from unittest.mock import MagicMock, patch import torch from tests.ut.base import TestBase from tests.ut.quantization.conftest_quantization import create_linear_layer, identity from vllm_ascend.quantization.methods.w8a8_static import AscendW8A8LinearMethod from vllm_ascend.utils import COMPRESSED_TENSORS_METHOD class TestAscendW8A8LinearMethod(TestBase): def setUp(self): self.method = AscendW8A8LinearMethod() def test_get_weight(self): sizes = [(64, 128), (256, 512), (1024, 2048), (1, 1)] for input_size, output_size in sizes: weight = self.method.get_weight(input_size, output_size) self.assertEqual(weight["weight"].dtype, torch.int8) self.assertEqual(weight["weight"].shape, (output_size, input_size)) self.assertEqual(len(weight), 1) weight = self.method.get_weight(256, 128, torch.float16) self.assertEqual(weight["weight"].dtype, torch.int8) def test_get_pertensor_param(self): dtypes = [torch.bfloat16, torch.float16, torch.float32] for dtype in dtypes: params = self.method.get_pertensor_param(dtype) self.assertEqual(params["input_scale"].dtype, dtype) self.assertEqual(params["input_offset"].dtype, torch.int8) self.assertEqual(params["input_scale"].shape, (1,)) self.assertEqual(params["input_offset"].shape, (1,)) def test_get_perchannel_param(self): for output_size, dtype in [(128, torch.bfloat16), (256, torch.float16)]: params = self.method.get_perchannel_param(output_size, dtype) self.assertEqual(params["quant_bias"].shape, (output_size,)) self.assertEqual(params["quant_bias"].dtype, torch.int32) self.assertEqual(params["weight_scale"].shape, (output_size, 1)) self.assertEqual(params["weight_scale"].dtype, dtype) self.assertEqual(params["weight_offset"].shape, (output_size, 1)) self.assertEqual(params["weight_offset"].dtype, dtype) self.assertEqual(params["deq_scale"].shape, (output_size,)) if dtype == torch.bfloat16: self.assertEqual(params["deq_scale"].dtype, torch.float32) elif dtype == torch.float16: self.assertEqual(params["deq_scale"].dtype, torch.int64) @patch("vllm_ascend.quantization.methods.w8a8_static.get_weight_prefetch_method") @patch("torch.ops.vllm.quantize") @patch("torch_npu.npu_quant_matmul") def test_apply_with_x_not_int8(self, mock_npu_quant_matmul, mock_quantize, mock_get_weight_prefetch_method): layer = MagicMock() layer.aclnn_input_scale = 0.1 layer.aclnn_input_offset = 0.2 layer.weight = torch.randn(128, 256) layer.deq_scale = 0.3 quant_bias = torch.zeros(256) layer.quant_bias = quant_bias mock_get_weight_prefetch_method.return_value = MagicMock() x = torch.randn(32, 128) bias = torch.randn(256) mock_quantize.return_value = torch.randint(-128, 127, x.shape, dtype=torch.int8) expected_y_output = torch.randn(32, 256) mock_npu_quant_matmul.return_value = expected_y_output output = self.method.apply(layer, x, bias) self.assertTrue(torch.equal(output, expected_y_output)) mock_quantize.assert_called_once() mock_npu_quant_matmul.assert_called_once() call_kwargs = mock_npu_quant_matmul.call_args.kwargs self.assertTrue(torch.equal(call_kwargs["bias"], quant_bias)) @patch("torch.ops.vllm.quantize") @patch("torch_npu.npu_quant_matmul") def test_apply_with_x_is_int8(self, mock_npu_quant_matmul, mock_quantize): layer = MagicMock() layer.aclnn_input_scale = 0.1 layer.aclnn_input_offset = 0.2 layer.weight = torch.randn(128, 256) layer.deq_scale = 0.3 layer.ascend_quant_method = COMPRESSED_TENSORS_METHOD x = torch.randint(-128, 127, (32, 128), dtype=torch.int8) bias = torch.randn(256) expected_y_output = torch.randn(32, 256) mock_npu_quant_matmul.return_value = expected_y_output output = self.method.apply(layer, x, bias) self.assertTrue(torch.equal(output, expected_y_output)) mock_quantize.assert_not_called() mock_npu_quant_matmul.assert_called_once() call_kwargs = mock_npu_quant_matmul.call_args.kwargs self.assertTrue(torch.equal(call_kwargs["bias"], bias)) @patch("vllm_ascend.utils.get_ascend_config") @patch("torch_npu.npu_format_cast") def test_process_weights_after_loading_with_nz1(self, mock_npu_format_cast, mock_get_config): mock_config = MagicMock() mock_config.weight_nz_mode = 1 mock_get_config.return_value = mock_config layer = MagicMock() layer.weight.data = torch.randint(-128, 127, (128, 256), dtype=torch.int8) layer.input_scale.data = torch.tensor([0.1]) layer.input_offset.data = torch.tensor([0]) layer.weight_scale.data = torch.randn(128, 1) layer.weight_offset.data = torch.randn(128, 1) mock_npu_format_cast.side_effect = identity self.method.process_weights_after_loading(layer) expected_offset = torch.tensor([0]).repeat(256).to(torch.int8) self.assertTrue(torch.equal(layer.aclnn_input_offset.data, expected_offset)) self.assertFalse(layer.aclnn_input_offset.requires_grad) self.assertEqual(layer.weight.data.shape, (256, 128)) self.assertEqual(layer.weight_scale.data.shape, (128,)) self.assertEqual(layer.weight_offset.data.shape, (128,)) mock_npu_format_cast.assert_called_once() self.assertTrue(isinstance(layer.deq_scale, MagicMock)) @patch("vllm_ascend.utils.get_ascend_config") @patch("torch_npu.npu_format_cast") def test_process_weights_after_loading_with_nz2_and_compressed_tensors(self, mock_npu_format_cast, mock_get_config): mock_config = MagicMock() mock_config.weight_nz_mode = 2 mock_get_config.return_value = mock_config layer = MagicMock() layer.weight.data = torch.randint(-128, 127, (128, 256), dtype=torch.int8) layer.input_scale.data = torch.tensor([0.1]) layer.input_offset.data = torch.tensor([0]) layer.weight_scale.data = torch.randn(128, 1) layer.weight_offset.data = torch.randn(128, 1) layer.ascend_quant_method = COMPRESSED_TENSORS_METHOD mock_npu_format_cast.side_effect = identity self.method.process_weights_after_loading(layer) expected_offset = torch.tensor([0]).repeat(256).to(torch.int8) self.assertTrue(torch.equal(layer.aclnn_input_offset.data, expected_offset)) self.assertFalse(layer.aclnn_input_offset.requires_grad) self.assertEqual(layer.weight.data.shape, (256, 128)) self.assertEqual(layer.weight_scale.data.shape, (128,)) self.assertEqual(layer.weight_offset.data.shape, (128,)) mock_npu_format_cast.assert_called_once() self.assertFalse(isinstance(layer.deq_scale, MagicMock)) class TestAscendW8A8LinearMethodWithNpu(TestBase): def setUp(self): self.method = AscendW8A8LinearMethod() self.mock_get_config = patch("vllm_ascend.utils.get_ascend_config") mock_config = self.mock_get_config.start() mock_ascend_config = MagicMock() mock_ascend_config.weight_nz_mode = 0 mock_config.return_value = mock_ascend_config def tearDown(self): self.mock_get_config.stop() @patch("vllm_ascend.quantization.methods.w8a8_static.get_weight_prefetch_method") def test_apply_with_npu(self, mock_get_weight_prefetch_method): mock_get_weight_prefetch_method.return_value = MagicMock() input_size, output_size = 128, 256 params_dtype = torch.bfloat16 layer = create_linear_layer(self.method, input_size, output_size, params_dtype) layer.params_dtype = params_dtype self.method.process_weights_after_loading(layer) x = torch.randn(32, input_size, dtype=params_dtype).npu() bias = torch.randn(output_size, dtype=torch.float32).npu() output = self.method.apply(layer, x, bias) self.assertEqual(output.shape, (32, output_size))