Merge branch 'PaddlePaddle:develop' into pr0819

Author: Kane2011

fastdeploy/model_executor/layers/moe/check_backend_supported.py

@@ -18,13 +18,26 @@
 from fastdeploy.model_executor.layers.moe.fused_moe_cutlass_backend import (
     CutlassMoEMethod,
 )
+from fastdeploy.model_executor.layers.moe.fused_moe_deepgemm_backend import (
+    DeepGemmFusedMoeMethod,
+)
+from fastdeploy.model_executor.layers.moe.fused_moe_marlin_backend import (
+    MarlinWeightOnlyMoEMethod,
+)
 from fastdeploy.model_executor.layers.moe.fused_moe_triton_backend import (
     BlockWiseFP8MoEMethod,
     TensorWiseFP8MoEMethod,
     TritonWeightOnlyMoEMethod,
 )
 
-pre_create_weights_list = (CutlassMoEMethod, TensorWiseFP8MoEMethod, BlockWiseFP8MoEMethod, TritonWeightOnlyMoEMethod)
+pre_create_weights_list = (
+    CutlassMoEMethod,
+    TensorWiseFP8MoEMethod,
+    BlockWiseFP8MoEMethod,
+    TritonWeightOnlyMoEMethod,
+    DeepGemmFusedMoeMethod,
+    MarlinWeightOnlyMoEMethod,
+)
 
 
 def is_supported_moe_backend(quant_method: MoEMethodBase):

fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py

@@ -23,7 +23,6 @@
 from fastdeploy.model_executor.layers.utils import get_tensor
 from fastdeploy.model_executor.ops.gpu import count_tokens_per_expert_func, deep_gemm
 
-from ..utils import create_and_set_parameter
 from .fused_moe_backend_base import MoEMethodBase
 
 
@@ -32,11 +31,73 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
     DeepGemmFusedMoeMethod is a class that implements the MoEMethodBase interface for DeepGemm backend.
     """
 
-    def create_weights(self, layer: nn.Layer, state_dict):
+    def create_weights(self, layer: nn.Layer, **extra_weight_attrs):
         """
         deepgemm create weight process.
         """
+        self.weight_dtype = paddle.float8_e4m3fn
+        up_gate_proj_weight_name = self.added_weight_attrs[0]
+        down_proj_weight_name = self.added_weight_attrs[1]
+        self.ffn1_weight_shape = [
+            layer.num_local_experts,
+            layer.moe_intermediate_size * 2,
+            layer.hidden_size,
+        ]
+        self.ffn2_weight_shape = [
+            layer.num_local_experts,
+            layer.hidden_size,
+            layer.moe_intermediate_size,
+        ]
+        setattr(
+            layer,
+            up_gate_proj_weight_name,
+            layer.create_parameter(
+                shape=self.ffn1_weight_shape,
+                dtype=self.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        setattr(
+            layer,
+            down_proj_weight_name,
+            layer.create_parameter(
+                shape=self.ffn2_weight_shape,
+                dtype=self.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        # weight_scale
+        setattr(
+            layer,
+            self.added_scale_attrs[0],
+            layer.create_parameter(
+                shape=[
+                    layer.num_local_experts,
+                    layer.moe_intermediate_size * 2 // self.quant_config.weight_block_size[0],
+                    layer.hidden_size // self.quant_config.weight_block_size[1],
+                ],
+                dtype="float32",
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        setattr(
+            layer,
+            self.added_scale_attrs[1],
+            layer.create_parameter(
+                shape=[
+                    layer.num_local_experts,
+                    layer.hidden_size // self.quant_config.weight_block_size[0],
+                    layer.moe_intermediate_size // self.quant_config.weight_block_size[1],
+                ],
+                dtype="float32",
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
 
