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