# 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. from __future__ import annotations import unittest import numpy as np from transformers import OPTConfig, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, slow 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} @require_tf 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) @require_tf 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_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.build() 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 _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) @require_tf 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 @require_sentencepiece @require_tf class OPTModelIntegrationTests(unittest.TestCase): @slow 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)) @require_tf @slow 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)) @require_tf @slow class TFOPTGenerationTest(unittest.TestCase): @property 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)