# coding=utf-8 # Copyright 2019 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import shutil import json import random import torch def create_and_check_for_headmasking(tester, model_classes, config, inputs_dict): for model_class in model_classes: config.output_hidden_states = True model = model_class(config=config) model.eval() head_mask = torch.zeros(tester.num_hidden_layers, tester.num_attention_heads) # Set that after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) head_mask.requires_grad_(requires_grad=True) outputs = model(**inputs_dict, head_mask=head_mask) # Compute some gradients output = sum(t.sum() for t in outputs[0]) output = output.sum() output.backward() multihead_outputs = head_mask.grad tester.parent.assertEqual(len(multihead_outputs), tester.num_hidden_layers) # self.parent.assertListEqual( # list(multihead_outputs[0].size()), # [self.batch_size, self.num_attention_heads, # self.seq_length, self.hidden_size // self.num_attention_heads]) # self.parent.assertEqual( # len(multihead_outputs[0][:, 1:(self.num_attention_heads-1), :, :].nonzero()), # 0) # self.parent.assertEqual( # len(multihead_outputs[0][:, 0, :, :].nonzero()), # self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads) # self.parent.assertEqual( # len(multihead_outputs[0][:, self.num_attention_heads-1, :, :].nonzero()), # self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads) # self.parent.assertListEqual( # list(multihead_outputs[1].size()), # [self.batch_size, self.num_attention_heads, # self.seq_length, self.hidden_size // self.num_attention_heads]) # self.parent.assertEqual( # len(multihead_outputs[1].nonzero()), # multihead_outputs[1].numel()) # self.parent.assertListEqual( # list(multihead_outputs[-1].size()), # [self.batch_size, self.num_attention_heads, # self.seq_length, self.hidden_size // self.num_attention_heads]) # self.parent.assertEqual( # len(multihead_outputs[-1][:, 1:, :, :].nonzero()), # 0) # self.parent.assertEqual( # len(multihead_outputs[-1][:, 0, :, :].nonzero()), # self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads) def create_and_check_for_head_pruning(tester, model_classes, config, inputs_dict): for model_class in model_classes: model = model_class(config=config) model.eval() heads_to_prune = {0: list(range(1, tester.num_attention_heads)), -1: [0]} model.prune_heads(heads_to_prune) outputs = model(**inputs_dict) # output = sum(t.sum() for t in outputs[0]) # output = output.sum() # output.backward() # multihead_outputs = bert_model.get_multihead_outputs() # self.parent.assertEqual(len(multihead_outputs), self.num_hidden_layers) # self.parent.assertListEqual( # list(multihead_outputs[0].size()), # [self.batch_size, 1, # self.seq_length, self.hidden_size // self.num_attention_heads]) # self.parent.assertListEqual( # list(multihead_outputs[1].size()), # [self.batch_size, self.num_attention_heads, # self.seq_length, self.hidden_size // self.num_attention_heads]) # self.parent.assertListEqual( # list(multihead_outputs[-1].size()), # [self.batch_size, self.num_attention_heads-1, # self.seq_length, self.hidden_size // self.num_attention_heads]) def create_and_check_for_attentions(tester, model_classes, config, inputs_dict): for model_class in model_classes: config.output_attentions = True config.output_hidden_states = False model = model_class(config) model.eval() outputs = model(**inputs_dict) attentions = outputs[-1] tester.parent.assertEqual(model.config.output_attentions, True) tester.parent.assertEqual(model.config.output_hidden_states, False) tester.parent.assertEqual(len(attentions), tester.num_hidden_layers) tester.parent.assertListEqual( list(attentions[0].shape[-3:]), [tester.num_attention_heads, tester.seq_length, tester.key_len if hasattr(tester, 'key_len') else tester.seq_length]) out_len = len(outputs) # Check attention is always last and order is fine config.output_attentions = True config.output_hidden_states = True model = model_class(config) model.eval() outputs = model(**inputs_dict) tester.parent.assertEqual(out_len+1, len(outputs)) tester.parent.assertEqual(model.config.output_attentions, True) tester.parent.assertEqual(model.config.output_hidden_states, True) attentions = outputs[-1] tester.parent.assertEqual(len(attentions), tester.num_hidden_layers) tester.parent.assertListEqual( list(attentions[0].shape[-3:]), [tester.num_attention_heads, tester.seq_length, tester.key_len if hasattr(tester, 'key_len') else tester.seq_length]) def create_and_check_for_hidden_states(tester, model_classes, config, inputs_dict): for model_class in model_classes: config.output_hidden_states = True config.output_attentions = False model = model_class(config) model.eval() outputs = model(**inputs_dict) hidden_states = outputs[-1] tester.parent.assertEqual(model.config.output_attentions, False) tester.parent.assertEqual(model.config.output_hidden_states, True) tester.parent.assertEqual(len(hidden_states), tester.num_hidden_layers + 1) tester.parent.assertListEqual( list(hidden_states[0].shape[-2:]), [tester.seq_length, tester.hidden_size]) def create_and_check_commons(tester, config, inputs_dict): create_and_check_for_attentions(tester, tester.all_model_classes, config, inputs_dict) create_and_check_for_headmasking(tester, tester.all_model_classes, config, inputs_dict) create_and_check_for_head_pruning(tester, tester.all_model_classes, config, inputs_dict) create_and_check_for_hidden_states(tester, tester.all_model_classes, config, inputs_dict) def ids_tensor(shape, vocab_size, rng=None, name=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous() class ConfigTester(object): def __init__(self, parent, config_class=None, **kwargs): self.parent = parent self.config_class = config_class self.inputs_dict = kwargs def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, 'hidden_size')) self.parent.assertTrue(hasattr(config, 'num_attention_heads')) self.parent.assertTrue(hasattr(config, 'num_hidden_layers')) def create_and_test_config_to_json_string(self): config = self.config_class(**self.inputs_dict) obj = json.loads(config.to_json_string()) for key, value in self.inputs_dict.items(): self.parent.assertEqual(obj[key], value) def create_and_test_config_to_json_file(self): config_first = self.config_class(**self.inputs_dict) json_file_path = "/tmp/config.json" config_first.to_json_file(json_file_path) config_second = self.config_class.from_json_file(json_file_path) os.remove(json_file_path) self.parent.assertEqual(config_second.to_dict(), config_first.to_dict()) def run_common_tests(self): self.create_and_test_config_common_properties() self.create_and_test_config_to_json_string() self.create_and_test_config_to_json_file() class GPTModelTester(object): def __init__(self, parent, batch_size=13, seq_length=7, is_training=True, use_position_ids=True, use_token_type_ids=True, use_labels=True, vocab_size=99, n_special=1, n_positions=33, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, n_choices=3, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, scope=None, config_class=None, base_model_class=None, lm_head_model_class=None, double_head_model_class=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_position_ids = use_position_ids self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.n_special = n_special self.n_positions = n_positions self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.n_choices = n_choices self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope self.config_class = config_class self.base_model_class = base_model_class self.lm_head_model_class = lm_head_model_class self.double_head_model_class = double_head_model_class self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class) def prepare_config_and_inputs(self): total_num_tokens = self.vocab_size + self.n_special input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens) position_ids = None if self.use_position_ids: position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions) token_type_ids = None if self.use_token_type_ids: total_voc = self.vocab_size token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc) mc_labels = None lm_labels = None mc_token_ids = None if self.use_labels: mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels) mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length) config = self.config_class( vocab_size_or_config_json_file=self.vocab_size, n_special=self.n_special, n_positions=self.n_positions, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, initializer_range=self.initializer_range) return (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = self.base_model_class(config) model.eval() outputs = model(input_ids, position_ids, token_type_ids) hidden_state = outputs[0] self.parent.assertListEqual( list(hidden_state.size()), [self.batch_size, self.n_choices, self.seq_length, self.hidden_size]) def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = self.lm_head_model_class(config) model.eval() outputs = model(input_ids, position_ids, token_type_ids, lm_labels) loss, lm_logits = outputs[:2] total_voc = self.n_special + self.vocab_size self.parent.assertListEqual( list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]) self.parent.assertListEqual( list(loss.size()), []) def create_and_check_presents(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): for model_class in self.all_model_classes: model = model_class(config) model.eval() outputs = model(input_ids) presents = outputs[-1] self.parent.assertEqual(self.num_hidden_layers, len(presents)) self.parent.assertListEqual( list(presents[0].size()), [2, self.batch_size * self.n_choices, self.num_attention_heads, self.seq_length, self.hidden_size // self.num_attention_heads]) def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = self.double_head_model_class(config) model.eval() outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels, token_type_ids=token_type_ids, position_ids=position_ids) lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4] loss = [lm_loss, mc_loss] total_voc = self.n_special + self.vocab_size self.parent.assertListEqual( list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]) self.parent.assertListEqual( list(mc_logits.size()), [self.batch_size, self.n_choices]) self.parent.assertListEqual( [list(l.size()) for l in loss], [[], []]) def create_and_check_model_from_pretrained(self): cache_dir = "/tmp/pytorch_pretrained_bert_test/" for model_name in list(self.base_model_class.PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir) shutil.rmtree(cache_dir) self.parent.assertIsNotNone(model) def create_and_check_commons(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): inputs_dict = {'input_ids': input_ids} create_and_check_commons(self, config, inputs_dict) def run_common_tests(self, test_presents=False): config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_base_model(*config_and_inputs) config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_lm_head(*config_and_inputs) config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_double_heads(*config_and_inputs) if test_presents: config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_presents(*config_and_inputs) config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_commons(*config_and_inputs) def run_slow_tests(self): config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_model_from_pretrained(*config_and_inputs)