mirror of
https://github.com/huggingface/transformers.git
synced 2025-08-03 03:31:05 +06:00
b670c26684
Caching models across test cases and across runs of the test suite makes slow tests somewhat more bearable. Use gettempdir() instead of /tmp in tests. This makes it easier to change the location of the cache with semi-standard TMPDIR/TEMP/TMP environment variables. Fix #2222.
880 lines
38 KiB
Python
880 lines
38 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace Inc.
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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 copy
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import sys
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import os.path
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import shutil
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import tempfile
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import json
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import random
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import uuid
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import unittest
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import logging
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from transformers import is_torch_available
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from .utils import CACHE_DIR, require_torch, slow, torch_device
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if is_torch_available():
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import torch
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import numpy as np
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from transformers import (AdaptiveEmbedding, PretrainedConfig, PreTrainedModel,
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BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
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if sys.version_info[0] == 2:
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import cPickle as pickle
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class TemporaryDirectory(object):
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"""Context manager for tempfile.mkdtemp() so it's usable with "with" statement."""
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def __enter__(self):
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self.name = tempfile.mkdtemp()
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return self.name
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def __exit__(self, exc_type, exc_value, traceback):
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shutil.rmtree(self.name)
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else:
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import pickle
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TemporaryDirectory = tempfile.TemporaryDirectory
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unicode = str
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if '_range' in key or '_std' in key or 'initializer_factor' in key:
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setattr(configs_no_init, key, 0.0)
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return configs_no_init
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class CommonTestCases:
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@require_torch
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class CommonModelTester(unittest.TestCase):
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model_tester = None
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all_model_classes = ()
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test_torchscript = True
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test_pruning = True
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test_resize_embeddings = True
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test_head_masking = True
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is_encoder_decoder = False
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def test_save_load(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**inputs_dict)
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out_2 = outputs[0].numpy()
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out_2[np.isnan(out_2)] = 0
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with TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname)
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model.to(torch_device)
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with torch.no_grad():
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after_outputs = model(**inputs_dict)
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# Make sure we don't have nans
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out_1 = after_outputs[0].cpu().numpy()
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out_1[np.isnan(out_1)] = 0
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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self.assertIn(param.data.mean().item(), [0.0, 1.0],
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msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
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def test_determinism(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**inputs_dict)[0]
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second = model(**inputs_dict)[0]
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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decoder_seq_length = self.model_tester.decoder_seq_length if hasattr(self.model_tester, 'decoder_seq_length') else self.model_tester.seq_length
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encoder_seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length
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decoder_key_length = self.model_tester.key_length if hasattr(self.model_tester, 'key_length') else decoder_seq_length
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encoder_key_length = self.model_tester.key_length if hasattr(self.model_tester, 'key_length') else encoder_seq_length
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for model_class in self.all_model_classes:
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config.output_attentions = True
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config.output_hidden_states = False
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(model.config.output_attentions, True)
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self.assertEqual(model.config.output_hidden_states, False)
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads,
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encoder_seq_length ,
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encoder_key_length])
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out_len = len(outputs)
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if self.is_encoder_decoder:
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self.assertEqual(out_len % 2, 0)
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decoder_attentions = outputs[(out_len // 2)-1]
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self.assertEqual(model.config.output_attentions, True)
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self.assertEqual(model.config.output_hidden_states, False)
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads,
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decoder_seq_length,
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decoder_key_length
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])
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# Check attention is always last and order is fine
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config.output_attentions = True
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config.output_hidden_states = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**inputs_dict)
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self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
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self.assertEqual(model.config.output_attentions, True)
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self.assertEqual(model.config.output_hidden_states, True)
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self_attentions = outputs[-1]
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads,
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encoder_seq_length,
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encoder_key_length])
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def test_torchscript(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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self._create_and_check_torchscript(config, inputs_dict)
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def test_torchscript_output_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_attentions = True
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self._create_and_check_torchscript(config, inputs_dict)
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def test_torchscript_output_hidden_state(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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self._create_and_check_torchscript(config, inputs_dict)
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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inputs = inputs_dict['input_ids'] # Let's keep only input_ids
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try:
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traced_gpt2 = torch.jit.trace(model, inputs)
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_gpt2, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_params = model.parameters()
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loaded_model_params = loaded_model.parameters()
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models_equal = True
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for p1, p2 in zip(model_params, loaded_model_params):
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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def test_headmasking(self):
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if not self.test_head_masking:
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return
