23524f2ca4
### What this PR does / why we need it?
This pull request focuses on a significant refactoring effort within the
vllm-ascend project, specifically targeting operations optimized for the
Ascend 310P hardware. The changes aim to streamline the implementation
of core components like quantization and multi-head attention, making
the codebase more maintainable and robust. Concurrently, new unit tests
have been introduced to ensure the correctness and reliability of these
refactored modules.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
E2E test with qwen3-32b w8a8
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
91 lines
3.9 KiB
Python
91 lines
3.9 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 vllm_ascend._310p.quantization.methods.w8a8_static import AscendW8A8LinearMethod310
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class TestAscendW8A8LinearMethod310(TestBase):
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def setUp(self):
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self.method = AscendW8A8LinearMethod310()
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def test_get_weight_310(self):
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weight = self.method.get_weight(10, 20)
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self.assertEqual(weight["weight"].dtype, torch.int8)
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self.assertEqual(weight["weight"].shape, (20, 10))
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def test_get_pertensor_param_310(self):
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params = self.method.get_pertensor_param(torch.bfloat16)
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self.assertEqual(params["input_scale"].dtype, torch.bfloat16)
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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_310(self):
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params = self.method.get_perchannel_param(10, torch.bfloat16)
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self.assertEqual(params["quant_bias"].dtype, torch.int32)
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self.assertEqual(params["deq_scale"].dtype, torch.float32)
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self.assertEqual(params["weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(params["weight_offset"].dtype, torch.bfloat16)
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self.assertEqual(params["quant_bias"].shape, (10,))
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self.assertEqual(params["deq_scale"].shape, (10,))
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self.assertEqual(params["weight_scale"].shape, (10, 1))
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self.assertEqual(params["weight_offset"].shape, (10, 1))
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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_310(self, mock_npu_quant_matmul, mock_quantize):
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layer = MagicMock()
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layer.aclnn_input_scale = torch.randn(256)
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layer.aclnn_input_scale_reciprocal = 1.0 / layer.aclnn_input_scale
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layer.aclnn_input_offset = torch.randint(-128, 127, (256,), dtype=torch.int8)
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layer.weight = torch.randn(128, 256)
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layer.deq_scale = torch.randn(128)
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layer.quant_bias = torch.randint(-128, 127, (256,))
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layer.params_dtype = torch.float16
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x = torch.randn(32, 128)
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expect_x_output = torch.randint(-128, 127, x.shape, dtype=torch.int8)
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mock_quantize.return_value = expect_x_output
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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, tp_rank=0)
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mock_quantize.assert_called_with(
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x, layer.aclnn_input_scale, layer.aclnn_input_scale_reciprocal, layer.aclnn_input_offset
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)
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mock_npu_quant_matmul.assert_called_with(
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expect_x_output, layer.weight, layer.deq_scale, bias=layer.quant_bias, output_dtype=layer.params_dtype
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)
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# The bias is added by the linear layer's forward pass, not the quant method.
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self.assertTrue(torch.equal(output, expected_y_output))
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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_310(self, mock_npu_quant_matmul, mock_quantize):
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layer = MagicMock()
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layer.aclnn_input_scale = torch.randn(256)
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layer.aclnn_input_offset = torch.randint(-128, 127, (256,), dtype=torch.int8)
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layer.weight = torch.randn(128, 256)
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layer.deq_scale = torch.randn(128)
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layer.quant_bias = torch.randint(-128, 127, (256,))
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layer.params_dtype = torch.float16
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x = torch.randint(-128, 127, (32, 128), 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, tp_rank=0)
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mock_quantize.assert_not_called()
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mock_npu_quant_matmul.assert_called_with(
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x, layer.weight, layer.deq_scale, bias=layer.quant_bias, output_dtype=layer.params_dtype
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)
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# The bias is added by the linear layer's forward pass, not the quant method.
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self.assertTrue(torch.equal(output, expected_y_output))
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