# Copyright (c) 2023 PaddlePaddle Authors. 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 unittest import paddle from paddlenlp.transformers import ( GPTJConfig, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, ) from ...testing_utils import slow from ..test_generation_utils import GenerationTesterMixin from ..test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = ["EleutherAI/gpt-j-6B"] class GPTJModelTester: 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, rotary_dim=4, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, max_position_embeddings=512, 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.rotary_dim = rotary_dim 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 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 GPTJConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=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, 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, rotary_dim=self.rotary_dim, ) 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, 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, token_type_ids, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_gptj_model(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.eval() result = model(input_ids, token_type_ids=token_type_ids, use_cache=True, return_dict=True) result = model(input_ids, token_type_ids=token_type_ids, use_cache=True, return_dict=True) result = model(input_ids, use_cache=True, return_dict=True) self.parent.assertEqual( result.last_hidden_state.shape, list((self.batch_size, self.seq_length, self.hidden_size)) ) self.parent.assertEqual(len(result.past_key_values), config.n_layer) def create_and_check_gptj_model_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.eval() # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True) outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past = outputs # 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 = paddle.concat([input_ids, next_tokens], axis=-1) next_token_type_ids = paddle.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids, return_dict=True)[ "last_hidden_state" ] output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past, return_dict=True)[ "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(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=5e-1)) def create_and_check_gptj_model_attention_mask_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) model.eval() # create attention mask attn_mask = paddle.ones(input_ids.shape, dtype=paddle.int64) 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, use_cache=True) # 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 = paddle.concat([input_ids, next_tokens], axis=-1) attn_mask = paddle.concat( [attn_mask, paddle.ones((attn_mask.shape[0], 1), dtype=paddle.int64)], axis=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask, return_dict=True)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask, return_dict=True)[ "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(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_gptj_model_past_large_inputs(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTJModel(config=config) 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 # 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 = paddle.concat([input_ids, next_tokens], axis=-1) next_token_type_ids = paddle.concat([token_type_ids, next_token_types], axis=-1) next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1) output_from_no_past = model( next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask, return_dict=True )["last_hidden_state"] output_from_past = model( next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past, return_dict=True, )["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(paddle.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 = GPTJForCausalLM(config) model.eval() result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids, return_dict=True) self.parent.assertIsInstance(result.loss.item(), float) self.parent.assertEqual(result.logits.shape, list((self.batch_size, self.seq_length, self.vocab_size))) def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, token_type_ids, *args, gradient_checkpointing=False ): model = GPTJForCausalLM(config) 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 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.astype("int64"), "token_type_ids": token_type_ids, } return config, inputs_dict class GPTJModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): base_model_class = GPTJModel all_model_classes = (GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification, GPTJForQuestionAnswering) all_generative_model_classes = {GPTJForCausalLM: (GPTJModel, "unimo")} fx_compatible = True test_pruning = False test_missing_keys = False test_model_parallel = False test_head_masking = False use_test_model_name_list = False # special case for DoubleHeads model def _prepare_for_class(self, inputs_dict, model_class): inputs_dict = super()._prepare_for_class(inputs_dict, model_class) return inputs_dict def setUp(self): self.model_tester = GPTJModelTester(self) def test_gptj_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model(*config_and_inputs) def test_gptj_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model_past(*config_and_inputs) def test_gptj_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs) def test_gptj_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs) def test_gptj_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) @slow def test_model_from_pretrained(self): for model_name in GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = GPTJModel.from_pretrained(model_name) self.assertIsNotNone(model)