
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import pytest
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import torch
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from pytorch_pretrained_bert import (GPT2Config, GPT2Model,
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GPT2LMHeadModel, GPT2DoubleHeadsModel)
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from pytorch_pretrained_bert.modeling_gpt2 import PRETRAINED_MODEL_ARCHIVE_MAP
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class GPT2ModelTest(unittest.TestCase):
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class GPT2ModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_position_ids=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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n_special=1,
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n_positions=33,
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n_embd=32,
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n_layer=5,
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n_head=4,
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n_choices=3,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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scope=None):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_position_ids = use_position_ids
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.n_special = n_special
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.n_choices = n_choices
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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def prepare_config_and_inputs(self):
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total_num_tokens = self.vocab_size + self.n_special
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input_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
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position_ids = None
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if self.use_position_ids:
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position_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
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token_type_ids = None
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if self.use_token_type_ids:
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total_voc = self.vocab_size
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token_type_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
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mc_labels = None
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lm_labels = None
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mc_token_ids = None
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if self.use_labels:
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mc_labels = GPT2ModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
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lm_labels = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
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mc_token_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices], self.seq_length)
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config = GPT2Config(
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vocab_size_or_config_json_file=self.vocab_size,
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n_special=self.n_special,
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n_positions=self.n_positions,
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n_embd=self.n_embd,
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n_layer=self.n_layer,
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n_head=self.n_head,
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initializer_range=self.initializer_range)
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return (config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids)
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def create_gpt2_model(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = GPT2Model(config)
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model.eval()
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hidden_states, presents = model(input_ids, position_ids, token_type_ids)
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outputs = {
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"hidden_states": hidden_states,
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"presents": presents,
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}
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return outputs
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def check_gpt2_model_output(self, result):
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self.parent.assertEqual(len(result["hidden_states"]), self.n_layer + 1)
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self.parent.assertListEqual(
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list(result["hidden_states"][0].size()),
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[self.batch_size, self.n_choices, self.seq_length, self.n_embd])
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def create_gpt2_lm_head(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = GPT2LMHeadModel(config)
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model.eval()
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loss = model(input_ids, position_ids, token_type_ids, lm_labels)
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lm_logits, presents = model(input_ids, position_ids, token_type_ids)
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outputs = {
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"loss": loss,
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"lm_logits": lm_logits,
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"presents": presents,
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}
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return outputs
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def create_gpt2_lm_head_with_output_attention(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = GPT2LMHeadModel(config, output_attentions=True)
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model.eval()
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loss = model(input_ids, position_ids, token_type_ids, lm_labels)
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attentions, lm_logits, presents = model(input_ids, position_ids, token_type_ids)
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outputs = {
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"loss": loss,
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"lm_logits": lm_logits,
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"presents": presents,
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"attentions": attentions,
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}
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return outputs
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def check_gpt2_lm_head_output(self, result):
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total_voc = self.n_special + self.vocab_size
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.n_choices, self.seq_length, total_voc])
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self.parent.assertEqual(self.n_layer, len(result["presents"]))
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self.parent.assertListEqual(
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list(result["presents"][0].size()),
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[2, self.batch_size * self.n_choices, self.n_head, self.seq_length, self.n_embd // self.n_head])
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def check_gpt2_lm_head_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_gpt2_double_heads(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = GPT2DoubleHeadsModel(config)
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model.eval()
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loss = model(input_ids, mc_token_ids,
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lm_labels=lm_labels, mc_labels=mc_labels,
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token_type_ids=token_type_ids, position_ids=position_ids)
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lm_logits, mc_logits, presents = model(input_ids, mc_token_ids, position_ids=position_ids, token_type_ids=token_type_ids)
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outputs = {
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"loss": loss,
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"lm_logits": lm_logits,
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"mc_logits": mc_logits,
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"presents": presents,
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}
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return outputs
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def create_gpt2_double_heads_with_output_attention(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = GPT2DoubleHeadsModel(config, output_attentions=True)
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model.eval()
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loss = model(input_ids, mc_token_ids,
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lm_labels=lm_labels, mc_labels=mc_labels,
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token_type_ids=token_type_ids, position_ids=position_ids)
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attentions, lm_logits, mc_logits, presents = model(input_ids, mc_token_ids, position_ids=position_ids, token_type_ids=token_type_ids)
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outputs = {
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"loss": loss,
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"lm_logits": lm_logits,
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"mc_logits": mc_logits,
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"presents": presents,
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"attentions": attentions,
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}
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return outputs
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def check_gpt2_double_heads_output(self, result):
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total_voc = self.n_special + self.vocab_size
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.n_choices, self.seq_length, total_voc])
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self.parent.assertListEqual(
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list(result["mc_logits"].size()),
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[self.batch_size, self.n_choices])
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def check_gpt2_double_heads_loss_output(self, result):
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self.parent.assertListEqual(
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[list(l.size()) for l in result["loss"]],
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[[], []])
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def create_and_check_gpt2_for_headmasking(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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for model_class in (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel):
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model = model_class(config=config, keep_multihead_output=True)
