fix ep prefill (#2762)

custom_ops/gpu_ops/cpp_extensions.cc

@@ -158,7 +158,8 @@ std::vector<paddle::Tensor> EPMoeExpertDispatchFP8(
     const paddle::Tensor &input, const paddle::Tensor &scale,
     const paddle::Tensor &topk_ids, const paddle::Tensor &topk_weights,
     const paddle::Tensor &token_nums_per_expert,
-    const paddle::Tensor &token_nums_per_expert_padded);
+    const paddle::Tensor &token_nums_per_expert_padded,
+    const bool use_in_ep, const int token_nums_this_rank_padded);
 
 std::vector<paddle::Tensor> PerTokenQuant(paddle::Tensor &input,
                                           const int block_size);

custom_ops/gpu_ops/moe/ep_moe_prefill_func.cu

@@ -870,7 +870,9 @@ std::vector<paddle::Tensor> EPMoeExpertDispatchFP8(
     const paddle::Tensor& topk_ids,
     const paddle::Tensor& topk_weights,
     const paddle::Tensor& num_experts_per_rank_tensor,
-    const paddle::Tensor& num_experts_per_rank_padded_tensor) {
+    const paddle::Tensor& num_experts_per_rank_padded_tensor,
+    const bool use_in_ep,
+    const int token_nums_this_rank_padded) {
   const auto input_type = input.dtype();
   const int moe_topk = topk_ids.dims()[1];
   auto place = input.place();
@@ -886,22 +888,21 @@ std::vector<paddle::Tensor> EPMoeExpertDispatchFP8(
   const int hidden_size = input.dims()[input_dims.size() - 1];
   const int num_experts_per_rank = num_experts_per_rank_tensor.dims()[0];
 
-  int32_t token_nums_this_rank_padded = token_rows * moe_topk + num_experts_per_rank * (128-1);
+  int32_t token_nums_feed_to_ffn = use_in_ep ? token_nums_this_rank_padded : token_rows * moe_topk + num_experts_per_rank * (128-1);
-  // token_nums_this_rank_padded = token_nums_this_rank_padded_useless;
 
   auto permute_input = GetEmptyTensor(
-    {token_nums_this_rank_padded, hidden_size},
+    {token_nums_feed_to_ffn, hidden_size},
     input_type,
     place);
   auto permute_scale = GetEmptyTensor(
-    {token_nums_this_rank_padded, hidden_size / 128},
+    {token_nums_feed_to_ffn, hidden_size / 128},
     paddle::DataType::FLOAT32,
     place);
 
-  auto m_indices = paddle::full({token_nums_this_rank_padded}, -1, paddle::DataType::INT32, place);
+  auto m_indices = paddle::full({token_nums_feed_to_ffn}, -1, paddle::DataType::INT32, place);
   auto token_nums_per_expert_cumsum = GetEmptyTensor({num_experts_per_rank}, paddle::DataType::INT64, place);
   auto token_nums_per_expert_padded_cumsum = GetEmptyTensor({num_experts_per_rank}, paddle::DataType::INT64, place);
-  auto dst_weights = GetEmptyTensor({token_nums_this_rank_padded}, paddle::DataType::FLOAT32, place);
+  auto dst_weights = GetEmptyTensor({token_nums_feed_to_ffn}, paddle::DataType::FLOAT32, place);
   auto dst_indices = GetEmptyTensor({num_rows, num_experts_per_rank}, paddle::DataType::INT32, place);
   auto permute_indices_per_token = paddle::full({num_experts_per_rank, num_rows}, -1, paddle::DataType::INT32, place);
   auto cumsum_idx_gpu = paddle::full({num_experts_per_rank}, 0, paddle::DataType::INT32, place);
@@ -949,4 +950,5 @@ PD_BUILD_STATIC_OP(ep_moe_expert_dispatch_fp8)
               "dst_indices",
               "cumsum_idx_gpu",
               "m_indices"})
+    .Attrs({"use_in_ep:bool", "token_nums_this_rank_padded:int"})
     .SetKernelFn(PD_KERNEL(EPMoeExpertDispatchFP8));

