# coding=utf-8 # Copyright 2020 The HuggingFace 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 from transformers import AutoTokenizer, GPTJConfig, is_tf_available from transformers.testing_utils import require_tf, slow, tooslow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin if is_tf_available(): import tensorflow as tf from transformers.models.gptj.modeling_tf_gptj import ( TFGPTJForCausalLM, TFGPTJForQuestionAnswering, TFGPTJForSequenceClassification, TFGPTJModel, shape_list, ) class TFGPTJModelTester: def __init__(self, parent): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.rotary_dim = 4 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.bos_token_id = self.vocab_size - 1 self.eos_token_id = self.vocab_size - 1 self.pad_token_id = self.vocab_size - 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = GPTJConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, rotary_dim=self.rotary_dim, return_dict=True, ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def create_and_check_gptj_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPTJModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) inputs = [input_ids, None, input_mask] # None is the input for 'past' result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_gptj_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPTJModel(config=config) # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True) outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids) outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"] output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, 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-6) def create_and_check_gptj_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = TFGPTJModel(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass output, past_key_values = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, 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-12) def create_and_check_gptj_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = TFGPTJModel(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] token_type_ids = token_type_ids[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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 = ids_tensor((self.batch_size, 3), 2) next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model( next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask )["last_hidden_state"] output_from_past = model( next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past_key_values, )["last_hidden_state"] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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) def create_and_check_gptj_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPTJForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_tf class TFGPTJModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel) if is_tf_available() else () ) all_generative_model_classes = (TFGPTJForCausalLM,) if is_tf_available() else () pipeline_model_mapping = ( { "feature-extraction": TFGPTJModel, "question-answering": TFGPTJForQuestionAnswering, "text-classification": TFGPTJForSequenceClassification, "text-generation": TFGPTJForCausalLM, "zero-shot": TFGPTJForSequenceClassification, } if is_tf_available() else {} ) test_onnx = False test_pruning = False test_missing_keys = False test_head_masking = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if ( pipeline_test_casse_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("Fast") ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def setUp(self): self.model_tester = TFGPTJModelTester(self) self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_gptj_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model(*config_and_inputs) def test_gptj_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model_past(*config_and_inputs) def test_gptj_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs) def test_gptj_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs) def test_gptj_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_lm_head_model(*config_and_inputs) @slow @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) > 0, "skip testing on GPU for now to avoid GPU OOM.", ) def test_model_from_pretrained(self): model = TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True) self.assertIsNotNone(model) @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.") def test_resize_token_embeddings(self): super().test_resize_token_embeddings() @require_tf @tooslow # Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM. class TFGPTJModelLanguageGenerationTest(unittest.TestCase): def test_lm_generate_gptj(self): model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True) input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32) # The dog # fmt: off # The dog is a man's best friend. It is a loyal companion, and it is a friend expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545] # fmt: on output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids) def test_gptj_sample(self): tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16") model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True) tokenized = tokenizer("Today is a nice day and", return_tensors="tf") # forces the generation to happen on CPU, to avoid GPU-related quirks with tf.device(":/CPU:0"): output_ids = model.generate(**tokenized, do_sample=True, seed=[42, 0]) output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True) EXPECTED_OUTPUT_STR = "Today is a nice day and I’m going to go for a walk. I’" self.assertEqual(output_str, EXPECTED_OUTPUT_STR) def _get_beam_search_test_objects(self): model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True) tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16") tokenizer.padding_side = "left" # Define PAD Token = EOS Token = 50256 tokenizer.pad_token = tokenizer.eos_token model.config.pad_token_id = model.config.eos_token_id # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] expected_output_sentences = [ "Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia", "Today, I’m going to be talking about a topic that’", ] return model, tokenizer, sentences, expected_output_sentences def test_batch_beam_search(self): # Confirms that we get the expected results with left-padded beam search model, tokenizer, sentences, expected_output_sentences = self._get_beam_search_test_objects() inputs = tokenizer(sentences, return_tensors="tf", padding=True) outputs = model.generate(**inputs, do_sample=False, num_beams=2) batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual(expected_output_sentences, batch_out_sentence) def test_batch_left_padding(self): # Confirms that left-padding is working properly model, tokenizer, sentences, expected_output_sentences = self._get_beam_search_test_objects() inputs = tokenizer(sentences, return_tensors="tf", padding=True) inputs_non_padded = tokenizer(sentences[0], return_tensors="tf") output_non_padded = model.generate(**inputs_non_padded, do_sample=False, num_beams=2) num_paddings = ( shape_list(inputs_non_padded["input_ids"])[-1] - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1], tf.int64)).numpy() ) inputs_padded = tokenizer(sentences[1], return_tensors="tf") output_padded = model.generate( **inputs_padded, do_sample=False, num_beams=2, max_length=model.config.max_length - num_paddings ) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) self.assertListEqual(expected_output_sentences, [non_padded_sentence, padded_sentence]) def test_xla_beam_search(self): # Confirms that XLA is working properly model, tokenizer, sentences, expected_output_sentences = self._get_beam_search_test_objects() inputs = tokenizer(sentences, return_tensors="tf", padding=True) xla_generate = tf.function(model.generate, jit_compile=True) outputs_xla = xla_generate(**inputs, do_sample=False, num_beams=2) xla_sentence = tokenizer.batch_decode(outputs_xla, skip_special_tokens=True) self.assertListEqual(expected_output_sentences, xla_sentence)