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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # 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 unittest | |
| import numpy as np | |
| from transformers import OPTConfig, is_tf_available | |
| from transformers.testing_utils import require_sentencepiece, require_tf, slow, tooslow | |
| from ...test_configuration_common import ConfigTester | |
| from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor | |
| from ...test_pipeline_mixin import PipelineTesterMixin | |
| if is_tf_available(): | |
| import tensorflow as tf | |
| from transformers import GPT2Tokenizer, TFOPTForCausalLM, TFOPTModel | |
| def prepare_opt_inputs_dict(config, input_ids, attention_mask=None, head_mask=None): | |
| if attention_mask is None: | |
| attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) | |
| return {"input_ids": input_ids, "attention_mask": attention_mask} | |
| class TFOPTModelTester: | |
| config_cls = OPTConfig | |
| config_updates = {} | |
| hidden_act = "gelu" | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=13, | |
| seq_length=7, | |
| is_training=True, | |
| use_labels=False, | |
| vocab_size=99, | |
| hidden_size=16, | |
| num_hidden_layers=2, | |
| num_attention_heads=4, | |
| intermediate_size=4, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=20, | |
| eos_token_id=2, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| embed_dim=16, | |
| word_embed_proj_dim=16, | |
| ): | |
| self.parent = parent | |
| self.batch_size = batch_size | |
| self.seq_length = seq_length | |
| self.is_training = is_training | |
| self.use_labels = use_labels | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.intermediate_size = intermediate_size | |
| self.hidden_act = hidden_act | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.eos_token_id = eos_token_id | |
| self.pad_token_id = pad_token_id | |
| self.bos_token_id = bos_token_id | |
| self.embed_dim = embed_dim | |
| self.word_embed_proj_dim = word_embed_proj_dim | |
| self.is_encoder_decoder = False | |
| def prepare_config_and_inputs_for_common(self): | |
| input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) | |
| eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) | |
| input_ids = tf.concat([input_ids, eos_tensor], axis=1) | |
| config = self.config_cls( | |
| vocab_size=self.vocab_size, | |
| hidden_size=self.hidden_size, | |
| num_hidden_layers=self.num_hidden_layers, | |
| num_attention_heads=self.num_attention_heads, | |
| ffn_dim=self.intermediate_size, | |
| dropout=self.hidden_dropout_prob, | |
| attention_dropout=self.attention_probs_dropout_prob, | |
| max_position_embeddings=self.max_position_embeddings, | |
| eos_token_id=self.eos_token_id, | |
| bos_token_id=self.bos_token_id, | |
| pad_token_id=self.pad_token_id, | |
| embed_dim=self.embed_dim, | |
| word_embed_proj_dim=self.word_embed_proj_dim, | |
| is_encoder_decoder=False, | |
| **self.config_updates, | |
| ) | |
| inputs_dict = prepare_opt_inputs_dict(config, input_ids) | |
| return config, inputs_dict | |
| def check_decoder_model_past_large_inputs(self, config, inputs_dict): | |
| model = TFOPTModel(config=config) | |
| input_ids = inputs_dict["input_ids"] | |
| input_ids = input_ids[:1, :] | |
| attention_mask = inputs_dict["attention_mask"][:1, :] | |
| self.batch_size = 1 | |
| # first forward pass | |
| outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) | |
| output, past_key_values = outputs.to_tuple() | |
| # create hypothetical next token and extent to next_input_ids | |
| next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) | |
| next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8) | |
| # append to next input_ids and | |
| next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) | |
| next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) | |
| output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] | |
| output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0] | |
| self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) | |
| # select random slice | |
| random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) | |
| output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] | |
| output_from_past_slice = output_from_past[:, :, random_slice_idx] | |
| # test that outputs are equal for slice | |
| tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) | |
| class TFOPTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): | |
| all_model_classes = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else () | |
