[GCU] Support gcu platform (#2702)

baseline: e7fa57ebae

Co-authored-by: yongqiangma <xing.wo@163.com>

fastdeploy/model_executor/layers/backends/gcu/quantization/__init__.py

@@ -0,0 +1,21 @@
+# 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.
+""""
+gcu quantization
+"""
+from .weight_only import GCUWeightOnlyLinearMethod
+
+__all__ = [
+    "GCUWeightOnlyLinearMethod",
+]

fastdeploy/model_executor/layers/backends/gcu/quantization/weight_only.py

@@ -0,0 +1,90 @@
+"""
+# 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.
+"""
+
+import paddle
+
+from fastdeploy.model_executor.layers.quantization.weight_only import (
+    WeightOnlyConfig, WeightOnlyLinearMethod)
+from fastdeploy.model_executor.layers.utils import get_tensor
+from fastdeploy.model_executor.ops.gcu import linear_quant, weight_quantize_rtn
+
+
+class GCUWeightOnlyLinearMethod(WeightOnlyLinearMethod):
+    """
+    Weight only quantization method for linear layer on GCU
+    """
+
+    def __init__(
+        self,
+        quant_config: WeightOnlyConfig,
+    ) -> None:
+        super().__init__(quant_config)
+        self.quant_config = quant_config
+        self.group_size = -1
+
+
+    def create_weights(self, layer):
+        # The scale shape should be equal to the output dim of weight using Per-Channel Quantization.
+        linear_weight_scale_shape = [layer.linear_weight_shape[1]]
+
+        layer.linear_weight_shape.reverse()
+        if self.quant_config.name() == "wint4":
+            layer.linear_weight_shape[0] //= 2
+        layer.weight_dtype = "int8"
+        layer.linear_weight_scale = layer.create_parameter(
+            shape=linear_weight_scale_shape,
+            dtype=layer._dtype,
+            is_bias=False,
+        )
+
+
+    def process_prequanted_weights(self, layer, state_dict) -> None:
+        """
+        Process pre-quantized weights before applying them to the model
+        Args:
+            layer: The layer that owns the weights
+            quant_weight: The quantized weights
+            weight_scale: The scale of the quantized weights
+        """
+        quant_weight = get_tensor(state_dict.pop(layer.weight_key))
+        weight_scale = get_tensor(state_dict.pop(layer.weight_scale_key))
+        layer.linear_weight.set_value(quant_weight)
+        layer.linear_weight_scale.set_value(
+            weight_scale.astype(paddle.get_default_dtype()))
+
+
+    def process_loaded_weights(self, layer, weight) -> None:
+        quanted_weight_tensor, weight_scale_tensor = weight_quantize_rtn(
+            weight,
+            self.quant_config.algo,
+            self.group_size,  # group_size
+        )
+
+        layer.linear_weight.set_value(quanted_weight_tensor)
+        layer.linear_weight_scale.set_value(
+            weight_scale_tensor.astype(paddle.get_default_dtype()))
+
+
+    @paddle.no_grad()
+    def apply(self, layer, x):
+        linear_out = linear_quant(
+            lhs=x,
+            rhs=layer.linear_weight,
+            scale=layer.linear_weight_scale,
+            bias=None,
+            group_size=self.group_size,
+        )
+        return linear_out