5df450bca4
### What this PR does / why we need it?
New Quantization Method: Introduced support for the W8A8SC static linear
quantization scheme specifically for 310P hardware, enabling more
efficient model compression.
Refactored the save_sharded_state_310.py to avoid multi-process issue.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
W8A8SC quant E2E test.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
123 lines
5.3 KiB
Python
123 lines
5.3 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from unittest.mock import MagicMock, patch
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import pytest
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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.w8a8sc import AscendW8A8SCLinearMethod310
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class TestAscendW8A8SCLinearMethod310(TestBase):
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def setUp(self):
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self.method = AscendW8A8SCLinearMethod310()
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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, (10 * 20, ))
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self.assertEqual(weight["index"].dtype, torch.int8)
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index_len = math.ceil(10 / 256) * math.ceil(20 / 128) * 8
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self.assertEqual(weight["index"].shape, (index_len, ))
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self.assertEqual(weight["info"].dtype, torch.int64)
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self.assertEqual(weight["info"].shape, (5, ))
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def test_get_pertensor_param_310(self):
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params = self.method.get_pertensor_param(torch.float16)
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self.assertEqual(params["input_scale"].dtype, torch.float16)
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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.float16)
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self.assertEqual(params["quant_bias"].dtype, torch.int32)
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self.assertEqual(params["deq_scale"].dtype, torch.int64)
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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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@pytest.mark.skip(
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"Skip as npu_matmul_compress_dequant will be supported in PTA 26.0.0.")
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@patch("torch.ops.vllm.quantize")
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@patch("torch_npu.npu_matmul_compress_dequant")
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def test_apply_with_x_not_int8_310(self, mock_matmul_compress_dequant,
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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,
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127, (256, ),
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dtype=torch.int8)
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layer.weight = torch.randint(-128,
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127, (256 * 128, ),
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dtype=torch.int8)
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layer.index = torch.randint(-128, 127, (8, ), dtype=torch.int8)
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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_matmul_compress_dequant.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(x, layer.aclnn_input_scale,
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layer.aclnn_input_scale_reciprocal,
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layer.aclnn_input_offset)
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mock_matmul_compress_dequant.assert_called_with(
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expect_x_output, layer.weight, layer.index, layer.quant_bias,
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layer.deq_scale)
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self.assertTrue(torch.equal(output, expected_y_output))
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@pytest.mark.skip(
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"Skip as npu_matmul_compress_dequant will be supported in PTA 26.0.0.")
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@patch("torch.ops.vllm.quantize")
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@patch("torch_npu.npu_matmul_compress_dequant")
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def test_apply_with_x_is_int8_310(self, mock_matmul_compress_dequant,
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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,
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127, (256, ),
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dtype=torch.int8)
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layer.weight = torch.randint(-128,
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127, (256 * 128, ),
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dtype=torch.int8)
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layer.index = torch.randint(-128, 127, (8, ), dtype=torch.int8)
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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_matmul_compress_dequant.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_matmul_compress_dequant.assert_called_with(
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x, layer.weight, layer.index, layer.quant_bias, layer.deq_scale)
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self.assertTrue(torch.equal(output, expected_y_output))
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