152 lines
6.0 KiB
Python
152 lines
6.0 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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# This file is a part of the vllm-ascend project.
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#
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from __future__ import annotations
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from typing import Any
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import torch
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from vllm.config import get_current_vllm_config
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from vllm.logger import logger
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from vllm.model_executor.layers.linear import LinearBase
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from vllm.model_executor.layers.quantization import register_quantization_config
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from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding,
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)
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from vllm_ascend._310p.quantization.methods.registry import (
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get_scheme_class,
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)
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from vllm_ascend.quantization.method_adapters import AscendFusedMoEMethod, AscendLinearMethod
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from vllm_ascend.quantization.modelslim_config import (
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AscendModelSlimConfig,
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get_quant_type_for_layer,
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packed_modules_model_mapping,
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)
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from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD, vllm_version_is
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if vllm_version_is("0.23.0"):
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from vllm.model_executor.layers.fused_moe import FusedMoE
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else:
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from vllm.model_executor.layers.fused_moe import MoERunner, RoutedExperts
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def _is_fused_moe_layer(layer: torch.nn.Module) -> bool:
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if vllm_version_is("0.23.0"):
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return isinstance(layer, FusedMoE)
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else:
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return isinstance(layer, (MoERunner, RoutedExperts))
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def create_scheme_for_layer(
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quant_description: dict[str, Any],
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prefix: str,
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layer_type: str,
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packed_modules_mapping: dict[str, Any] | None = None,
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):
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"""Create a quantization scheme instance for a layer.
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Args:
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quant_description: The quantization description dictionary.
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prefix: The layer prefix.
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layer_type: The type of layer ("linear", "moe", "attention").
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packed_modules_mapping: Mapping for packed/fused modules.
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Returns:
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An instance of the appropriate quantization scheme class.
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"""
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logger.info_once("Using vLLM Ascend ModelSlim quantization.")
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quant_type = get_quant_type_for_layer(quant_description, prefix, layer_type, packed_modules_mapping)
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if quant_type is None:
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err_msg = f"Could not determine quantization type for layer {prefix} (layer_type={layer_type})."
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logger.error(err_msg)
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raise ValueError(err_msg)
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# Use registry to get scheme class
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scheme_cls = get_scheme_class(quant_type, layer_type)
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if scheme_cls is not None:
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return scheme_cls()
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err_msg = f"Unsupported quant_type={quant_type} for layer_type={layer_type}."
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logger.error(err_msg)
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raise NotImplementedError(err_msg)
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@register_quantization_config(ASCEND_QUANTIZATION_METHOD)
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class AscendModelSlimConfig310(AscendModelSlimConfig):
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"""310P override for ModelSlim quantization config.
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- Uses 310P-local scheme registry to create scheme by (quant_type, layer_type).
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- MUST keep packed_modules_mapping behavior consistent with base, otherwise
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fused modules (qkv_proj / gate_up_proj) will miss and fallback to base,
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causing NZ/transpose issues on 310P.
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"""
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def get_quant_method(
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self,
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layer: torch.nn.Module,
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prefix: str,
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tid2eid: Any = None,
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) -> QuantizeMethodBase | None:
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vllm_config = get_current_vllm_config()
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model_type = vllm_config.model_config.hf_config.model_type
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if model_type in packed_modules_model_mapping:
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self.packed_modules_mapping = packed_modules_model_mapping[model_type]
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prefix = self.quant_prefix_mapper(model_type, prefix)
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if isinstance(layer, LinearBase):
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packed = getattr(self, "packed_modules_mapping", {})
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if self.is_layer_skipped_ascend(prefix, packed):
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from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
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logger.debug("Select AscendUnquantizedLinearMethod for %s (layer=%s)", prefix, "LinearBase")
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return AscendUnquantizedLinearMethod()
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scheme = create_scheme_for_layer(
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quant_description=self.quant_description,
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prefix=prefix,
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layer_type="linear",
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packed_modules_mapping=packed,
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)
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logger.debug("Select AscendLinearMethod for %s (layer=%s)", prefix, "LinearBase")
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return AscendLinearMethod(scheme)
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elif _is_fused_moe_layer(layer):
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if self.is_layer_skipped_ascend(prefix, self.packed_modules_mapping):
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from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
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logger.debug("Select AscendUnquantizedFusedMoEMethod310 for %s (layer=%s)", prefix, "FusedMoE")
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return AscendUnquantizedFusedMoEMethod310(layer.moe_config)
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scheme = create_scheme_for_layer(self.quant_description, prefix, "moe", self.packed_modules_mapping)
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logger.debug("Select AscendFusedMoEMethod for %s (layer=%s)", prefix, "FusedMoE")
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return AscendFusedMoEMethod(scheme, layer.moe_config)
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elif isinstance(layer, VocabParallelEmbedding):
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from vllm_ascend._310p.ops.vocab_parallel_embedding import AscendUnquantizedEmbeddingMethod310
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logger.debug(
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"Select AscendUnquantizedEmbeddingMethod310 for %s (layer=%s)", prefix, "VocabParallelEmbedding"
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)
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return AscendUnquantizedEmbeddingMethod310()
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logger.debug("No quant method matched for %s, falling back to base", prefix)
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return super().get_quant_method(layer, prefix)
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