# Copyright (c) 2025 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 from unittest.mock import Mock import numpy as np import paddle from fastdeploy.config import CacheConfig, FDConfig, ModelConfig, ParallelConfig from fastdeploy.model_executor.layers.attention.attention import Attention class MockQuantMethod: """Mock quantization method for testing.""" def __init__(self, has_zero_point=False, max_bound=1.0): self.cache_quant_config = Mock() self.cache_quant_config.has_zero_point = has_zero_point self.cache_quant_config.max_bound = max_bound self.create_weights_called = False self.create_weights_args = None def create_weights(self, layer, weight_loader): self.create_weights_called = True self.create_weights_args = (layer, weight_loader) def process_loaded_weights(self, layer, state_dict): pass class TestAttentionInitWeight(unittest.TestCase): """Test cases for Attention.init_weight method.""" def setUp(self): """Set up test fixtures.""" # Create mock config self.model_config = Mock(spec=ModelConfig) self.model_config.num_attention_heads = 32 self.model_config.head_dim = 128 self.model_config.num_key_value_heads = 8 self.model_config.model = "test_model" self.model_config.num_hidden_layers = 12 self.parallel_config = Mock(spec=ParallelConfig) self.parallel_config.tensor_parallel_size = 1 self.parallel_config.tensor_parallel_rank = 0 self.parallel_config.max_num_seqs = 8 self.cache_config = Mock(spec=CacheConfig) self.fd_config = Mock(spec=FDConfig) self.fd_config.model_config = self.model_config self.fd_config.parallel_config = self.parallel_config self.fd_config.cache_config = self.cache_config self.fd_config.quant_config = None self.fd_config.moba_attention_config = None def test_init_weight_without_quantization(self): """Test init_weight without quantization.""" # Test case 1: No quantization, no qk_norm attention = Attention(fd_config=self.fd_config, layer_id=0, use_qk_norm=False) # Check that q_norm_weight and k_norm_weight are not created self.assertFalse(hasattr(attention, "q_norm_weight")) self.assertFalse(hasattr(attention, "k_norm_weight")) def test_init_weight_with_qk_norm(self): """Test init_weight with qk_norm enabled.""" # Test case 2: No quantization, with qk_norm attention = Attention(fd_config=self.fd_config, layer_id=0, use_qk_norm=True, rms_norm_eps=1e-6) # Check that q_norm_weight and k_norm_weight are created self.assertTrue(hasattr(attention, "q_norm_weight")) self.assertTrue(hasattr(attention, "k_norm_weight")) # Check parameter shapes self.assertEqual(attention.q_norm_weight.shape, [attention.qk_head_dim]) self.assertEqual(attention.k_norm_weight.shape, [attention.qk_head_dim]) # Check parameter dtype self.assertEqual(attention.q_norm_weight.dtype, paddle.float32) self.assertEqual(attention.k_norm_weight.dtype, paddle.float32) # Check initial values (should be zeros) np.testing.assert_array_equal( attention.q_norm_weight.numpy(), np.zeros(attention.qk_head_dim, dtype=np.float32) ) np.testing.assert_array_equal( attention.k_norm_weight.numpy(), np.zeros(attention.qk_head_dim, dtype=np.float32) ) def test_init_weight_with_quantization(self): """Test init_weight with quantization enabled.""" # Test case 3: With quantization mock_quant_method = MockQuantMethod() self.fd_config.quant_config = Mock() self.fd_config.quant_config.get_quant_method = Mock(return_value=mock_quant_method) attention = Attention(fd_config=self.fd_config, layer_id=0, use_qk_norm=False) # Check that quant_method.create_weights was called self.assertTrue(mock_quant_method.create_weights_called) self.assertEqual(mock_quant_method.create_weights_args[0], attention) # Check that weight_loader is passed correctly self.assertIsNotNone(mock_quant_method.create_weights_args[1]) class TestAttentionWeightLoader(unittest.TestCase): """Test cases for Attention.weight_loader method.""" def setUp(self): """Set up test fixtures.""" # Create mock config self.model_config = Mock(spec=ModelConfig) self.model_config.num_attention_heads = 32 self.model_config.head_dim = 128 self.model_config.num_key_value_heads = 8 self.model_config.model = "test_model" self.model_config.num_hidden_layers = 12 self.parallel_config = Mock(spec=ParallelConfig) self.parallel_config.tensor_parallel_size = 1 self.parallel_config.tensor_parallel_rank = 0 self.parallel_config.max_num_seqs = 8 self.cache_config = Mock(spec=CacheConfig) self.fd_config = Mock(spec=FDConfig) self.fd_config.model_config = self.model_config self.fd_config.parallel_config = self.parallel_config self.fd_config.cache_config = self.cache_config self.fd_config.moba_attention_config = None # Create mock quant method self.mock_quant_method = MockQuantMethod() self.fd_config.quant_config = Mock() self.fd_config.quant_config.get_quant_method = Mock(return_value=self.mock_quant_method) # Create attention layer self.attention = Attention(fd_config=self.fd_config, layer_id=0, use_qk_norm=False) def test_weight_loader_without_zero_point(self): """Test weight_loader without zero point.""" # Test case 1: No zero point mock_quant_method = MockQuantMethod(has_zero_point=False, max_bound=8.0) self.attention.quant_method = mock_quant_method # Create mock parameter param = paddle.zeros([10], dtype=paddle.float32) # Create mock loaded weight loaded_weight = np.array([2.0, 4.0, 8.0, 1.0, 0.5, 2.0, 4.0, 8.0, 1.0, 0.5]) # Call weight_loader self.attention.weight_loader(param, loaded_weight) # Check that the parameter is updated correctly expected_value = 8.0 / loaded_weight np.testing.assert_array_almost_equal(param.numpy(), expected_value.astype(np.float32)) def test_weight_loader_with_zero_point(self): """Test weight_loader with zero point.""" # Test case 2: With zero point mock_quant_method = MockQuantMethod(has_zero_point=True, max_bound=8.0) self.attention.quant_method = mock_quant_method # Create mock parameter param = paddle.zeros([10], dtype=paddle.float32) # Create mock loaded weight loaded_weight = np.array([2.0, 4.0, 8.0, 1.0, 0.5, 2.0, 4.0, 8.0, 1.0, 0.5]) # Call weight_loader self.attention.weight_loader(param, loaded_weight) # Check that the parameter is updated correctly expected_value = 1.0 / loaded_weight np.testing.assert_array_almost_equal(param.numpy(), expected_value.astype(np.float32)) if __name__ == "__main__": unittest.main()