Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| # 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. | |
| import copy | |
| import os | |
| import random | |
| import tempfile | |
| from transformers import is_tf_available, is_torch_available | |
| from .utils import require_tf | |
| if is_tf_available(): | |
| import tensorflow as tf | |
| import numpy as np | |
| # from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP | |
| def _config_zero_init(config): | |
| configs_no_init = copy.deepcopy(config) | |
| for key in configs_no_init.__dict__.keys(): | |
| if "_range" in key or "_std" in key: | |
| setattr(configs_no_init, key, 0.0) | |
| return configs_no_init | |
| class TFModelTesterMixin: | |
| model_tester = None | |
| all_model_classes = () | |
| test_torchscript = True | |
| test_pruning = True | |
| test_resize_embeddings = True | |
| is_encoder_decoder = False | |
| def test_initialization(self): | |
| pass | |
| # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| # configs_no_init = _config_zero_init(config) | |
| # for model_class in self.all_model_classes: | |
| # model = model_class(config=configs_no_init) | |
| # for name, param in model.named_parameters(): | |
| # if param.requires_grad: | |
| # self.assertIn(param.data.mean().item(), [0.0, 1.0], | |
| # msg="Parameter {} of model {} seems not properly initialized".format(name, model_class)) | |
| def test_save_load(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) | |
| outputs = model(inputs_dict) | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| model.save_pretrained(tmpdirname) | |
| model = model_class.from_pretrained(tmpdirname) | |
| after_outputs = model(inputs_dict) | |
| # Make sure we don't have nans | |
| out_1 = after_outputs[0].numpy() | |
| out_2 = outputs[0].numpy() | |
| out_1 = out_1[~np.isnan(out_1)] | |
| out_2 = out_2[~np.isnan(out_2)] | |
| max_diff = np.amax(np.abs(out_1 - out_2)) | |
| self.assertLessEqual(max_diff, 1e-5) | |
| def test_pt_tf_model_equivalence(self): | |
| if not is_torch_available(): | |
| return | |
| import torch | |
| import transformers | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| for model_class in self.all_model_classes: | |
| pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining | |
| pt_model_class = getattr(transformers, pt_model_class_name) | |
| config.output_hidden_states = True | |
| tf_model = model_class(config) | |
| pt_model = pt_model_class(config) | |
| # Check we can load pt model in tf and vice-versa with model => model functions | |
| tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict) | |
| pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model) | |
| # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences | |
| pt_model.eval() | |
| pt_inputs_dict = dict( | |
| (name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items() | |
| ) | |
| with torch.no_grad(): | |
| pto = pt_model(**pt_inputs_dict) | |
| tfo = tf_model(inputs_dict, training=False) | |
| tf_hidden_states = tfo[0].numpy() | |
| pt_hidden_states = pto[0].numpy() | |
| tf_hidden_states[np.isnan(tf_hidden_states)] = 0 | |
| pt_hidden_states[np.isnan(pt_hidden_states)] = 0 | |
| max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states)) | |
| # Debug info (remove when fixed) | |
| if max_diff >= 2e-2: | |
| print("===") | |
| print(model_class) | |
| print(config) | |
| print(inputs_dict) | |
| print(pt_inputs_dict) | |
| self.assertLessEqual(max_diff, 2e-2) | |
| # Check we can load pt model in tf and vice-versa with checkpoint => model functions | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin") | |
| torch.save(pt_model.state_dict(), pt_checkpoint_path) | |
| tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path) | |
| tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5") | |
| tf_model.save_weights(tf_checkpoint_path) | |
| pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path) | |
| # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences | |
| pt_model.eval() | |
| pt_inputs_dict = dict( | |
| (name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items() | |
| ) | |
| with torch.no_grad(): | |
| pto = pt_model(**pt_inputs_dict) | |
| tfo = tf_model(inputs_dict) | |
| tfo = tfo[0].numpy() | |
| pto = pto[0].numpy() | |
| tfo[np.isnan(tfo)] = 0 | |
| pto[np.isnan(pto)] = 0 | |
| max_diff = np.amax(np.abs(tfo - pto)) | |
| self.assertLessEqual(max_diff, 2e-2) | |
| def test_compile_tf_model(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| if self.is_encoder_decoder: | |
| input_ids = { | |
| "decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"), | |
| "encoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="encoder_input_ids", dtype="int32"), | |
| } | |
| else: | |
| input_ids = tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32") | |
| optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) | |
| loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) | |
| metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy") | |
| for model_class in self.all_model_classes: | |
| # Prepare our model | |
| model = model_class(config) | |
| # Let's load it from the disk to be sure we can use pretrained weights | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| outputs = model(inputs_dict) # build the model | |
