# 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. import datetime import gc import math import unittest import pytest from transformers import GPT2Config, is_torch_available from transformers.testing_utils import ( backend_empty_cache, require_flash_attn, require_torch, require_torch_gpu, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( GPT2DoubleHeadsModel, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model, GPT2Tokenizer, ) class GPT2ModelTester: 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, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): 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.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 get_large_model_config(self): return GPT2Config.from_pretrained("openai-community/gpt2") def prepare_config_and_inputs( self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False ): 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( gradient_checkpointing=gradient_checkpointing, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, ) 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 get_config( self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False ): return GPT2Config( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_inner=self.intermediate_size, activation_function=self.hidden_act, resid_pdrop=self.hidden_dropout_prob, attn_pdrop=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, gradient_checkpointing=gradient_checkpointing, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, input_mask, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask) 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_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2Model(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_gpt2_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = GPT2Model(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_gpt2_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = GPT2Model(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, head_mask, token_type_ids, *args): model = GPT2LMHeadModel(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, head_mask, token_type_ids, *args, gradient_checkpointing=False ): model = GPT2LMHeadModel(config) model.to(torch_device) if gradient_checkpointing: model.gradient_checkpointing_enable() 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 create_and_check_double_lm_head_model( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args ): model = GPT2DoubleHeadsModel(config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() inputs = { "input_ids": multiple_choice_inputs_ids, "mc_token_ids": mc_token_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, "labels": multiple_choice_inputs_ids, } result = model(**inputs) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices)) def create_and_check_gpt2_for_question_answering( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPT2ForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_gpt2_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPT2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_gpt2_for_token_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPT2ForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_gpt2_weight_initialization(self, config, *args): model = GPT2Model(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer) for key in model.state_dict().keys(): if "c_proj" in key and "weight" in key: self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001) self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01) def create_and_check_cached_forward_with_and_without_attention_mask(self, config, input_ids, *args): # Relevant issue: https://github.com/huggingface/transformers/issues/31943 model = GPT2Model(config) model.to(torch_device) model.eval() # We want this for SDPA, eager works with a `None` attention mask assert ( model.config._attn_implementation == "sdpa" ), "This test assumes the model to have the SDPA implementation for its attention calculations." # Prepare cache and non_cache input, needs a full attention mask cached_len = input_ids.shape[-1] // 2 input_mask = torch.ones(size=input_ids.size()).to(torch_device) cache_inputs = {"input_ids": input_ids[:, :cached_len], "attention_mask": input_mask[:, :cached_len]} non_cache_inputs = {"input_ids": input_ids[:, cached_len:], "attention_mask": input_mask} # Cached forward once with the attention mask provided and the other time without it (which should assume full attention) cache_outputs = model(**cache_inputs) full_outputs_with_attention_mask = model( **non_cache_inputs, past_key_values=cache_outputs.past_key_values ).last_hidden_state full_outputs_without_attention_mask = model( non_cache_inputs["input_ids"], past_key_values=cache_outputs.past_key_values ).last_hidden_state self.parent.assertTrue( torch.allclose(full_outputs_with_attention_mask, full_outputs_without_attention_mask, atol=1e-5) ) 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, "head_mask": head_mask, } return config, inputs_dict @require_torch class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": GPT2Model, "question-answering": GPT2ForQuestionAnswering, "text-classification": GPT2ForSequenceClassification, "text-generation": GPT2LMHeadModel, "token-classification": GPT2ForTokenClassification, "zero-shot": GPT2ForSequenceClassification, } if is_torch_available() else {} ) all_parallelizable_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else () fx_compatible = True test_missing_keys = False test_model_parallel = True # 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) if return_labels: if model_class.