[V1 Loader] Ernie kv cache quant support v1 loader (#3899)

* support c8 for ernie

* add unittest

* support vl

* fix c8

tests/model_loader/test_load_attention.py

@@ -0,0 +1,194 @@
+# 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()