| model.save_pretrained(tmpdirname) | |
| model = model_class.from_pretrained(tmpdirname) | |
| outputs_dict = model(input_ids) | |
| hidden_states = outputs_dict[0] | |
| # Add a dense layer on top to test intetgration with other keras modules | |
| outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states) | |
| # Compile extended model | |
| extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs]) | |
| extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric]) | |
| def test_keyword_and_dict_args(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) | |
| outputs_dict = model(inputs_dict) | |
| inputs_keywords = copy.deepcopy(inputs_dict) | |
| input_ids = inputs_keywords.pop("input_ids" if not self.is_encoder_decoder else "decoder_input_ids", None) | |
| outputs_keywords = model(input_ids, **inputs_keywords) | |
| output_dict = outputs_dict[0].numpy() | |
| output_keywords = outputs_keywords[0].numpy() | |
| self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6) | |
| def test_attention_outputs(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| decoder_seq_length = ( | |
| self.model_tester.decoder_seq_length | |
| if hasattr(self.model_tester, "decoder_seq_length") | |
| else self.model_tester.seq_length | |
| ) | |
| encoder_seq_length = ( | |
| self.model_tester.encoder_seq_length | |
| if hasattr(self.model_tester, "encoder_seq_length") | |
| else self.model_tester.seq_length | |
| ) | |
| decoder_key_length = ( | |
| self.model_tester.key_length if hasattr(self.model_tester, "key_length") else decoder_seq_length | |
| ) | |
| encoder_key_length = ( | |
| self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length | |
| ) | |
| for model_class in self.all_model_classes: | |
| config.output_attentions = True | |
| config.output_hidden_states = False | |
| model = model_class(config) | |
| outputs = model(inputs_dict) | |
| attentions = [t.numpy() for t in outputs[-1]] | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, False) | |
| self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) | |
| self.assertListEqual( | |
| list(attentions[0].shape[-3:]), | |
| [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], | |
| ) | |
| out_len = len(outputs) | |
| if self.is_encoder_decoder: | |
| self.assertEqual(out_len % 2, 0) | |
| decoder_attentions = outputs[(out_len // 2) - 1] | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, False) | |
| self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) | |
| self.assertListEqual( | |
| list(decoder_attentions[0].shape[-3:]), | |
| [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], | |
| ) | |
| # Check attention is always last and order is fine | |
| config.output_attentions = True | |
| config.output_hidden_states = True | |
| model = model_class(config) | |
| outputs = model(inputs_dict) | |
| self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) | |
| self.assertEqual(model.config.output_attentions, True) | |
| self.assertEqual(model.config.output_hidden_states, True) | |
| attentions = [t.numpy() for t in outputs[-1]] | |
| self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) | |
| self.assertListEqual( | |
| list(attentions[0].shape[-3:]), | |
| [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], | |
| ) | |
| 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) | |
| outputs = model(inputs_dict) | |
| hidden_states = [t.numpy() for t in 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.seq_length, self.model_tester.hidden_size] | |
| ) | |
| 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) | |
| assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) | |
| x = model.get_output_embeddings() | |
| assert x is None or isinstance(x, tf.keras.layers.Layer) | |
| def test_determinism(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) | |
| first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0] | |
| out_1 = first.numpy() | |
| out_2 = second.numpy() | |
| out_1 = out_1[~np.isnan(out_1)] | |
| out_2 = out_2[~np.isnan(out_2)] | |
| max_diff = np.amax(np.abs(out_1 - out_2)) | |
| self.assertLessEqual(max_diff, 1e-5) | |
| def _get_embeds(self, wte, input_ids): | |
| # ^^ In our TF models, the input_embeddings can take slightly different forms, | |
| # so we try a few of them. | |
| # We used to fall back to just synthetically creating a dummy tensor of ones: | |
| try: | |
| x = wte(input_ids, mode="embedding") | |
| except Exception: | |
| try: | |
| x = wte([input_ids], mode="embedding") | |
| except Exception: | |
| try: | |
| x = wte([input_ids, None, None, None], mode="embedding") | |
| except Exception: | |
| if hasattr(self.model_tester, "embedding_size"): | |
| x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32) | |
| else: | |
| x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) | |
| return x | |
| 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) | |
| wte = model.get_input_embeddings() | |
| if not self.is_encoder_decoder: | |
| inputs_dict["inputs_embeds"] = self._get_embeds(wte, input_ids) | |
| else: | |
| inputs_dict["encoder_inputs_embeds"] = self._get_embeds(wte, encoder_input_ids) | |
| inputs_dict["decoder_inputs_embeds"] = self._get_embeds(wte, decoder_input_ids) | |
| 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 | |