# Copyright 2021 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. """Testing suite for the PyTorch GPT Neo model.""" import unittest from functools import cached_property from transformers import GPTNeoConfig, is_torch_available from transformers.testing_utils import require_torch, slow, 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 ( GPT2Tokenizer, GPTNeoForCausalLM, GPTNeoForQuestionAnswering, GPTNeoForSequenceClassification, GPTNeoForTokenClassification, GPTNeoModel, ) class GPTNeoModelTester: 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, attention_types=[[["global", "local"], 1]], 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, window_size=7, 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.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.window_size = window_size 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.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 self.attention_types = attention_types 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 GPTNeoConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, max_position_embeddings=self.max_position_embeddings, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, attention_types=self.attention_types, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def create_and_check_gpt_neo_model(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTNeoModel(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)) # past_key_values is not implemented # self.parent.assertEqual(len(result.past_key_values), config.n_layer) def create_and_check_gpt_neo_model_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTNeoModel(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_gpt_neo_model_attention_mask_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTNeoModel(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_gpt_neo_model_past_large_inputs(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTNeoModel(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 = GPTNeoForCausalLM(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_gpt_neo_for_question_answering( self, config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPTNeoForQuestionAnswering(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_gpt_neo_for_sequence_classification( self, config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPTNeoForSequenceClassification(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_gpt_neo_for_token_classification( self, config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPTNeoForTokenClassification(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_forward_and_backwards( self, config, input_ids, input_mask, token_type_ids, *args, gradient_checkpointing=False ): model = GPTNeoForCausalLM(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 GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( GPTNeoModel, GPTNeoForCausalLM, GPTNeoForQuestionAnswering, GPTNeoForSequenceClassification, GPTNeoForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": GPTNeoModel, "question-answering": GPTNeoForQuestionAnswering, "text-classification": GPTNeoForSequenceClassification, "text-generation": GPTNeoForCausalLM, "token-classification": GPTNeoForTokenClassification, "zero-shot": GPTNeoForSequenceClassification, } if is_torch_available() else {} ) test_missing_keys = 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 = GPTNeoModelTester(self) self.config_tester = ConfigTester(self, config_class=GPTNeoConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_gpt_neo_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_model(*config_and_inputs) def test_gpt_neo_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_model_past(*config_and_inputs) def test_gpt_neo_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_model_attention_mask_past(*config_and_inputs) def test_gpt_neo_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_model_past_large_inputs(*config_and_inputs) def test_gpt_neo_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_gpt_neo_question_answering_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_for_question_answering(*config_and_inputs) def test_gpt_neo_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_for_sequence_classification(*config_and_inputs) def test_gpt_neo_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_neo_for_token_classification(*config_and_inputs) def test_gpt_neo_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 _get_hidden_states(self): return torch.tensor( [ [ [0.4983, -0.7584, -1.6944, 0.5440], [2.6918, 0.4206, 0.4176, 0.2055], [-0.0071, -0.0405, -1.4920, -0.3630], [1.0492, 0.1599, -1.7648, 0.2419], [-1.8348, 2.0514, -0.1946, 0.3203], [0.7672, -1.1600, -1.7118, -0.9056], [0.2986, 0.5372, 0.7729, -0.1927], [0.0285, 0.2629, -1.1156, -1.1992], ] ], dtype=torch.float32, device=torch_device, ) def test_local_attn_probs(self): model = GPTNeoModel.from_pretrained("valhalla/gpt-neo-random-tiny").eval() layer = model.h[1].attn.attention.to(torch_device) hidden_states = self._get_hidden_states() hidden_states = torch.cat([hidden_states, hidden_states - 0.5], dim=2) batch_size, seq_length, _ = hidden_states.shape mask_tokens = 2 attention_mask = torch.ones(batch_size, seq_length, device=torch_device, dtype=torch.long) attention_mask[:, -mask_tokens:] = 0 # dont attend last mask_tokens attention_mask = attention_mask.view(batch_size, -1) attention_mask = attention_mask[:, None, None, :] attention_mask = (1.0 - attention_mask) * -10000.0 attn_probs = layer(hidden_states, attention_mask=attention_mask, output_attentions=True)[-1] # the last 2 tokens are masked, and should have 0 attn_probs self.assertTrue(torch.all(attn_probs[:, :, -mask_tokens:, -mask_tokens:] == 0)) # in local attention each token can only attend to the previous window_size tokens (including itself) # here window_size is 4, so a token at index 5 can only attend to indices [2, 3, 4, 5] # and the attn_probs should be 0 for token [0, 1] self.assertTrue(torch.all(attn_probs[:, :, 5, 2:6] != 0)) self.assertTrue(torch.all(attn_probs[:, :, 5, :2] == 0)) @require_torch class GPTNeoModelLanguageGenerationTest(unittest.TestCase): @cached_property def model(self): return GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B").to(torch_device) @cached_property def tokenizer(self): return GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B") @slow def test_lm_generate_gpt_neo(self): for checkpointing in [True, False]: model = self.model if checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog # The dog-eared copy of the book, which is a collection of essays by the late author, expected_output_ids = [464, 3290, 12, 3380, 4866, 286, 262, 1492, 11, 543, 318, 257, 4947, 286, 27126, 416, 262, 2739, 1772, 11] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids) @slow def test_gpt_neo_sample(self): model = self.model tokenizer = self.tokenizer 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) EXPECTED_OUTPUT_STR = "Today is a nice day and if you don’t get the memo here is what you can" self.assertEqual(output_str, EXPECTED_OUTPUT_STR) @slow def test_batch_generation(self): model = self.model tokenizer = self.tokenizer 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 am", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) outputs = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) 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) 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 kitty. She is a very sweet and loving", "Today, I am going to talk about the best way to get a job in the", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence]) @slow def test_model_from_pretrained(self): model_name = "EleutherAI/gpt-neo-1.3B" model = GPTNeoModel.from_pretrained(model_name) self.assertIsNotNone(model)