fastdeploy/model_executor/layers/attention/append_attn_backend.py

@@ -33,6 +33,7 @@ from fastdeploy.config import FDConfig
 from fastdeploy.model_executor.layers.attention.attention import Attention
 from fastdeploy.model_executor.layers.attention.base_attention_backend import (
     AttentionBackend, AttentionMetadata)
+from fastdeploy.model_executor.layers.attention.utils import init_rank_and_device_id
 from fastdeploy.worker.forward_meta import ForwardMeta
 
 
@@ -91,7 +92,6 @@ class AppendAttentionBackend(AttentionBackend):
         self.use_speculate: bool = self.speculative_method is not None
         self.speculate_max_draft_token_num: int = fd_config.speculative_config.num_speculative_tokens
         self.keep_pd_step_flag: bool = fd_config.speculative_config.model_type == "mtp"
-        self.rank: int = fd_config.parallel_config.tensor_parallel_rank
 
         self.kv_num_heads: int = kv_num_heads
         self.num_heads: int = num_heads
@@ -104,16 +104,11 @@ class AppendAttentionBackend(AttentionBackend):
         self.use_pd_disaggregation: int = int(
             os.getenv("FLAGS_use_pd_disaggregation", 0))
         self.start_layer_index: int = fd_config.model_config.start_layer_index
-        self.device_id: int = os.getenv("CUDA_VISIBLE_DEVICES", None)
 
         if fd_config.parallel_config.expert_parallel_rank is None:
             fd_config.parallel_config.expert_parallel_rank = 0
-        device_id = self.rank + fd_config.parallel_config.tensor_parallel_degree * \
+        
-            fd_config.parallel_config.expert_parallel_rank
+        self.rank, self.device_id = init_rank_and_device_id(fd_config)
-        if self.device_id is None:
-            self.device_id = device_id
-        else:
-            self.device_id = self.device_id.split(",")[device_id]
 
     def init_attention_metadata(self, forward_meta: ForwardMeta):
         """Initialize attntion metadata hence all layers in the forward pass can reuse it."""

fastdeploy/model_executor/layers/attention/flash_attn_backend.py

@@ -34,6 +34,7 @@ from fastdeploy.model_executor.layers.attention.base_attention_backend import (
 from fastdeploy.model_executor.layers.attention.ops import (
     get_block_shape_and_split_kv_block, gqa_rope_write_cache,
     init_signal_layerwise, open_shm_and_get_meta_signal, pre_cache_len_concat)
+from fastdeploy.model_executor.layers.attention.utils import init_rank_and_device_id
 from fastdeploy.worker.forward_meta import ForwardMeta
 
 
@@ -100,22 +101,16 @@ class FlashAttentionBackend(AttentionBackend):
         self.use_speculate = self.speculative_method is not None
         self.speculate_max_draft_token_num = fd_config.speculative_config.num_speculative_tokens
         self.keep_pd_step_flag: bool = fd_config.speculative_config.model_type == "mtp"
-        self.rank: int = fd_config.parallel_config.tensor_parallel_rank
 
         # pd_disaggregation
         self.use_pd_disaggregation: int = int(
             os.getenv("FLAGS_use_pd_disaggregation", 0))
         self.start_layer_index: int = fd_config.model_config.start_layer_index
-        self.device_id: int = os.getenv("CUDA_VISIBLE_DEVICES", None)
 
         if fd_config.parallel_config.expert_parallel_rank is None:
             fd_config.parallel_config.expert_parallel_rank = 0
-        device_id = self.rank + fd_config.parallel_config.tensor_parallel_degree * \
+            
-            fd_config.parallel_config.expert_parallel_rank
+        self.rank, self.device_id = init_rank_and_device_id(fd_config)
-        if self.device_id is None:
-            self.device_id = device_id
-        else:
-            self.device_id = self.device_id.split(",")[device_id]
 
