# 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 GPTNeoXJapanese model.""" import unittest from transformers import GPTNeoXJapaneseConfig, is_torch_available from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer 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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel class GPTNeoXJapaneseModelTester: 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=32, num_hidden_layers=2, num_attention_heads=4, intermediate_multiple_size=4, hidden_act="gelu", hidden_dropout=0.0, attention_dropout=0.1, weight_tying=True, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, bos_token_id=1, eos_token_id=0, 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_multiple_size = intermediate_multiple_size self.hidden_act = hidden_act self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.weight_tying = weight_tying 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.eos_token_id = eos_token_id self.bos_token_id = bos_token_id 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 GPTNeoXJapaneseConfig( 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_multiple_size=self.intermediate_multiple_size, hidden_act=self.hidden_act, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, weight_tying=self.weight_tying, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, eos_token_id=self.eos_token_id, bos_token_id=self.bos_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 = GPTNeoXJapaneseModel(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 = GPTNeoXJapaneseModel(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 = GPTNeoXJapaneseForCausalLM(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_decoder_model_past_large_inputs(self, config, input_ids, input_mask): config.is_decoder = True model = GPTNeoXJapaneseForCausalLM(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 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 GPTNeoXModelJapaneseTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM} if is_torch_available() else {} ) test_missing_keys = False def setUp(self): self.model_tester = GPTNeoXJapaneseModelTester(self) self.config_tester = ConfigTester(self, config_class=GPTNeoXJapaneseConfig, hidden_size=37) 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) @slow def test_generation(self): model_id = "abeja/gpt-neox-japanese-2.7b" prompts = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"] # fmt: skip EXPECTED_OUTPUTS = [ "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。", "100年後に必要とされる会社は、「人」が中心の会社です。", "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。", "国境の長いトンネルを抜けると、そこは雪国だった。", "美味しい日本食といえば、やっぱりお寿司ですよね。", ] tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained(model_id) model = GPTNeoXJapaneseForCausalLM.from_pretrained(model_id) predicted_outputs = [] for prompt in prompts: input_ids = tokenizer(prompt, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=50) generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) @unittest.skip("GPTNeoXJapanese applies bias to attention scores") def test_custom_4d_attention_mask(self): pass