Sync v2.0 version of code to github repo

fastdeploy/model_executor/layers/quantization/mix_quant.py

@@ -0,0 +1,75 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. 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 typing import Optional
+
+from ..attention import Attention
+from ..moe import FusedMoE
+from . import get_quantization_config
+from .quant_base import QuantConfigBase, QuantMethodBase
+
+
+class MixQuantConfig(QuantConfigBase):
+    """
+    Quantization config for layers that has different quantization methods.
+    """
+
+    def __init__(
+        self,
+        dense_quant_type: str,
+        moe_quant_type: str,
+        kv_cache_quant_type: str = None,
+        image_moe_quant_type: str = None,
+    ) -> None:
+        super().__init__()
+        self.dense_quant_type = dense_quant_type
+        self.moe_quant_type = moe_quant_type
+        self.kv_cache_quant_type = kv_cache_quant_type
+        if image_moe_quant_type is None:
+            self.image_moe_quant_type = moe_quant_type
+        else:
+            self.image_moe_quant_type = image_moe_quant_type
+        self.quant_max_bound = 0
+        self.quant_min_bound = 0
+        self.quant_round_type = 0
+
+    def name(self) -> str:
+        return "mix_quant"
+
+    @classmethod
+    def from_config(cls, config: dict) -> "MixQuantConfig":
+        return cls(config['dense_quant_type'], config['moe_quant_type'],
+                   config.get('kv_cache_quant_type', None),
+                   config.get('image_moe_quant_type', None))
+
+    def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
+        if isinstance(layer, FusedMoE):
+            if layer.moe_tag == "Image":
+                return get_quantization_config(
+                    self.image_moe_quant_type).from_config(
+                        {}).get_quant_method(layer)
+            else:
+                return get_quantization_config(
+                    self.moe_quant_type).from_config(
+                        {}).get_quant_method(layer)
+        elif isinstance(layer, Attention):
+            if self.kv_cache_quant_type is not None:
+                return (get_quantization_config("kvcache").from_config(
+                    self.kv_cache_quant_type).get_quant_method(layer))
+            else:
+                return None
+        else:
+            return get_quantization_config(self.dense_quant_type).from_config(
+                {}).get_quant_method(layer)