2064afe380
### What this PR does / why we need it?
- This PR fixes an issue with weight format conversion for unquantized
models running on Ascend 310P devices.
- The changes refactor the logic for converting weights to the
FRACTAL_NZ format. Previously, this was handled in a 310P-specific
linear layer implementation (`AscendUnquantizedLinearMethod310`). This
implementation has been removed, and the logic is now centralized in the
`maybe_trans_nz` utility function. This function now checks if the
device is a 310P and applies the NZ format cast accordingly for
`float16`/`bfloat16` weights.
- This refactoring simplifies the code by removing platform-specific
duplication and ensures correct weight handling for unquantized models
on 310P.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut and local test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
105 lines
5.3 KiB
Python
105 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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from unittest.mock import MagicMock, patch
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig, FusedMoEParallelConfig
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from vllm.model_executor.layers.linear import LinearBase
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from tests.ut.base import TestBase
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from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
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from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
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from vllm_ascend._310p.quantization.modelslim_config import AscendModelSlimConfig310
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class TestAscendModelSlimConfig310(TestBase):
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def setUp(self):
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self.sample_config = {
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"weight": "INT8",
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"layer1.weight": "INT8",
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"layer2.weight": "FLOAT",
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"fused_layer.weight": "FLOAT",
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"fused_layer.shard1.weight": "FLOAT",
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"fused_layer.shard2.weight": "FLOAT",
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"shard1.weight": "FLOAT",
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"shard2.weight": "FLOAT",
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}
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self.ascend_config = AscendModelSlimConfig310(self.sample_config)
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self.ascend_config.packed_modules_mapping = None
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def test_get_quant_method_for_linear_310(self):
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mock_config = MagicMock()
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mock_config.model_config.hf_config.model_type = None
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linear_layer = MagicMock(spec=LinearBase)
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# Test skipped layer
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with (
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patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
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patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
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):
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method = self.ascend_config.get_quant_method(linear_layer, ".attn")
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self.assertIsInstance(method, AscendUnquantizedLinearMethod)
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# Test quantized layer
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mock_scheme = MagicMock()
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with (
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patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
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patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
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patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
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patch(
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"vllm_ascend._310p.quantization.modelslim_config.AscendLinearMethod", return_value=MagicMock()
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) as mock_ascend_linear,
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):
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method = self.ascend_config.get_quant_method(linear_layer, ".attn")
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self.assertIs(method, mock_ascend_linear.return_value)
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mock_ascend_linear.assert_called_once_with(mock_scheme)
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def test_get_quant_method_for_fused_moe_310(self):
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fused_moe_layer = MagicMock(spec=FusedMoE)
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fused_moe_layer.moe = MagicMock(spec=FusedMoEConfig)
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fused_moe_layer.moe_config = MagicMock(spec=FusedMoEConfig)
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fused_moe_layer.moe_config.moe_parallel_config = MagicMock(spec=FusedMoEParallelConfig)
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fused_moe_layer.moe_config.moe_parallel_config.use_ep = True
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fused_moe_layer.moe_config.moe_parallel_config.dp_size = 1
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mock_config = MagicMock()
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mock_config.model_config.hf_config.model_type = None
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mock_config.compilation_config.custom_ops = ["all"]
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mock_scheme = MagicMock()
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# Test skipped layer
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with (
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patch("vllm.config.vllm.get_current_vllm_config", return_value=mock_config),
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patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
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patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
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patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
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):
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method = self.ascend_config.get_quant_method(fused_moe_layer, ".moe")
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self.assertIsInstance(method, AscendUnquantizedFusedMoEMethod310)
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# Test quantized layer
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mock_scheme = MagicMock()
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with (
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patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
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patch("vllm.config.vllm.get_current_vllm_config", return_value=mock_config),
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patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
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patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
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patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
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patch(
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"vllm_ascend._310p.quantization.modelslim_config.AscendFusedMoEMethod", return_value=MagicMock()
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) as fused_moe_method,
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):
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method = self.ascend_config.get_quant_method(fused_moe_layer, ".moe")
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self.assertIs(method, fused_moe_method.return_value)
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fused_moe_method.assert_called_once_with(mock_scheme, fused_moe_layer.moe_config)
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