| all_generative_model_classes = (TFOPTForCausalLM,) if is_tf_available() else () | |
| pipeline_model_mapping = ( | |
| {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {} | |
| ) | |
| is_encoder_decoder = False | |
| test_pruning = False | |
| test_onnx = False | |
| onnx_min_opset = 10 | |
| def setUp(self): | |
| self.model_tester = TFOPTModelTester(self) | |
| self.config_tester = ConfigTester(self, config_class=OPTConfig) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_decoder_model_past_large_inputs(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() | |
| self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) | |
| 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) | |
| if model_class in self.all_generative_model_classes: | |
| x = model.get_output_embeddings() | |
| assert isinstance(x, tf.keras.layers.Layer) | |
| else: | |
| x = model.get_output_embeddings() | |
| assert x is None | |
| def test_resize_token_embeddings(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| def _get_word_embedding_weight(model, embedding_layer): | |
| if hasattr(embedding_layer, "weight"): | |
| return embedding_layer.weight | |
| else: | |
| # Here we build the word embeddings weights if not exists. | |
| # And then we retry to get the attribute once built. | |
| model(model.dummy_inputs) | |
| if hasattr(embedding_layer, "weight"): | |
| return embedding_layer.weight | |
| else: | |
| return None | |
| for model_class in self.all_model_classes: | |
| for size in [config.vocab_size - 10, config.vocab_size + 10]: | |
| # build the embeddings | |
| model = model_class(config=config) | |
| old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) | |
| old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) | |
| # reshape the embeddings | |
| model.resize_token_embeddings(size) | |
| new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) | |
| new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) | |
| # check that the resized embeddings size matches the desired size. | |
| assert_size = size if size is not None else config.vocab_size | |
| self.assertEqual(new_input_embeddings.shape[0], assert_size) | |
| # check that weights remain the same after resizing | |
| models_equal = True | |
| for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()): | |
| if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: | |
| models_equal = False | |
| self.assertTrue(models_equal) | |
| if old_output_embeddings is not None and new_output_embeddings is not None: | |
| self.assertEqual(new_output_embeddings.shape[0], assert_size) | |
| models_equal = True | |
| for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()): | |
| if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: | |
| models_equal = False | |
| self.assertTrue(models_equal) | |
| def test_saved_model_creation(self): | |
| pass | |
| def _long_tensor(tok_lst): | |
| return tf.constant(tok_lst, dtype=tf.int32) | |
| class TFOPTHeadTests(unittest.TestCase): | |
| vocab_size = 99 | |
| def _get_config_and_data(self): | |
| eos_column_vector = tf.ones((4, 1), dtype=tf.int32) * 2 | |
| input_ids = tf.concat([ids_tensor((4, 6), self.vocab_size - 3) + 3, eos_column_vector], axis=1) | |
| batch_size = input_ids.shape[0] | |
| config = OPTConfig( | |
| vocab_size=self.vocab_size, | |
| hidden_size=24, | |
| num_hidden_layers=2, | |
| num_attention_heads=2, | |
| ffn_dim=32, | |
| max_position_embeddings=48, | |
| eos_token_id=2, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| ) | |
| return config, input_ids, batch_size | |
| class OPTModelIntegrationTests(unittest.TestCase): | |
| def test_inference_no_head(self): | |
| model = TFOPTModel.from_pretrained("facebook/opt-350m") | |
| input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) | |
| attention_mask = tf.not_equal(input_ids, model.config.pad_token_id) | |
| with tf.GradientTape(): | |
| output = model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state | |
| expected_shape = (1, 11, 512) | |
| self.assertEqual(output.shape, expected_shape) | |
| expected_slice = tf.constant( | |
| [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] | |
| ) | |
| self.assertTrue(np.allclose(output[:, :3, :3], expected_slice, atol=4e-3)) | |
| xla_generate = tf.function(model, jit_compile=True) | |
| output = xla_generate(input_ids, attention_mask)[0] | |
| self.assertTrue(np.allclose(output[:, :3, :3], expected_slice, atol=4e-2)) | |
| class TFOPTEmbeddingsTest(unittest.TestCase): | |
| def setUp(self): | |
| super().setUp() | |
| self.path_model = "facebook/opt-350m" | |
| def test_logits(self): | |