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global_rng.seed(42)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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global_rng.seed()
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config.output_attentions = True
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config.output_hidden_states = True
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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# Prepare head_mask
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# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
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head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device)
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head_mask[0, 0] = 0
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head_mask[-1, :-1] = 0
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head_mask.requires_grad_(requires_grad=True)
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inputs = inputs_dict.copy()
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inputs['head_mask'] = head_mask
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outputs = model(**inputs)
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# Test that we can get a gradient back for importance score computation
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output = sum(t.sum() for t in outputs[0])
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output = output.sum()
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output.backward()
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multihead_outputs = head_mask.grad
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attentions = outputs[-1]
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hidden_states = outputs[-2]
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# Remove Nan
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for t in attentions:
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self.assertLess(torch.sum(torch.isnan(t)), t.numel() / 4) # Check we don't have more than 25% nans (arbitrary)
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attentions = [t.masked_fill(torch.isnan(t), 0.0) for t in attentions] # remove them (the test is less complete)
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self.assertIsNotNone(multihead_outputs)
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self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
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self.assertAlmostEqual(
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attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
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self.assertNotEqual(
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attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
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self.assertNotEqual(
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attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
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self.assertAlmostEqual(
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attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
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self.assertNotEqual(
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attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
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def test_head_pruning(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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config.output_attentions = True
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config.output_hidden_states = False
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0]}
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model.prune_heads(heads_to_prune)
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(
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attentions[0].shape[-3], 1)
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self.assertEqual(
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attentions[1].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(
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attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_head_pruning_save_load_from_pretrained(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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config.output_attentions = True
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config.output_hidden_states = False
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0]}
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model.prune_heads(heads_to_prune)
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with TemporaryDirectory() as temp_dir_name:
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model.save_pretrained(temp_dir_name)
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model = model_class.from_pretrained(temp_dir_name)
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model.to(torch_device)
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_head_pruning_save_load_from_config_init(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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config.output_attentions = True
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config.output_hidden_states = False
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heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
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-1: [0]}
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config.pruned_heads = heads_to_prune
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_head_pruning_integration(self):
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if not self.test_pruning:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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config.output_attentions = True
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config.output_hidden_states = False
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heads_to_prune = {0: [0], 1: [1, 2]}
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config.pruned_heads = heads_to_prune
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model = model_class(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
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self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
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with TemporaryDirectory() as temp_dir_name:
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model.save_pretrained(temp_dir_name)
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model = model_class.from_pretrained(temp_dir_name)
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model.to(torch_device)
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
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self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
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self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
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heads_to_prune = {0: [0], 2: [1, 2]}
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model.prune_heads(heads_to_prune)
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with torch.no_grad():
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outputs = model(**inputs_dict)
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attentions = outputs[-1]
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self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads -1)
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self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
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self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
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self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
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self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
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def test_hidden_states_output(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
config.output_hidden_states = True
|
|
config.output_attentions = False
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**inputs_dict)
|
|
hidden_states = outputs[-1]
|
|
self.assertEqual(model.config.output_attentions, False)
|
|
self.assertEqual(model.config.output_hidden_states, True)
|
|
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length,
|
|
self.model_tester.hidden_size])
|
|
|
|
def test_resize_tokens_embeddings(self):
|
|
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config)
|
|
|
|
model_vocab_size = config.vocab_size
|
|
# Retrieve the embeddings and clone theme
|
|
model_embed = model.resize_token_embeddings(model_vocab_size)
|
|
cloned_embeddings = model_embed.weight.clone()
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
|
|
|
|
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
|
|
models_equal = True
|
|
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_model_common_attributes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIsInstance(
|
|
model.get_input_embeddings(),
|
|
(torch.nn.Embedding, AdaptiveEmbedding)
|
|
)
|
|
model.set_input_embeddings(torch.nn.Embedding(10, 10))
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(
|
|
x is None or isinstance(x, torch.nn.Linear)
|
|
)
|
|
|
|
def test_tie_model_weights(self):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def check_same_values(layer_1, layer_2):
|
|
equal = True
|
|
for p1, p2 in zip(layer_1.weight, layer_2.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
equal = False
|
|
return equal
|
|
|
|
for model_class in self.all_model_classes:
|
|
config.torchscript = True
|
|
model_not_tied = model_class(config)
|
|
if model_not_tied.get_output_embeddings() is None:
|
|
continue
|
|
|
|
params_not_tied = list(model_not_tied.parameters())
|
|
|
|
config_tied = copy.deepcopy(config)
|
|
config_tied.torchscript = False
|
|
model_tied = model_class(config_tied)
|
|
params_tied = list(model_tied.parameters())
|
|
|
|
# Check that the embedding layer and decoding layer are the same in size and in value
|
|
self.assertGreater(len(params_not_tied), len(params_tied))
