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>
105 lines
5.3 KiB
Python
105 lines
5.3 KiB
Python
#
|
|
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
from vllm.model_executor.layers.fused_moe import FusedMoE
|
|
from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig, FusedMoEParallelConfig
|
|
from vllm.model_executor.layers.linear import LinearBase
|
|
|
|
from tests.ut.base import TestBase
|
|
from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
|
|
from vllm_ascend._310p.ops.linear import AscendUnquantizedLinearMethod310
|
|
from vllm_ascend._310p.quantization.modelslim_config import AscendModelSlimConfig310
|
|
|
|
|
|
class TestAscendModelSlimConfig310(TestBase):
|
|
def setUp(self):
|
|
self.sample_config = {
|
|
"weight": "INT8",
|
|
"layer1.weight": "INT8",
|
|
"layer2.weight": "FLOAT",
|
|
"fused_layer.weight": "FLOAT",
|
|
"fused_layer.shard1.weight": "FLOAT",
|
|
"fused_layer.shard2.weight": "FLOAT",
|
|
"shard1.weight": "FLOAT",
|
|
"shard2.weight": "FLOAT",
|
|
}
|
|
self.ascend_config = AscendModelSlimConfig310(self.sample_config)
|
|
self.ascend_config.packed_modules_mapping = None
|
|
|
|
def test_get_quant_method_for_linear_310(self):
|
|
mock_config = MagicMock()
|
|
mock_config.model_config.hf_config.model_type = None
|
|
linear_layer = MagicMock(spec=LinearBase)
|
|
# Test skipped layer
|
|
with (
|
|
patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
|
|
patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
|
|
):
|
|
method = self.ascend_config.get_quant_method(linear_layer, ".attn")
|
|
self.assertIsInstance(method, AscendUnquantizedLinearMethod310)
|
|
|
|
# Test quantized layer
|
|
mock_scheme = MagicMock()
|
|
with (
|
|
patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
|
|
patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
|
|
patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
|
|
patch(
|
|
"vllm_ascend._310p.quantization.modelslim_config.AscendLinearMethod", return_value=MagicMock()
|
|
) as mock_ascend_linear,
|
|
):
|
|
method = self.ascend_config.get_quant_method(linear_layer, ".attn")
|
|
self.assertIs(method, mock_ascend_linear.return_value)
|
|
mock_ascend_linear.assert_called_once_with(mock_scheme)
|
|
|
|
def test_get_quant_method_for_fused_moe_310(self):
|
|
fused_moe_layer = MagicMock(spec=FusedMoE)
|
|
fused_moe_layer.moe = MagicMock(spec=FusedMoEConfig)
|
|
fused_moe_layer.moe_config = MagicMock(spec=FusedMoEConfig)
|
|
fused_moe_layer.moe_config.moe_parallel_config = MagicMock(spec=FusedMoEParallelConfig)
|
|
fused_moe_layer.moe_config.moe_parallel_config.use_ep = True
|
|
fused_moe_layer.moe_config.moe_parallel_config.dp_size = 1
|
|
mock_config = MagicMock()
|
|
mock_config.model_config.hf_config.model_type = None
|
|
mock_config.compilation_config.custom_ops = ["all"]
|
|
mock_scheme = MagicMock()
|
|
# Test skipped layer
|
|
with (
|
|
patch("vllm.config.vllm.get_current_vllm_config", return_value=mock_config),
|
|
patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
|
|
patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
|
|
patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
|
|
):
|
|
method = self.ascend_config.get_quant_method(fused_moe_layer, ".moe")
|
|
self.assertIsInstance(method, AscendUnquantizedFusedMoEMethod310)
|
|
|
|
# Test quantized layer
|
|
mock_scheme = MagicMock()
|
|
with (
|
|
patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
|
|
patch("vllm.config.vllm.get_current_vllm_config", return_value=mock_config),
|
|
patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
|
|
patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
|
|
patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
|
|
patch(
|
|
"vllm_ascend._310p.quantization.modelslim_config.AscendFusedMoEMethod", return_value=MagicMock()
|
|
) as fused_moe_method,
|
|
):
|
|
method = self.ascend_config.get_quant_method(fused_moe_layer, ".moe")
|
|
self.assertIs(method, fused_moe_method.return_value)
|
|
fused_moe_method.assert_called_once_with(mock_scheme, fused_moe_layer.moe_config)
|