490 lines
22 KiB
Python
490 lines
22 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, division, print_function
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import os
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import copy
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import json
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import logging
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import importlib
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import random
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import shutil
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import unittest
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import uuid
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import tempfile
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import pytest
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import sys
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from transformers import is_tf_available, is_torch_available
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if is_tf_available():
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import tensorflow as tf
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import numpy as np
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from transformers import TFPreTrainedModel
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# from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
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else:
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pytestmark = pytest.mark.skip("Require TensorFlow")
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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:
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setattr(configs_no_init, key, 0.0)
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return configs_no_init
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class TFCommonTestCases:
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class TFCommonModelTester(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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def test_initialization(self):
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pass
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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_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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outputs = model(inputs_dict)
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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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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].numpy()
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out_2 = outputs[0].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_pt_tf_model_equivalence(self):
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if not is_torch_available():
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return
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import torch
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import transformers
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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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pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
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pt_model_class = getattr(transformers, pt_model_class_name)
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config.output_hidden_states = True
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tf_model = model_class(config)
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pt_model = pt_model_class(config)
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# Check we can load pt model in tf and vice-versa with model => model functions
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tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))
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for name, key in inputs_dict.items())
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with torch.no_grad():
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pto = pt_model(**pt_inputs_dict)
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tfo = tf_model(inputs_dict)
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max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))
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self.assertLessEqual(max_diff, 2e-2)
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# Check we can load pt model in tf and vice-versa with checkpoint => model functions
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with TemporaryDirectory() as tmpdirname:
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pt_checkpoint_path = os.path.join(tmpdirname, 'pt_model.bin')
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torch.save(pt_model.state_dict(), pt_checkpoint_path)
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tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
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tf_checkpoint_path = os.path.join(tmpdirname, 'tf_model.h5')
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tf_model.save_weights(tf_checkpoint_path)
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pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))
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for name, key in inputs_dict.items())
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with torch.no_grad():
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pto = pt_model(**pt_inputs_dict)
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tfo = tf_model(inputs_dict)
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max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))
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self.assertLessEqual(max_diff, 2e-2)
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def test_compile_tf_model(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')
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optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
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for model_class in self.all_model_classes:
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# Prepare our model
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model = model_class(config)
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# Let's load it from the disk to be sure we can use pretrained weights
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with TemporaryDirectory() as tmpdirname:
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outputs = model(inputs_dict) # build the model
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname)
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outputs_dict = model(input_ids)
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hidden_states = outputs_dict[0]
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# Add a dense layer on top to test intetgration with other keras modules
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outputs = tf.keras.layers.Dense(2, activation='softmax', name='outputs')(hidden_states)
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# Compile extended model
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extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
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extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
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def test_keyword_and_dict_args(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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outputs_dict = model(inputs_dict)
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inputs_keywords = copy.deepcopy(inputs_dict)
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input_ids = inputs_keywords.pop('input_ids')
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outputs_keywords = model(input_ids, **inputs_keywords)
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output_dict = outputs_dict[0].numpy()
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output_keywords = outputs_keywords[0].numpy()
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self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
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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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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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outputs = model(inputs_dict)
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attentions = [t.numpy() for t in 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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self.model_tester.seq_length,
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self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
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out_len = len(outputs)
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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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outputs = model(inputs_dict)
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self.assertEqual(out_len+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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attentions = [t.numpy() for t in outputs[-1]]
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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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self.model_tester.seq_length,
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self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
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def test_headmasking(self):
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pass
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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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# 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.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)
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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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# 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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pass
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# if not self.test_pruning:
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# return
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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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# 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.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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# 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_hidden_states_output(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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config.output_hidden_states = True
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config.output_attentions = False
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model = model_class(config)
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outputs = model(inputs_dict)
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hidden_states = [t.numpy() for t in outputs[-1]]
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self.assertEqual(model.config.output_attentions, False)
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self.assertEqual(model.config.output_hidden_states, True)
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self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.seq_length, self.model_tester.hidden_size])
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def test_resize_tokens_embeddings(self):
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pass
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# original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# if not self.test_resize_embeddings:
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# return
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# for model_class in self.all_model_classes:
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# config = copy.deepcopy(original_config)
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# model = model_class(config)
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# model_vocab_size = config.vocab_size
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# # Retrieve the embeddings and clone theme
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# model_embed = model.resize_token_embeddings(model_vocab_size)
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# cloned_embeddings = model_embed.weight.clone()
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# # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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# model_embed = model.resize_token_embeddings(model_vocab_size + 10)
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# self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
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# # Check that it actually resizes the embeddings matrix
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# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
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# # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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# model_embed = model.resize_token_embeddings(model_vocab_size - 15)
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# self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
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# # Check that it actually resizes the embeddings matrix
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# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
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# # Check that adding and removing tokens has not modified the first part of the embedding matrix.
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# models_equal = True
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# for p1, p2 in zip(cloned_embeddings, model_embed.weight):
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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_model_common_attributes(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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assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
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x = model.get_output_embeddings()
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assert x is None or isinstance(x, tf.keras.layers.Layer)
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def test_tie_model_weights(self):
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pass
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# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# def check_same_values(layer_1, layer_2):
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# equal = True
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# for p1, p2 in zip(layer_1.weight, layer_2.weight):
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# if p1.data.ne(p2.data).sum() > 0:
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# equal = False
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# return equal
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# for model_class in self.all_model_classes:
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# if not hasattr(model_class, 'tie_weights'):
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# continue
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# config.torchscript = True
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# model_not_tied = model_class(config)
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# params_not_tied = list(model_not_tied.parameters())
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# config_tied = copy.deepcopy(config)
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# config_tied.torchscript = False
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# model_tied = model_class(config_tied)
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# params_tied = list(model_tied.parameters())
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# # Check that the embedding layer and decoding layer are the same in size and in value
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# self.assertGreater(len(params_not_tied), len(params_tied))
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# # Check that after resize they remain tied.
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# model_tied.resize_token_embeddings(config.vocab_size + 10)
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# params_tied_2 = list(model_tied.parameters())
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# self.assertGreater(len(params_not_tied), len(params_tied))
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# self.assertEqual(len(params_tied_2), len(params_tied))
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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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first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
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self.assertTrue(tf.math.equal(first, second).numpy().all())
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def test_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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input_ids = inputs_dict["input_ids"]
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del inputs_dict["input_ids"]
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for model_class in self.all_model_classes:
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model = model_class(config)
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wte = model.get_input_embeddings()
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try:
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x = wte(input_ids, mode="embedding")
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except:
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try:
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x = wte([input_ids], mode="embedding")
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except:
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try:
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x = wte([input_ids, None, None, None], mode="embedding")
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except:
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if hasattr(self.model_tester, "embedding_size"):
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x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)
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else:
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x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
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# ^^ In our TF models, the input_embeddings can take slightly different forms,
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# so we try a few of them.
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# We used to fall back to just synthetically creating a dummy tensor of ones:
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#
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inputs_dict["inputs_embeds"] = x
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outputs = model(inputs_dict)
|
|
|
|
|
|
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=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))
|
|
|
|
output = tf.constant(values,
|
|
shape=shape,
|
|
dtype=dtype if dtype is not None else tf.int32)
|
|
|
|
return output
|
|
|
|
|
|
class TFModelUtilsTest(unittest.TestCase):
|
|
@pytest.mark.skipif('tensorflow' not in sys.modules, reason="requires TensorFlow")
|
|
def test_model_from_pretrained(self):
|
|
pass
|
|
# 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()
|