# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # Copyright 2024 The Qwen team, Alibaba Group and 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. from __future__ import annotations import gc import unittest import paddle from paddlenlp.transformers import ( Qwen2Config, Qwen2ForCausalLM, Qwen2ForSequenceClassification, Qwen2ForTokenClassification, Qwen2Model, ) from tests.transformers.test_configuration_common import ConfigTester from tests.transformers.test_generation_utils import GenerationTesterMixin from tests.transformers.test_modeling_common import ( ModelTesterMixin, ids_tensor, random_attention_mask, ) class Qwen2ModelTester: 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=5, max_window_layers=3, use_sliding_window=True, sliding_window=2, num_attention_heads=4, num_key_value_heads=2, 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, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent: Qwen2ModelTest = 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.max_window_layers = max_window_layers self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_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.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.scope = scope # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype=paddle.int64) 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) -> Qwen2Config: return Qwen2Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, max_window_layers=self.max_window_layers, use_sliding_window=self.use_sliding_window, sliding_window=self.sliding_window, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Qwen2 def create_and_check_model( self, config: Qwen2Config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Qwen2Model(config=config) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Qwen2 def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = Qwen2Model(config) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Qwen2 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 = Qwen2ForCausalLM(config=config) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels, return_dict=True) self.parent.assertEqual(result.logits.shape, [self.batch_size, self.seq_length, self.vocab_size]) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Qwen2 def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = Qwen2ForCausalLM(config=config) model.eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_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 = paddle.concat([input_ids, next_tokens], dim=-1) next_attention_mask = paddle.concat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_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(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common 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 class Qwen2ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): base_model_class = Qwen2Model return_dict = False use_labels = False use_test_model_name_list = False all_model_classes = (Qwen2Model, Qwen2ForCausalLM) all_generative_model_classes = {Qwen2ForCausalLM: {Qwen2Model, "qwen2"}} pipeline_model_mapping = { "feature-extraction": Qwen2Model, "text-classification": Qwen2ForSequenceClassification, "token-classification": Qwen2ForTokenClassification, "text-generation": Qwen2ForCausalLM, "zero-shot": Qwen2ForSequenceClassification, } def setUp(self): super().setUp() self.model_tester = Qwen2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Qwen2Config, hidden_size=37) 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_Qwen2_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() print(config) config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = Qwen2ForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True) self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels]) def test_Qwen2_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = Qwen2ForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True) self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels]) def test_Qwen2_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 = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(paddle.float32) model = Qwen2ForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels, return_dict=True) self.assertEqual(result.logits.shape, [self.model_tester.batch_size, self.model_tester.num_labels]) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_token_classification_model with Llama->Qwen2,llama->Qwen2 def test_Qwen2_token_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 = paddle.not_equal(input_ids, paddle.ones_like(input_ids)) token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels) model = Qwen2ForTokenClassification(config=config) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=token_labels, return_dict=True) self.assertEqual( result.logits.shape, [self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels], ) @unittest.skip("Qwen2 buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass @unittest.skip("Qwen2 uses GQA on all models so the KV cache is a non standard format") def test_past_key_values_format(self): pass class Qwen2IntegrationTest(unittest.TestCase): def test_model_tiny_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = Qwen2ForCausalLM.from_pretrained("__internal_testing__/tiny-random-qwen2", dtype="float32") input_ids = paddle.to_tensor([input_ids]) with paddle.no_grad(): out = model(input_ids, return_dict=True).logits # Expected mean on dim = -1 EXPECTED_MEAN = paddle.to_tensor( [[0.00008947, -0.00001425, 0.00035553, -0.00003941, 0.00068506, 0.00005345, 0.00060015, 0.00081522]] ) paddle.allclose(out.mean(-1), EXPECTED_MEAN, atol=1e-6, rtol=1e-6) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = paddle.to_tensor([0.26874602, 0.51205510, -0.00591420, 0.05831886, 0.18694536, 0.04331543, 0.09623559, -0.10191102, 0.07565773, 0.13765232, 0.03041580, 0.42183253, 0.40434697, 0.06868516, 0.02637704, -0.13485563, -0.01698003, 0.21499887, -0.03826120, 0.16291623, -0.27641180, -0.36975217, 0.34660554, -0.52724630, -0.41814676, 0.00843160, -0.29562786, -0.07467390, 0.40502766, 0.13571614]) # fmt: skip print(out[0, 0, :30]) paddle.allclose(out[0, 0, :30], EXPECTED_SLICE, atol=1e-6, rtol=1e-6) del model paddle.device.cuda.empty_cache() gc.collect()