| model = TFOPTForCausalLM.from_pretrained(self.path_model) | |
| tokenizer = GPT2Tokenizer.from_pretrained(self.path_model) | |
| prompts = [ | |
| "Today is a beautiful day and I want to", | |
| "In the city of", | |
| "Paris is the capital of France and", | |
| "Computers and mobile phones have taken", | |
| ] | |
| # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False | |
| inputs = tokenizer(prompts, return_tensors="tf", padding=True, add_special_tokens=False) | |
| logits = tf.math.reduce_mean(model(inputs.input_ids, attention_mask=inputs.attention_mask)[0], axis=-1) | |
| logits_meta = tf.constant( | |
| [ | |
| [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670], | |
| [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822], | |
| [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703], | |
| [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477], | |
| ] | |
| ) | |
| self.assertTrue(np.allclose(logits, logits_meta, atol=1e-4)) | |
| xla_generate = tf.function(model, jit_compile=True) | |
| logits = tf.math.reduce_mean(xla_generate(inputs.input_ids, attention_mask=inputs.attention_mask)[0], axis=-1) | |
| self.assertTrue(np.allclose(logits, logits_meta, atol=1e-4)) | |
| class TFOPTGenerationTest(unittest.TestCase): | |
| def prompts(self): | |
| return [ | |
| "Today is a beautiful day and I want", | |
| "In the city of", | |
| "Paris is the capital of France and", | |
| "Computers and mobile phones have taken", | |
| ] | |
| def test_generation_pre_attn_layer_norm(self): | |
| model_id = "facebook/opt-125m" | |
| EXPECTED_OUTPUTS = [ | |
| "Today is a beautiful day and I want to", | |
| "In the city of New York, the city", | |
| "Paris is the capital of France and the capital", | |
| "Computers and mobile phones have taken over the", | |
| ] | |
| predicted_outputs = [] | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_id) | |
| model = TFOPTForCausalLM.from_pretrained(model_id) | |
| for prompt in self.prompts: | |
| input_ids = tokenizer(prompt, return_tensors="tf").input_ids | |
| generated_ids = model.generate(input_ids, max_length=10) | |
| generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) | |
| predicted_outputs += generated_string | |
| self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) | |
| def test_batch_generation(self): | |
| model_id = "facebook/opt-350m" | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_id) | |
| model = TFOPTForCausalLM.from_pretrained(model_id) | |
| tokenizer.padding_side = "left" | |
| # use different length sentences to test batching | |
| sentences = [ | |
| "Hello, my dog is a little", | |
| "Today, I", | |
| ] | |
| inputs = tokenizer(sentences, return_tensors="tf", padding=True) | |
| input_ids = inputs["input_ids"] | |
| outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"]) | |
| inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids | |
| output_non_padded = model.generate(input_ids=inputs_non_padded) | |
| num_paddings = inputs_non_padded.shape[-1] - tf.math.reduce_sum( | |
| tf.cast(inputs["attention_mask"][-1], tf.int64) | |
| ) | |
| inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids | |
| output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) | |
| batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) | |
| non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) | |
| padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) | |
| expected_output_sentence = [ | |
| "Hello, my dog is a little bit of a dork.\nI'm a little bit", | |
| "Today, I was in the middle of a conversation with a friend about the", | |
| ] | |
| self.assertListEqual(expected_output_sentence, batch_out_sentence) | |
| self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence]) | |
| def test_generation_post_attn_layer_norm(self): | |
| model_id = "facebook/opt-350m" | |
| EXPECTED_OUTPUTS = [ | |
| "Today is a beautiful day and I want to", | |
| "In the city of San Francisco, the city", | |
| "Paris is the capital of France and the capital", | |
| "Computers and mobile phones have taken over the", | |
| ] | |
| predicted_outputs = [] | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_id) | |
| model = TFOPTForCausalLM.from_pretrained(model_id) | |
| for prompt in self.prompts: | |
| input_ids = tokenizer(prompt, return_tensors="tf").input_ids | |
| generated_ids = model.generate(input_ids, max_length=10) | |
| generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) | |
| predicted_outputs += generated_string | |
| self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) | |