mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
387 lines
16 KiB
Python
387 lines
16 KiB
Python
# 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)
|
|
|