__name__ == "GPT2DoubleHeadsModel": inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) inputs_dict["input_ids"] = inputs_dict["labels"] inputs_dict["token_type_ids"] = inputs_dict["labels"] inputs_dict["mc_token_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.num_choices), dtype=torch.long, device=torch_device, ) inputs_dict["mc_labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = GPT2ModelTester(self) self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37) def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) def test_config(self): self.config_tester.run_common_tests() def test_gpt2_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model(*config_and_inputs) def test_gpt2_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs) def test_gpt2_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs) def test_gpt2_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs) def test_gpt2_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_gpt2_double_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs) def test_gpt2_question_answering_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_question_answering(*config_and_inputs) def test_gpt2_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs) def test_gpt2_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_token_classification(*config_and_inputs) def test_gpt2_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) def test_gpt2_scale_attn_by_inverse_layer_idx(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True) self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs) def test_gpt2_reorder_and_upcast_attn(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True) self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs) def test_gpt2_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_weight_initialization(*config_and_inputs) def test_cached_forward_with_and_without_attention_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_cached_forward_with_and_without_attention_mask(*config_and_inputs) @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @slow def test_batch_generation(self): model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") model.to(torch_device) tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") 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().cpu().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 bit of a mess. I'm not sure if he's going", "Today, I'm going to be doing a lot of research on this. I", ] 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_batch_generation_2heads(self): model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2") model.to(torch_device) tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") tokenizer.padding_side = "left" # This tokenizer has no pad token, so we have to set it in some way # 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().cpu().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 bit of a mess. I'm not sure if he's going", "Today, I'm going to be doing a lot of research on this. I", ] 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 = "openai-community/gpt2" model = GPT2Model.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class GPT2ModelLanguageGenerationTest(unittest.TestCase): def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) def _test_lm_generate_gpt2_helper( self, gradient_checkpointing=False, reorder_and_upcast_attn=False, scale_attn_by_inverse_layer_idx=False, verify_outputs=True, ): model = GPT2LMHeadModel.from_pretrained( "openai-community/gpt2", reorder_and_upcast_attn=reorder_and_upcast_attn, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, ) if gradient_checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() model.to(torch_device) # The dog input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290,] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) if verify_outputs: self.assertListEqual(output_ids[0].tolist(), expected_output_ids) @slow def test_lm_generate_gpt2(self): self._test_lm_generate_gpt2_helper() @slow def test_lm_generate_gpt2_with_gradient_checkpointing(self): self._test_lm_generate_gpt2_helper(gradient_checkpointing=True) @slow def test_lm_generate_gpt2_with_reorder_and_upcast_attn(self): self._test_lm_generate_gpt2_helper(reorder_and_upcast_attn=True) @slow def test_lm_generate_gpt2_with_scale_attn_by_inverse_layer_idx(self): self._test_lm_generate_gpt2_helper(scale_attn_by_inverse_layer_idx=True, verify_outputs=False) @slow def test_gpt2_sample(self): tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") 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) EXPECTED_OUTPUT_STR = ( "Today is a nice day and if you don't know anything about the state of play during your holiday" ) 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 @slow def test_gpt2_sample_max_time(self): tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") 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) MAX_TIME = 0.5 start = datetime.datetime.now() model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=False, max_time=None, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) @slow def test_contrastive_search_gpt2(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" ) gpt2_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2-large") gpt2_model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-large").to(torch_device) input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) outputs = gpt2_model.generate(input_ids, penalty_alpha=0.6, top_k=4, max_length=256) generated_text = gpt2_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\n\nGoogle has a lot of data on its users and uses it to improve its products, such as " "Google Now, which helps users find the information they're looking for on the web. But the company " "is not the only one to collect data on its users. Facebook, for example, has its own facial " "recognition technology, as well as a database of millions of photos that it uses to personalize its " "News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates " "concerned about the company's ability to keep users' information private. In a blog post last " 'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our ' 'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with ' 'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at ' 'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, ' "but said in a statement to The Associated Press that" ], ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_left(self): """ Overwritting the common test as the test is flaky on tiny models """ model = GPT2LMHeadModel.from_pretrained("gpt2", torch_dtype=torch.float16).to(0) tokenizer = GPT2Tokenizer.from_pretrained("gpt2") texts = ["hi", "Hello this is a very long sentence"] tokenizer.padding_side = "left" tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0) output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_native = tokenizer.batch_decode(output_native) model = GPT2LMHeadModel.from_pretrained( "gpt2", device_map={"": 0}, attn_implementation="flash_attention_2", torch_dtype=torch.float16 ) output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_fa_2 = tokenizer.batch_decode(output_fa_2) expected_output = [ "<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>hi, who was born in the city of Kolkata, was a member of the Kolkata", "Hello this is a very long sentence. I'm sorry. I'm sorry. I'm sorry. I'm sorry. I'm sorry", ] self.assertListEqual(output_native, output_fa_2) self.assertListEqual(output_native, expected_output)