# 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 GPTNeoX model.""" import unittest from transformers import AutoTokenizer, DynamicCache, GPTNeoXConfig, 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 ( GPTNeoXForCausalLM, GPTNeoXForQuestionAnswering, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification, GPTNeoXModel, ) class GPTNeoXModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=64, 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_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 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_labels = None if self.use_labels: token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) config = self.get_config() return config, input_ids, input_mask, token_labels def get_config(self): return GPTNeoXConfig( 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, pad_token_id=self.pad_token_id, ) def prepare_config_and_inputs_for_decoder(self): config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs() config.is_decoder = True return config, input_ids, input_mask, token_labels def create_and_check_model(self, config, input_ids, input_mask): model = GPTNeoXModel(config=config) model.to(torch_device) model.eval() _ = 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_model_as_decoder(self, config, input_ids, input_mask): config.add_cross_attention = True model = GPTNeoXModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) 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, input_mask, token_labels): model = GPTNeoXForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_question_answering(self, config, input_ids, input_mask, token_labels): config.num_labels = self.num_labels model = GPTNeoXForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) 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_for_sequence_classification(self, config, input_ids, input_mask, token_labels): config.num_labels = self.num_labels model = GPTNeoXForSequenceClassification(config) model.to(torch_device) model.eval() sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) result = model(input_ids, attention_mask=input_mask, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification(self, config, input_ids, input_mask, token_labels): config.num_labels = self.num_labels model = GPTNeoXForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_decoder_model_past_large_inputs(self, config, input_ids, input_mask): config.is_decoder = True model = GPTNeoXForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True) output_from_no_past = output_from_no_past["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # 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_cached_forward_with_and_without_attention_mask(self, config, input_ids, *args): # Relevant issue: https://github.com/huggingface/transformers/issues/31943 model = GPTNeoXModel(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} def copy_cache(cache: DynamicCache): """Deep copy a DynamicCache to reuse the same one multiple times.""" new_cache = cache for i in range(len(cache)): new_cache.layers[i].keys = cache.layers[i].keys.clone() new_cache.layers[i].values = cache.layers[i].values.clone() # Cached forward once with the attention mask provided and the other time without it (which should assume full attention) # We need to run both on a copy of the cache, otherwise it is modified in-place cache_outputs = model(**cache_inputs) cache = cache_outputs.past_key_values full_outputs_with_attention_mask = model( **non_cache_inputs, past_key_values=copy_cache(cache) ).last_hidden_state full_outputs_without_attention_mask = model( non_cache_inputs["input_ids"], past_key_values=copy_cache(cache) ).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, token_labels = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( GPTNeoXModel, GPTNeoXForCausalLM, GPTNeoXForQuestionAnswering, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": GPTNeoXModel, "question-answering": GPTNeoXForQuestionAnswering, "text-classification": GPTNeoXForSequenceClassification, "text-generation": GPTNeoXForCausalLM, "token-classification": GPTNeoXForTokenClassification, "zero-shot": GPTNeoXForSequenceClassification, } if is_torch_available() else {} ) test_missing_keys = False def setUp(self): self.model_tester = GPTNeoXModelTester(self) self.config_tester = ConfigTester(self, config_class=GPTNeoXConfig, hidden_size=64, num_attention_heads=8) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(config, input_ids, input_mask) def test_model_as_decoder(self): config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask) def test_model_as_decoder_with_default_input_mask(self): config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask) def test_decoder_model_past_large_inputs(self): config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(config, input_ids, input_mask) def test_model_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_model_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_model_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_model_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*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="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @require_torch class GPTNeoXLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_gptneox(self): tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped") for checkpointing in [True, False]: model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped") if checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() model.to(torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) # The hub repo. is updated on 2023-04-04, resulting in poor outputs. # See: https://github.com/huggingface/transformers/pull/24193 expected_output = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure" output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20) output_str = tokenizer.batch_decode(output_ids)[0] self.assertEqual(output_str, expected_output) @slow def test_lm_generate_flex_attn_gptneox(self): tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped") for checkpointing in [True, False]: model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-410m-deduped", attn_implementation="flex_attention" ) self.assertTrue(model.config._attn_implementation == "flex_attention") if checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() model.to(torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) # The hub repo. is updated on 2023-04-04, resulting in poor outputs. # See: https://github.com/huggingface/transformers/pull/24193 expected_output = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure" output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20) output_str = tokenizer.batch_decode(output_ids)[0] self.assertEqual(output_str, expected_output) def pythia_integration_test(self): model_name_or_path = "EleutherAI/pythia-70m" model = GPTNeoXForCausalLM.from_pretrained(model_name_or_path, dtype=torch.float16).to(torch_device) EXPECTED_LOGITS = torch.tensor([1069.0000, 228.7500, 1072.0000, 1072.0000, 1069.0000, 1068.0000, 1068.0000, 1071.0000, 1071.0000, 1071.0000, 1073.0000, 1070.0000, 1071.0000, 1075.0000, 1073.0000, 1075.0000, 1074.0000, 1069.0000, 1072.0000, 1071.0000, 1071.0000, 1071.0000, 1070.0000, 1069.0000, 1069.0000, 1069.0000, 1070.0000, 1075.0000, 1073.0000, 1074.0000]) # fmt: skip input_ids = [29, 93, 303, 64, 5478, 49651, 10394, 187, 34, 12939, 875] # alternative: tokenizer('<|im_start|>system\nA chat between') input_ids = torch.as_tensor(input_ids)[None].to(torch_device) outputs = model(input_ids)["logits"][:, -1][0, :30] torch.testing.assert_close(EXPECTED_LOGITS, outputs, rtol=1e-5, atol=1e-5)