# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch BioGPT model. """ import math import unittest from transformers import BioGptConfig, 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 ( BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification, BioGptModel, BioGptTokenizer, ) from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST class BioGptModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, 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_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.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 = scope 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) 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return BioGptConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=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, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BioGptModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) 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_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = BioGptForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_biogpt_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = BioGptModel(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_biogpt_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = BioGptModel(config=config).to(torch_device).eval() attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple 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) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "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[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # 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_forward_and_backwards( self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False ): model = BioGptForCausalLM(config) model.to(torch_device) if gradient_checkpointing: model.gradient_checkpointing_enable() result = model(input_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_biogpt_weight_initialization(self, config, *args): model = BioGptModel(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers) 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_biogpt_for_token_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): config.num_labels = self.num_labels model = BioGptForTokenClassification(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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class BioGptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification) if is_torch_available() else () ) all_generative_model_classes = (BioGptForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": BioGptModel, "text-classification": BioGptForSequenceClassification, "text-generation": BioGptForCausalLM, "token-classification": BioGptForTokenClassification, "zero-shot": BioGptForSequenceClassification, } if is_torch_available() else {} ) test_pruning = False def setUp(self): self.model_tester = BioGptModelTester(self) self.config_tester = ConfigTester(self, config_class=BioGptConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_biogpt_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_model_attention_mask_past(*config_and_inputs) def test_biogpt_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_biogpt_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_model_past_large_inputs(*config_and_inputs) def test_biogpt_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_weight_initialization(*config_and_inputs) def test_biogpt_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_biogpt_for_token_classification(*config_and_inputs) @slow def test_batch_generation(self): model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") model.to(torch_device) tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") 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) 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().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) 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 bigger than a little bit.", "Today, I have a good idea of how to use the information", ] 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): for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BioGptModel.from_pretrained(model_name) self.assertIsNotNone(model) # Copied from tests.models.opt.test_modeling_opt.OPTModelTest with OPT->BioGpt, prepare_config_and_inputs-> prepare_config_and_inputs_for_common def test_biogpt_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = BioGptForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) # Copied from tests.models.opt.test_modeling_opt.OPTModelTest with OPT->BioGpt, prepare_config_and_inputs-> prepare_config_and_inputs_for_common def test_biogpt_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = BioGptForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @require_torch class BioGptModelIntegrationTest(unittest.TestCase): @slow def test_inference_lm_head_model(self): model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") input_ids = torch.tensor([[2, 4805, 9, 656, 21]]) output = model(input_ids)[0] vocab_size = 42384 expected_shape = torch.Size((1, 5, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_biogpt_generation(self): tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") model.to(torch_device) torch.manual_seed(0) tokenized = tokenizer("COVID-19 is", return_tensors="pt").to(torch_device) output_ids = model.generate( **tokenized, min_length=100, max_length=1024, num_beams=5, early_stopping=True, ) output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True) EXPECTED_OUTPUT_STR = ( "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the" " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and" " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK)," " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and" " more than 800,000 deaths." ) self.assertEqual(output_str, EXPECTED_OUTPUT_STR)