     def get_attntion_meta(self):
         """get_attntion_meta"""

fastdeploy/model_executor/layers/attention/mla_attention_backend.py

@@ -41,6 +41,7 @@ from fastdeploy.config import FDConfig
 from fastdeploy.model_executor.layers.attention.attention import Attention
 from fastdeploy.model_executor.layers.attention.base_attention_backend import (
     AttentionBackend, AttentionMetadata)
+from fastdeploy.model_executor.layers.attention.utils import init_rank_and_device_id
 from fastdeploy.worker.forward_meta import ForwardMeta
 
 
@@ -109,7 +110,6 @@ class MLAAttentionBackend(AttentionBackend):
         self.use_speculate: bool = self.speculative_method is not None
         self.speculate_max_draft_token_num: int = fd_config.speculative_config.num_speculative_tokens
         self.keep_pd_step_flag: bool = fd_config.speculative_config.model_type == "mtp"
-        self.rank: int = fd_config.parallel_config.tensor_parallel_rank
 
         self.kv_num_heads: int = kv_num_heads
         self.num_heads: int = num_heads
@@ -135,10 +135,8 @@ class MLAAttentionBackend(AttentionBackend):
             os.getenv("FLAGS_use_pd_disaggregation", 0))
         self.start_layer_index: int = fd_config.model_config.start_layer_index
         self.device_id: int = os.getenv("CUDA_VISIBLE_DEVICES", None)
-        if self.device_id is None:
+        
-            self.device_id = self.rank
+        self.rank, self.device_id = init_rank_and_device_id(fd_config)
-        else:
-            self.device_id = self.device_id.split(",")[self.rank]
 
     def init_attention_metadata(self, forward_meta: ForwardMeta):
         """Initialize attention metadata hence all layers in the forward pass can reuse it."""

fastdeploy/model_executor/layers/attention/utils.py

@@ -0,0 +1,36 @@
+"""
+# 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 os
+from fastdeploy.config import FDConfig
+
+def init_rank_and_device_id(fd_config: FDConfig):
+    """
+    
+    """
+    rank = (fd_config.parallel_config.expert_parallel_rank * 
+        fd_config.parallel_config.tensor_parallel_degree + fd_config.parallel_config.tensor_parallel_rank)
+
+    cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES", None)
+
+    if cuda_visible_devices is None:
+        device_id = rank
+    else:
+        cuda_visible_devices = cuda_visible_devices.split(",")
+        rank_index = rank % len(cuda_visible_devices)
+        device_id = cuda_visible_devices[rank_index]
+
+    return rank, device_id

fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py

@@ -144,7 +144,10 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
         if token_all_num > 0:
             logger.info(f"token_all_num {token_all_num}")
             (recv_x, recv_x_scale) = recv_x
-            tmp = count_tokens_per_expert_func(recv_topk_idx, layer.num_local_experts)
+            
+            token_nums_this_rank = count_tokens_per_expert_func(recv_topk_idx, layer.num_local_experts)
+            token_nums_this_rank_padded = sum(token_nums_this_rank[1].numpy().tolist())
+            
             (
                 permute_input,
                 permute_scale,
@@ -160,8 +163,10 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
                 recv_x_scale,
                 recv_topk_idx,
                 recv_topk_weights,
-                tmp[0],
+                token_nums_this_rank[0],
-                tmp[1]
+                token_nums_this_rank[1],
+                True,  # use_in_ep
+                token_nums_this_rank_padded,
             )
 
             permute_scale = permute_scale.transpose([1, 0]).contiguous()
@@ -328,6 +333,8 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
             topk_weights,
             tmp[0],
             tmp[1],
+            False, # use_in_ep
+            -1,
         )
 
         permute_scale = permute_scale.transpose([1, 0]).contiguous()