|
|
# self.assertTrue(check_same_values(embeddings, decoding))
|
|
|
|
# # Check that after modification, they remain the same.
|
|
# embeddings.weight.data.div_(2)
|
|
# # Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
|
|
# self.assertTrue(check_same_values(embeddings, decoding))
|
|
|
|
# # Check that after modification, they remain the same.
|
|
# decoding.weight.data.div_(4)
|
|
# # Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
|
|
# self.assertTrue(check_same_values(embeddings, decoding))
|
|
|
|
# Check that after resize they remain tied.
|
|
model_tied.resize_token_embeddings(config.vocab_size + 10)
|
|
params_tied_2 = list(model_tied.parameters())
|
|
self.assertGreater(len(params_not_tied), len(params_tied))
|
|
self.assertEqual(len(params_tied_2), len(params_tied))
|
|
|
|
# decoding.weight.data.mul_(20)
|
|
# # Check that the embedding layer and decoding layer are the same in size and in value
|
|
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
|
|
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
|
|
|
|
def test_inputs_embeds(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.is_encoder_decoder:
|
|
input_ids = inputs_dict["input_ids"]
|
|
del inputs_dict["input_ids"]
|
|
else:
|
|
encoder_input_ids = inputs_dict["encoder_input_ids"]
|
|
decoder_input_ids = inputs_dict["decoder_input_ids"]
|
|
del inputs_dict["encoder_input_ids"]
|
|
del inputs_dict["decoder_input_ids"]
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
wte = model.get_input_embeddings()
|
|
if not self.is_encoder_decoder:
|
|
inputs_dict["inputs_embeds"] = wte(input_ids)
|
|
else:
|
|
inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids)
|
|
inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs_dict)
|
|
|
|
class GPTModelTester(CommonModelTester):
|
|
|
|
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_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_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
|
|
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=self.vocab_size,
|
|
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.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(input_ids, position_ids, token_type_ids)
|
|
outputs = model(input_ids, position_ids)
|
|
outputs = model(input_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.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
|
|
loss, lm_logits = outputs[:2]
|
|
|
|
total_voc = 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.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
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.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
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.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):
|
|
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)
|
|
self.parent.assertIsNotNone(model)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(config, input_ids, token_type_ids, position_ids,
|
|
mc_labels, lm_labels, mc_token_ids) = config_and_inputs
|
|
inputs_dict = {'input_ids': input_ids}
|
|
return 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)
|
|
|
|
@slow
|
|
def run_slow_tests(self):
|
|
self.create_and_check_model_from_pretrained()
|
|
|
|
|
|
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, 'vocab_size'))
|
|
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 = os.path.join(os.getcwd(), "config_" + str(uuid.uuid4()) + ".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()
|
|
|
|
|
|
global_rng = random.Random()
|
|
|
|
|
|
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 = global_rng
|
|
|
|
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, device=torch_device).view(shape).contiguous()
|
|
|
|
|
|
def floats_tensor(shape, scale=1.0, rng=None, name=None):
|
|
"""Creates a random float32 tensor of the shape within the vocab size."""
|
|
if rng is None:
|
|
rng = global_rng
|
|
|
|
total_dims = 1
|
|
for dim in shape:
|
|
total_dims *= dim
|
|
|
|
values = []
|
|
for _ in range(total_dims):
|
|
values.append(rng.random() * scale)
|
|
|
|
return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
|
|
|
|
|
|
@require_torch
|
|
class ModelUtilsTest(unittest.TestCase):
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
logging.basicConfig(level=logging.INFO)
|
|
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
|
config = BertConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, PretrainedConfig)
|
|
|
|
model = BertModel.from_pretrained(model_name)
|
|
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, PreTrainedModel)
|
|
for value in loading_info.values():
|
|
self.assertEqual(len(value), 0)
|
|
|
|
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
|
|
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
|
|
self.assertEqual(model.config.output_attentions, True)
|
|
self.assertEqual(model.config.output_hidden_states, True)
|
|
self.assertEqual(model.config, config)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|