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model.eval()
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head_mask = torch.zeros(self.n_layer, self.n_head).to(input_ids.device)
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head_mask[0, 1:-1] = 1.0 # Mask all but the first and last heads on the first layer
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head_mask[-1, 1:] = 1.0 # Mask all but the first head on the last layer
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if isinstance(model, GPT2DoubleHeadsModel):
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output = model(input_ids, mc_token_ids, head_mask=head_mask)
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else:
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output = model(input_ids, head_mask=head_mask)
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if isinstance(model, GPT2Model):
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output = sum(t.sum() for t in output[0])
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elif isinstance(output, (list, tuple)):
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output = sum(t.sum() for t in output[:-1])
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output = output.sum()
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output.backward()
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multihead_outputs = (model if isinstance(model, GPT2Model) else model.transformer).get_multihead_outputs()
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self.parent.assertEqual(len(multihead_outputs), self.n_layer)
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self.parent.assertListEqual(
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list(multihead_outputs[0].size()),
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[self.batch_size * self.n_choices, self.n_head,
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self.seq_length, self.n_embd // self.n_head])
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self.parent.assertEqual(
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len(multihead_outputs[0][:, 1:(self.n_head-1), :, :].nonzero()),
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0)
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self.parent.assertEqual(
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len(multihead_outputs[0][:, 0, :, :].nonzero()),
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self.batch_size * self.n_choices * self.seq_length * self.n_embd // self.n_head)
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self.parent.assertEqual(
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len(multihead_outputs[0][:, self.n_head-1, :, :].nonzero()),
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self.batch_size * self.n_choices * self.seq_length * self.n_embd // self.n_head)
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self.parent.assertListEqual(
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list(multihead_outputs[1].size()),
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[self.batch_size * self.n_choices, self.n_head,
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self.seq_length, self.n_embd // self.n_head])
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self.parent.assertEqual(
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len(multihead_outputs[1].nonzero()),
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multihead_outputs[1].numel())
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self.parent.assertListEqual(
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list(multihead_outputs[-1].size()),
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[self.batch_size * self.n_choices, self.n_head,
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self.seq_length, self.n_embd // self.n_head])
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self.parent.assertEqual(
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len(multihead_outputs[-1][:, 1:, :, :].nonzero()),
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0)
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self.parent.assertEqual(
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len(multihead_outputs[-1][:, 0, :, :].nonzero()),
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self.batch_size * self.n_choices * self.seq_length * self.n_embd // self.n_head)
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def create_and_check_gpt2_for_head_pruning(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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for model_class in (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel):
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model = model_class(config=config, keep_multihead_output=True)
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model.eval()
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transformer = model if isinstance(model, GPT2Model) else model.transformer
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heads_to_prune = {0: list(range(1, self.n_head)),
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-1: [0]}
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transformer.prune_heads(heads_to_prune)
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if isinstance(model, GPT2DoubleHeadsModel):
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output = model(input_ids, mc_token_ids)
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else:
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output = model(input_ids)
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if isinstance(model, GPT2Model):
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output = sum(t.sum() for t in output[0])
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elif isinstance(output, (list, tuple)):
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output = sum(t.sum() for t in output[:-1])
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output = output.sum()
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output.backward()
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multihead_outputs = transformer.get_multihead_outputs()
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self.parent.assertEqual(len(multihead_outputs), self.n_layer)
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self.parent.assertListEqual(
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list(multihead_outputs[0].size()),
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[self.batch_size * self.n_choices, 1,
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self.seq_length, self.n_embd // self.n_head])
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self.parent.assertListEqual(
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list(multihead_outputs[1].size()),
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[self.batch_size * self.n_choices, self.n_head,
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self.seq_length, self.n_embd // self.n_head])
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self.parent.assertListEqual(
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list(multihead_outputs[-1].size()),
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[self.batch_size * self.n_choices, self.n_head-1,
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self.seq_length, self.n_embd // self.n_head])
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def test_default(self):
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self.run_tester(GPT2ModelTest.GPT2ModelTester(self))
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def test_config_to_json_string(self):
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config = GPT2Config(vocab_size_or_config_json_file=99, n_embd=37)
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obj = json.loads(config.to_json_string())
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self.assertEqual(obj["vocab_size"], 99)
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self.assertEqual(obj["n_embd"], 37)
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def test_config_to_json_file(self):
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config_first = GPT2Config(vocab_size_or_config_json_file=99, n_embd=37)
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json_file_path = "/tmp/config.json"
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config_first.to_json_file(json_file_path)
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config_second = GPT2Config.from_json_file(json_file_path)
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os.remove(json_file_path)
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self.assertEqual(config_second.to_dict(), config_first.to_dict())
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@pytest.mark.slow
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/pytorch_pretrained_bert_test/"
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for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(model)
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def run_tester(self, tester):
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config_and_inputs = tester.prepare_config_and_inputs()
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output_result = tester.create_gpt2_model(*config_and_inputs)
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tester.check_gpt2_model_output(output_result)
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output_result = tester.create_gpt2_lm_head(*config_and_inputs)
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tester.check_gpt2_lm_head_output(output_result)
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tester.check_gpt2_lm_head_loss_output(output_result)
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output_result = tester.create_gpt2_double_heads(*config_and_inputs)
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tester.check_gpt2_double_heads_output(output_result)
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tester.check_gpt2_double_heads_loss_output(output_result)
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tester.create_and_check_gpt2_for_headmasking(*config_and_inputs)
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tester.create_and_check_gpt2_for_head_pruning(*config_and_inputs)
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@classmethod
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def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
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"""Creates a random int32 tensor of the shape within the vocab size."""
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if rng is None:
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rng = random.Random()
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total_dims = 1
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for dim in shape:
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total_dims *= dim
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values = []
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for _ in range(total_dims):
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values.append(rng.randint(0, vocab_size - 1))
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return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
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if __name__ == "__main__":
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unittest.main()
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