# Copyright 2021 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. import unittest from transformers import GPTJConfig, is_torch_available from transformers.testing_utils import ( require_torch, slow, tooslow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoTokenizer, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, ) class GPTJModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_token_type_ids=True, use_input_mask=True, use_labels=True, use_mc_token_ids=True, vocab_size=99, hidden_size=32, rotary_dim=4, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_token_type_ids = use_token_type_ids self.use_input_mask = use_input_mask self.use_labels = use_labels self.use_mc_token_ids = use_mc_token_ids self.vocab_size = vocab_size self.hidden_size = hidden_size self.rotary_dim = rotary_dim 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.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = 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 = self.get_config() return ( config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def get_config(self): return 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, use_cache=True, 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, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def create_and_check_gptj_model(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(len(result.past_key_values), config.n_layer) def create_and_check_gptj_model_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.to(torch_device) model.eval() # 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 = 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 = torch.cat([input_ids, next_tokens], dim=-1) next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-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)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_gptj_model_attention_mask_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = self.seq_length // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past = 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).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=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, attention_mask=attn_mask)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_gptj_model_past_large_inputs(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True) output, past = 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_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-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 )["last_hidden_state"] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_lm_head_model(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, token_type_ids, *args, gradient_checkpointing=False ): model = GPTJForCausalLM(config) if gradient_checkpointing: model.gradient_checkpointing_enable() model.to(torch_device) result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) result.loss.backward() def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_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} return config, inputs_dict @require_torch class GPTJModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification, GPTJForQuestionAnswering) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": GPTJModel, "question-answering": GPTJForQuestionAnswering, "text-classification": GPTJForSequenceClassification, "text-generation": GPTJForCausalLM, "zero-shot": GPTJForSequenceClassification, } if is_torch_available() else {} ) test_missing_keys = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if ( pipeline_test_case_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 # special case for DoubleHeads model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) return inputs_dict def setUp(self): self.model_tester = GPTJModelTester(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_lm_head_model(*config_and_inputs) def test_gptj_gradient_checkpointing(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True) @tooslow def test_batch_generation(self): # Marked as @tooslow due to GPU OOM model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", dtype=torch.float16) model.to(torch_device) 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", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) token_type_ids = torch.cat( [ input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0), input_ids.new_full((input_ids.shape[0], 1), 500), ], dim=-1, ) outputs = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) outputs_tt = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), token_type_ids=token_type_ids, ) inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device) output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().item() inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device) 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) batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, 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 over a year old and has been diagnosed with a heart murmur", "Today, I’m going to talk about the most important thing in the", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence]) @slow def test_model_from_pretrained(self): model_name = "EleutherAI/gpt-j-6B" model = GPTJModel.from_pretrained(model_name, revision="float16", dtype=torch.float16) self.assertIsNotNone(model) @require_torch class GPTJModelLanguageGenerationTest(unittest.TestCase): @tooslow def test_lm_generate_gptj(self): # Marked as @tooslow due to GPU OOM for checkpointing in [True, False]: model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", dtype=torch.float16) if checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() model.to(torch_device) input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog # 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: skip output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids) @tooslow def test_gptj_sample(self): # Marked as @tooslow due to GPU OOM (issue #13676) tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16") model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", dtype=torch.float16) model.to(torch_device) torch.manual_seed(0) tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True) input_ids = tokenized.input_ids.to(torch_device) output_ids = model.generate(input_ids, do_sample=True) output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True) token_type_ids = tokenized.token_type_ids.to(torch_device) output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5) output_seq_tt = model.generate( input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5 ) output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True) output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True) if torch_device != "cpu": # currently this expect value is only for `cuda` EXPECTED_OUTPUT_STR = ( "Today is a nice day and I've already been enjoying it. I walked to work with my wife" ) else: EXPECTED_OUTPUT_STR = "Today is a nice day and one of those days that feels a bit more alive. I am ready" self.assertEqual(output_str, EXPECTED_OUTPUT_STR) self.assertTrue( all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))) ) # token_type_ids should change output # TODO joao, manuel: remove this in v4.62.0 @tooslow def test_contrastive_search_gptj(self): article = ( "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and " "research laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based" ) tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", dtype=torch.float16).to( torch_device ) input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device) outputs = model.generate( input_ids, penalty_alpha=0.6, top_k=4, max_length=256, trust_remote_code=True, custom_generate="transformers-community/contrastive-search", ) generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, " "United Kingdom with offices in Mountain View, San Francisco, New York City, Paris, Tokyo, Seoul, " "Beijing, Singapore, Tel Aviv, Dublin, Sydney, and Melbourne.[1]\n\nContents\n\nIn 2010, Google's " "parent company, Alphabet, announced a $500 million investment in DeepMind, with the aim of creating " "a company that would apply deep learning to problems in healthcare, energy, transportation, and " "other areas.[2]\n\nOn April 23, 2014, Google announced that it had acquired DeepMind for $400 " "million in cash and stock.[3] The acquisition was seen as a way for Google to enter the " "fast-growing field of artificial intelligence (AI), which it had so far avoided due to concerns " 'about ethical and social implications.[4] Google co-founder Sergey Brin said that he was "thrilled" ' 'to have acquired DeepMind, and that it would "help us push the boundaries of AI even further."' "[5]\n\nDeepMind's founders, Demis Hassabis and Mustafa Suleyman, were joined by a number of Google " "employees" ], )