[Models] Add forward_meta to moe models' forward function (#5138)

* [Models] Add forward_meta to moe models' forward function

* fix missing param

* fix

* fix

* fix forward_meta

* fix test and remove chunked MoE releated in config

* fix test

* fix

* fix

fastdeploy/model_executor/layers/moe/moe.py

@@ -25,6 +25,7 @@ from fastdeploy.distributed.communication import (
     tensor_model_parallel_all_reduce,
     tensor_model_parallel_all_reduce_custom,
 )
+from fastdeploy.model_executor.forward_meta import ForwardMeta
 from fastdeploy.model_executor.layers.utils import get_tensor
 from fastdeploy.model_executor.utils import h2d_copy, slice_fn
 from fastdeploy.platforms import current_platform
@@ -621,7 +622,7 @@ class FusedMoE(nn.Layer):
 
         return out
 
-    def forward(self, x: paddle.Tensor, gate: nn.Layer):
+    def forward(self, x: paddle.Tensor, gate: nn.Layer, forward_meta: ForwardMeta):
         """
         Defines the forward computation of the moe layer.
 
@@ -641,7 +642,7 @@ class FusedMoE(nn.Layer):
         ):
             out = self.forward_split_allgather(x, gate)
         elif self.fd_config.parallel_config.use_ep and self.fd_config.parallel_config.enable_chunked_moe:
-            out = self.forward_chunked_moe(x, gate)
+            out = self.forward_chunked_moe(x, gate, forward_meta)
         else:
             out = self.forward_normal(x, gate)
 
@@ -652,7 +653,7 @@ class FusedMoE(nn.Layer):
                 out = tensor_model_parallel_all_reduce(out, self.tp_group)
         return out
 
-    def forward_chunked_moe(self, x: paddle.Tensor, gate: nn.Layer):
+    def forward_chunked_moe(self, x: paddle.Tensor, gate: nn.Layer, forward_meta: ForwardMeta):
         """
         Split input to multi chunk to reduce the memory usage of moe.
 
@@ -671,11 +672,11 @@ class FusedMoE(nn.Layer):
         # input size that are less than a chunk, less than the max size data or empty input
         # need to be repeated until the max chunk data infer MOE finished.
         if token_num > chunk_size:  # chunked moe
-            x_split_list = paddle.tensor_split(x, self.fd_config.parallel_config.moe_num_chunk, axis=0)
-            out_split_list = [None] * self.fd_config.parallel_config.moe_num_chunk
+            x_split_list = paddle.tensor_split(x, forward_meta.moe_num_chunk, axis=0)
+            out_split_list = [None] * forward_meta.moe_num_chunk
 
-            for i in range(self.fd_config.parallel_config.max_moe_num_chunk):
-                if i < self.fd_config.parallel_config.moe_num_chunk:
+            for i in range(forward_meta.max_moe_num_chunk):
+                if i < forward_meta.moe_num_chunk:
                     out_split_list[i] = self.quant_method.apply(self, x_split_list[i], gate)
                 else:
                     # just need to use real data to infer max_moe_num_chunk times.
@@ -685,7 +686,7 @@ class FusedMoE(nn.Layer):
         else:
             # when only one chunk, just need to use real data to infer once.
             out = self.quant_method.apply(self, x, gate)
-            for i in range(self.fd_config.parallel_config.max_moe_num_chunk - 1):
+            for i in range(forward_meta.max_moe_num_chunk - 1):
                 self.quant_method.apply(self, fake_x, gate)
 
         return out