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* support fa3 backend run in pd disaggregated * support fa3 backend run in pd disaggregated * support fa3 backend run in pd disaggregated * support fa3 backend run in pd disaggregated * delete use_fast_ffn
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. 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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"""
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from typing import Optional
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.moe.moe import FusedMoE
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from . import get_quantization_config
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from .quant_base import QuantConfigBase, QuantMethodBase
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class MixQuantConfig(QuantConfigBase):
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"""
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Quantization config for layers that has different quantization methods.
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"""
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def __init__(
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self,
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dense_quant_type: str,
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moe_quant_type: str,
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kv_cache_quant_type: str = None,
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image_moe_quant_type: str = None,
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) -> None:
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super().__init__()
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self.dense_quant_type = dense_quant_type
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self.moe_quant_type = moe_quant_type
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self.kv_cache_quant_type = kv_cache_quant_type
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if image_moe_quant_type is None:
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self.image_moe_quant_type = moe_quant_type
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else:
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self.image_moe_quant_type = image_moe_quant_type
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self.quant_max_bound = 0
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self.quant_min_bound = 0
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self.quant_round_type = 0
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def name(self) -> str:
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return "mix_quant"
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@classmethod
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def from_config(cls, config: dict) -> "MixQuantConfig":
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return cls(config['dense_quant_type'], config['moe_quant_type'],
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config.get('kv_cache_quant_type', None),
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config.get('image_moe_quant_type', None))
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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if isinstance(layer, FusedMoE):
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if layer.moe_tag == "Image":
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return get_quantization_config(
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self.image_moe_quant_type).from_config(
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{}).get_quant_method(layer)
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else:
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return get_quantization_config(
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self.moe_quant_type).from_config(
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{}).get_quant_method(layer)
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elif isinstance(layer, Attention):
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if self.kv_cache_quant_type is not None:
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return (get_quantization_config("kvcache").from_config(
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self.kv_cache_quant_type).get_quant_method(layer))
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else:
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return None
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else:
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return get_quantization_config(self.dense_quant_type).from_config(
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{}).get_quant_method(layer)
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