8952fddc7e
### What this PR does / why we need it? This PR implements the `AscendW8A8DynamicLinearMethod310` quantization scheme specifically for 310P hardware. It includes the logic for weight retrieval, per-channel parameter generation, and the application of dynamic quantization using NPU-specific kernels. Additionally, it updates `ShardedStateLoader310` to handle quantization configurations more robustly when generating parameter type maps. Feedback from the review identified two critical issues in the implementation: 1. The tensor squeezing logic in the `apply` method incorrectly handles 2D inputs, which may lead to shape mismatches in subsequent layers. 2. The weight tensor in `process_weights_after_loading` is transposed after being converted to the private NZ format; the transpose operation should be performed on the ND tensor before conversion to ensure correct physical layout. cherry-pick from : #7546 #7725 ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? New unit tests were added in `tests/ut/_310p/quantization/test_w8a8_dynamic_310.py` to verify the quantization method, and `tests/ut/_310p/test_sharded_state_loader_310p.py` was updated to test the state loader changes. --------- Signed-off-by: csoulnd <daidaicurry@foxmail.com>
145 lines
6.2 KiB
Python
145 lines
6.2 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, Mock, 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_dynamic import (
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AscendW8A8DynamicFusedMoEMethod310,
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AscendW8A8DynamicLinearMethod310,
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)
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class TestAscendW8A8FusedMoEMethod310(TestBase):
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num_experts = 8
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hidden_size = 128
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intermediate_size = 128
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@patch("vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_ep_group")
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def setUp(self, mock_get_ep_group):
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with patch(
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"vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_current_vllm_config"
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) as mock_get_current_vllm_config:
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mock_vllm_config = Mock()
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mock_vllm_config.quant_config = Mock(quant_description={"group_size": 0})
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mock_vllm_config.scheduler_config = Mock(
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max_num_batched_tokens=2048, max_model_len=2048, enable_chunked_prefill=False
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)
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mock_get_current_vllm_config.return_value = mock_vllm_config
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mock_ep_group = Mock()
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mock_get_ep_group.return_value = mock_ep_group
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mock_ascend_config = Mock()
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mock_ascend_config.enable_chunked_prefill = False
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self.quant_method = AscendW8A8DynamicFusedMoEMethod310()
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def test_get_weight_310(self):
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param_dict = self.quant_method.get_weight(
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self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
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)
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self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
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self.assertEqual(
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param_dict["w13_weight"].shape, (self.num_experts, 2 * self.intermediate_size, self.hidden_size)
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)
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self.assertEqual(param_dict["w2_weight"].dtype, torch.int8)
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self.assertEqual(param_dict["w2_weight"].shape, (self.num_experts, self.hidden_size, self.intermediate_size))
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def test_get_dynamic_quant_param_310(self):
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param_dict = self.quant_method.get_dynamic_quant_param(
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self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
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)
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self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.float32)
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self.assertEqual(param_dict["w13_weight_scale"].shape, (self.num_experts, 2 * self.intermediate_size, 1))
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self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.float32)
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self.assertEqual(param_dict["w2_weight_scale"].shape, (self.num_experts, self.hidden_size, 1))
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class TestAscendW8A8DynamicLinearMethod310(TestBase):
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def setUp(self):
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self.method = AscendW8A8DynamicLinearMethod310()
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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_perchannel_param_310(self):
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params = self.method.get_perchannel_param(10, torch.float32)
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self.assertEqual(params["weight_scale"].dtype, torch.float32)
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self.assertEqual(params["weight_offset"].dtype, torch.float32)
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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_npu.npu_dynamic_quant")
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@patch("torch_npu.npu_quant_matmul")
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def test_apply_310(self, mock_npu_quant_matmul, mock_npu_dynamic_quantize):
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layer = MagicMock()
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layer.weight = torch.randn(128, 256, dtype=torch.float16)
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layer.weight_scale = torch.randn(128, dtype=torch.float32)
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layer.params_dtype = torch.float16
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x = torch.randn(32, 128, dtype=torch.float16)
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expect_x_output = torch.randint(-128, 127, x.shape, dtype=torch.int8)
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expect_pertoken_scale_output = torch.randn(x.shape[0], dtype=torch.float32)
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mock_npu_dynamic_quantize.return_value = expect_x_output, expect_pertoken_scale_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_npu_dynamic_quantize.assert_called_with(x)
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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.weight_scale))
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# kwargs
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self.assertTrue(torch.equal(kwargs["pertoken_scale"], expect_pertoken_scale_output))
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self.assertTrue(kwargs["bias"] is None)
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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.weight_scale = MagicMock()
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layer.weight_offset = MagicMock()
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layer.weight.data = torch.randint(-127, 128, (128, 256), 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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