# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # Copyright 2020 The HuggingFace 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 unittest import paddle from paddlenlp.transformers import Qwen2MoeConfig, Qwen2MoeForCausalLM, Qwen2MoeModel 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 Qwen2MoeModelTester: def __init__( self, parent, vocab_size=32000, hidden_size=64, num_hidden_layers=2, num_attention_heads=8, num_key_value_heads=8, masked_softmax_fusion=True, layer_norm_epsilon=1e-5, initializer_range=0.02, is_training=True, use_cache=False, bos_token_id=1, eos_token_id=2, apply_residual_connection_post_layernorm=False, hidden_dropout=0.0, attention_dropout=0.0, attention_softmax_in_fp32=True, pretraining_tp=1, # TP rank used when training with megatron dtype="bfloat16", slow_but_exact=False, batch_size: int = 2, seq_length: int = 10, type_sequence_label_size=2, activation_function="gelu", num_labels=3, num_choices=4, scope=None, dropout=0.56, use_input_mask: bool = False, use_labels: bool = False, return_dict=False, ): self.parent: Qwen2MoeModelTest = parent 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.num_key_value_heads = num_key_value_heads self.masked_softmax_fusion = masked_softmax_fusion self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.is_training = is_training self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.attention_softmax_in_fp32 = attention_softmax_in_fp32 self.pretraining_tp = pretraining_tp self.dtype = dtype self.slow_but_exact = slow_but_exact self.batch_size = batch_size self.seq_length = seq_length self.type_sequence_label_size = type_sequence_label_size self.activation_function = activation_function self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.dropout = dropout self.use_input_mask = use_input_mask self.use_labels = use_labels self.return_dict = return_dict 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]) 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, sequence_labels, token_labels, choice_labels def get_config(self) -> Qwen2MoeConfig: return Qwen2MoeConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, masked_softmax_fusion=self.masked_softmax_fusion, layer_norm_epsilon=self.layer_norm_epsilon, initializer_range=self.initializer_range, use_cache=self.use_cache, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, apply_residual_connection_post_layernorm=self.apply_residual_connection_post_layernorm, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, attention_softmax_in_fp32=self.attention_softmax_in_fp32, pretraining_tp=self.pretraining_tp, dtype=self.dtype, slow_but_exact=self.slow_but_exact, activation_function=self.activation_function, ) def create_and_check_model( self, config: Qwen2MoeConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Qwen2MoeModel(config) model.eval() result = model(input_ids) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) def create_and_check_model_attention_mask( self, config: Qwen2MoeConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Qwen2MoeModel(config) model.eval() attn_mask_2d = random_attention_mask([self.batch_size, self.seq_length]) result_2d = model(input_ids, attention_mask=attn_mask_2d)[0] batch, seq_length = input_ids.shape causal_mask = paddle.tril(paddle.ones((batch, seq_length, seq_length), dtype=attn_mask_2d.dtype)) attn_mask_3d = causal_mask & attn_mask_2d.unsqueeze(-1) result_3d = model(input_ids, attention_mask=attn_mask_3d)[0] attn_mask_4d = attn_mask_3d.unsqueeze(1) result_4d = model(input_ids, attention_mask=attn_mask_4d)[0] result_no_attention_mask = model(input_ids, attention_mask=None)[0] # Assert non-padding tokens have the same logits with different attention_mask shape self.parent.assertTrue((result_2d[attn_mask_2d] == result_3d[attn_mask_2d]).all()) self.parent.assertTrue((result_2d[attn_mask_2d] == result_4d[attn_mask_2d]).all()) self.parent.assertTrue((result_2d[attn_mask_2d] == result_no_attention_mask[attn_mask_2d]).all()) def create_and_check_model_past_large_inputs( self, config: Qwen2MoeConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = Qwen2MoeModel(config) model.eval() # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.return_dict) past_key_values = outputs.past_key_values if self.return_dict else outputs[2] # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), self.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], axis=-1) next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1) outputs = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, return_dict=self.return_dict ) output_from_no_past = outputs[2][0] outputs = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, return_dict=self.return_dict, ) output_from_past = outputs[2][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)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_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 def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args): model = Qwen2MoeForCausalLM(config) model.eval() result = model( input_ids, use_cache=True, labels=input_ids if self.parent.use_labels else None, return_dict=self.parent.return_dict, ) if self.parent.use_labels: self.parent.assertIsInstance(result[0].item(), float) self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size]) else: self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size]) def check_model_position_ids(self, config, input_ids, input_mask, *args): model = Qwen2MoeForCausalLM(config) model.eval() result_no_position_id = model( input_ids, labels=input_ids if self.parent.use_labels else None, return_dict=self.parent.return_dict, ) batch_size, seq_len = input_ids.shape position_ids = paddle.arange(seq_len).expand((batch_size, seq_len)) result_position_id = model( input_ids, position_ids, labels=input_ids if self.parent.use_labels else None, return_dict=self.parent.return_dict, ) if self.parent.use_labels: self.parent.assertTrue((result_position_id[1] == result_no_position_id[1]).all()) else: self.parent.assertTrue((result_position_id[0] == result_no_position_id[0]).all()) class Qwen2MoeModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): base_model_class = Qwen2MoeModel return_dict = False use_labels = False use_test_model_name_list = False all_model_classes = (Qwen2MoeModel, Qwen2MoeForCausalLM) all_generative_model_classes = {Qwen2MoeForCausalLM: (Qwen2MoeModel, "qwen2_moe")} def setUp(self): super().setUp() self.model_tester = Qwen2MoeModelTester(self) self.config_tester = ConfigTester(self, config_class=Qwen2MoeConfig, vocab_size=256, hidden_size=24) def _get_input_ids_and_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict[self.input_name] attention_mask = paddle.ones_like(input_ids, dtype=paddle.int64) max_batch_size = 2 sequence_length = input_ids.shape[-1] // 2 input_ids = input_ids[:max_batch_size, :sequence_length] attention_mask = attention_mask[:max_batch_size, :sequence_length] max_length = 3 return config, input_ids, attention_mask, max_length 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_attention_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_attention_mask(*config_and_inputs) def test_model_position_ids(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_model_position_ids(*config_and_inputs) def test_generate_without_input_ids(self): # this requires 4-D attention mask logic, which is not supported yet pass def test_qwen2moe_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)