b6bc3d2f9d
NZ Format Support for Linear Layers: Implemented support for the NZ
(N-dimensional Z-order) format for linear layer weights on Ascend 310P,
enhancing performance for both quantized and unquantized layers.
Unquantized Linear Method for Ascend 310P: Introduced
AscendUnquantizedLinearMethod310 to specifically handle and apply NZ
format casting to unquantized linear layer weights during the loading
process.
MRotaryEmbedding Integration: Extended Rotary Embedding support by
adding AscendMRotaryEmbedding310 to provide an Ascend-specific
implementation for MRotaryEmbedding.
Quantization Method Updates: Updated the w8a8_static quantization method
to directly transpose weights and apply NZ format casting, ensuring
consistency with the new format.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
151 lines
6.1 KiB
Python
151 lines
6.1 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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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.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["weight_scale"].dtype, torch.float16)
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self.assertEqual(params["weight_offset"].dtype, torch.float16)
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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,
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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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)
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mock_npu_quant_matmul.assert_called_once()
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(args, kwargs) = mock_npu_quant_matmul.call_args
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# positional args
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self.assertTrue(torch.equal(args[0], expect_x_output))
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self.assertTrue(torch.equal(args[1], layer.weight.data))
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self.assertTrue(torch.equal(args[2], layer.deq_scale))
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# kwargs
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self.assertTrue(torch.equal(kwargs["bias"], layer.quant_bias))
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self.assertEqual(kwargs["output_dtype"], layer.params_dtype)
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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_once()
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(args, kwargs) = mock_npu_quant_matmul.call_args
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self.assertTrue(torch.equal(args[0], x))
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self.assertTrue(torch.equal(args[1], layer.weight.data))
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self.assertTrue(torch.equal(args[2], layer.deq_scale))
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self.assertTrue(torch.equal(kwargs["bias"], layer.quant_bias))
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self.assertEqual(kwargs["output_dtype"], layer.params_dtype)
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self.assertTrue(torch.equal(output, expected_y_output))
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@patch("torch_npu.npu_format_cast")
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def test_process_weights_after_loading_calls_nz_format_cast_310p(self, mock_npu_format_cast):
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mock_npu_format_cast.side_effect = lambda x, fmt: x
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layer = MagicMock()
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# Attributes used by process_weights_after_loading()
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layer.weight = MagicMock()
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layer.input_scale = MagicMock()
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layer.input_offset = MagicMock()
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layer.weight_scale = MagicMock()
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layer.weight_offset = MagicMock()
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layer.w2_weight_offset = MagicMock()
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layer.weight.data = torch.randint(-127, 128, (128, 256), dtype=torch.int8)
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layer.input_scale.data = torch.tensor([0.1], dtype=torch.float16)
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layer.input_offset.data = torch.tensor([0], dtype=torch.int8)
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layer.weight_scale.data = torch.randn(128, 1, dtype=torch.bfloat16)
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layer.weight_offset.data = torch.randn(128, 1, dtype=torch.bfloat16)
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# w2_weight_offset is reshaped to (N, -1); any (N, 1) is fine
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layer.w2_weight_offset.data = torch.randn(128, 1, dtype=torch.bfloat16)
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self.method.process_weights_after_loading(layer)
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mock_npu_format_cast.assert_called_once()
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