+    def process_loaded_weights(self, layer: nn.Layer, state_dict):
+        """
+        deepgemm create weight process.
+        """
         up_gate_proj_weights, down_proj_weights = layer.extract_moe_ffn_weights(state_dict)
 
         self.check(layer, up_gate_proj_weights, down_proj_weights)
@@ -56,11 +117,11 @@ def create_weights(self, layer: nn.Layer, state_dict):
                 weight_scale_list.append(scale)
             quanted_weight = paddle.stack(weight_list, axis=0)
             quanted_weight = quanted_weight.transpose([0, 2, 1]).contiguous()
-            create_and_set_parameter(layer, weight_name, quanted_weight)
+            getattr(layer, weight_name).copy_(quanted_weight, False)
 
             quanted_weight_scale = paddle.stack(weight_scale_list, axis=0)
             quanted_weight_scale = quanted_weight_scale.transpose([0, 2, 1]).contiguous()
-            create_and_set_parameter(layer, scale_name, quanted_weight_scale)
+            getattr(layer, scale_name).set_value(quanted_weight_scale)
 
     def process_prequanted_weights(self, layer: nn.Layer, state_dict):
         """
@@ -120,7 +181,7 @@ def process_prequanted_weights(self, layer: nn.Layer, state_dict):
             "down_proj_weight_scale": down_proj_weight_scale,
         }
         for name, tensor in name_tensor_map.items():
-            create_and_set_parameter(layer, name, tensor)
+            getattr(layer, name).set_value(tensor)
 
     def apply_ep_prefill(
         self,

fastdeploy/model_executor/layers/moe/fused_moe_marlin_backend.py

@@ -139,9 +139,63 @@ def __init__(self, quant_method=None):
         ]
         self.added_zeros_attrs = ["zeros0", "zeros1"]
 
-    def create_weights(self, layer: nn.Layer, state_dict):
+    def create_weights(self, layer: nn.Layer, **extra_weight_attrs):
+        self.default_dtype = layer._helper.get_default_dtype()
+        self.weight_dtype = "int32"
+
+        up_gate_proj_weight_name = self.added_weight_attrs[0]
+        down_proj_weight_name = self.added_weight_attrs[1]
+        self.ffn1_weight_shape = [
+            layer.num_local_experts,
+            layer.hidden_size // 16,
+            layer.moe_intermediate_size * 4,
+        ]
+        self.ffn2_weight_shape = [
+            layer.num_local_experts,
+            layer.moe_intermediate_size // 16,
+            layer.hidden_size * 2,
+        ]
+        setattr(
+            layer,
+            up_gate_proj_weight_name,
+            layer.create_parameter(
+                shape=self.ffn1_weight_shape,
+                dtype=self.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        setattr(
+            layer,
+            down_proj_weight_name,
+            layer.create_parameter(
+                shape=self.ffn2_weight_shape,
+                dtype=self.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        # weight_scale
+        setattr(
+            layer,
+            self.added_scale_attrs[0],
+            layer.create_parameter(
+                shape=[layer.num_local_experts, 1, layer.moe_intermediate_size * 2],
+                dtype=self.default_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        setattr(
+            layer,
+            self.added_scale_attrs[1],
+            layer.create_parameter(
+                shape=[layer.num_local_experts, 1, layer.hidden_size],
+                dtype=self.default_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+
+    def process_loaded_weights(self, layer: nn.Layer, state_dict):
         """
-        Marlin MoE create weight process.
+        Marlin MoE load weight process.
         """
         up_gate_proj_weights, down_proj_weights = layer.extract_moe_ffn_weights(state_dict)
         assert len(up_gate_proj_weights) == layer.num_local_experts
@@ -204,15 +258,6 @@ def create_weights(self, layer: nn.Layer, state_dict):
                 (weight_name, quanted_weight),
                 (scale_name, weight_scale),
             ]:
-                setattr(
-                    layer,
-                    name,
-                    layer.create_parameter(
-                        shape=tensor.shape,
-                        dtype=tensor.dtype,
-                        default_initializer=paddle.nn.initializer.Constant(0),
-                    ),
-                )
                 getattr(layer, name).set_value(tensor)
 
     def apply(

fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py

@@ -630,7 +630,7 @@ def create_weights(self, layer: nn.Layer, **extra_weight_attrs):
             layer,
             down_proj_weight_name,
             layer.create_parameter(
-                shape=self.ffn1_weight_shape,
+                shape=self.ffn2_weight_shape,
                 dtype=self.weight_dtype,
                 default_initializer=paddle.nn.initializer.Constant(0),
             ),