[LLM] First commit the llm deployment code

fastdeploy/model_executor/layers/quantization/w8a8.py

@@ -0,0 +1,109 @@
+"""
+# 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
+
+import paddle
+from paddlenlp.utils.log import logger
+
+import fastdeploy
+from fastdeploy.platforms.utils import convert_to_npu_dequant_scale
+
+from .quant_base import QuantConfigBase, QuantMethodBase
+
+
+class W8A8Config(QuantConfigBase):
+    """
+    quantization config for weight 8bits and activation 8bits
+    """
+
+    def __init__(self, weight_scale_dict, act_scale_dict,
+                 use_gemm_dequant) -> None:
+        super().__init__()
+        self.weight_scale_dict = weight_scale_dict
+        self.act_scale_dict = act_scale_dict
+        self.use_gemm_dequant = use_gemm_dequant
+
+    def get_name(self) -> str:
+        return "w8a8"
+
+    @classmethod
+    def from_config(cls, config: dict) -> "W8A8Config":
+        weight_scale_dict = config["weight_scale_dict"]
+        act_scale_dict = config["act_scale_dict"]
+        use_gemm_dequant = config["use_gemm_dequant"]
+        return cls(weight_scale_dict, act_scale_dict, use_gemm_dequant)
+
+    def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
+        return W8A8LinearMethod(self)
+
+
+class W8A8LinearMethod(QuantMethodBase):
+    """
+    quantization method for weight 8bits and activation 8bits of linear layer
+    """
+
+    def __init__(
+        self,
+        quant_config: W8A8Config,
+    ) -> None:
+        super().__init__()
+        self.quant_config = quant_config
+
+    def create_weights(self, layer):
+        weight_scale = self.quant_config.weight_scale_dict.get(
+            layer.prefix + ".weight_quanter")
+        in_scale = self.quant_config.act_scale_dict.get(layer.prefix +
+                                                        ".activation_quanter")
+        self.skip_quant = False
+        if weight_scale is None or in_scale is None:
+            self.skip_quant = True
+            return
+
+        max_range = 127.0
+        linear_out_scale = paddle.to_tensor(
+            weight_scale /
+            (max_range * max_range * in_scale)).astype("float32")
+        layer.linear_out_scale = layer.create_parameter(
+            shape=[layer.embed_dim],
+            dtype="float32",
+            is_bias=False,
+            default_initializer=paddle.nn.initializer.Constant(0),
+        )
+        layer.linear_out_scale.set_value(
+            convert_to_npu_dequant_scale(linear_out_scale))
+
+    def process_loaded_weights(self, layer, weights) -> None:
+        if self.skip_quant:
+            logger.debug(f"{layer.prefix} skip quant")
+            weight_tensor = weights.cast(layer._dtype)
+            layer.linear_weight.set_value(weight_tensor)
+        else:
+            weight_tensor = weights.transpose([1, 0])
+            weight_tensor = paddle.cast(weight_tensor, layer.weight_dtype)
+            layer.linear_weight.set_value(weight_tensor)
+
+    def apply(self, layer, x):
+        if self.skip_quant:
+            linear_out = paddle.matmul(x, layer.linear_weight, False, True)
+            return linear_out
+        if self.quant_config.use_gemm_dequant:
+            linear_out = fastdeploy.model_executor.ops.gpu.gemm_dequant(
+                x, layer.linear_weight, layer.linear_out_scale, layer._dtype)
+        else:
+            linear_out = paddle.matmul(x, layer.linear_weight, False, True)
+            linear_out = fastdeploy.model_executor.ops.gpu.dequant_int8(
+                linear_out, layer.linear_out_scale, layer._dtype)
+        return linear_out