[OP]Unify MoE op with moe_permute path for bf16 GLM

custom_ops/gpu_ops/cpp_extensions.cc

@@ -537,7 +537,9 @@ std::vector<paddle::Tensor> TextImageGatherScatter(
     const bool is_scatter);
 
 std::vector<paddle::Tensor> count_tokens_per_expert_func(
-    const paddle::Tensor& topk_ids, int64_t num_experts);
+    const paddle::Tensor& topk_ids,
+    int64_t num_experts,
+    bool compute_padded_cumsum = false);
 void GetPositionIdsAndMaskEncoderBatch(
     const paddle::Tensor& seq_lens_encoder,
     const paddle::Tensor& seq_lens_decoder,

custom_ops/gpu_ops/moe/deepgemm_preprocess.cu

@@ -15,10 +15,11 @@
 #include "helper.h"
 #include "paddle/extension.h"
 
-template <typename scalar_t>
+template <typename scalar_t, bool kComputeCumsum>
 __global__ void cuda_kernel(const scalar_t *__restrict__ topk_ids,
                             int32_t *__restrict__ res,
                             int32_t *__restrict__ res_padded,
+                            int32_t *__restrict__ res_padded_cumsum,
                             size_t numel,
                             int num_experts) {
   extern __shared__ int32_t tokens_per_ep[];
@@ -35,48 +36,81 @@ __global__ void cuda_kernel(const scalar_t *__restrict__ topk_ids,
 
   __syncthreads();
 
-  for (size_t i = threadIdx.x; i < num_experts; i += blockDim.x) {
-    res[i] = tokens_per_ep[i];
-    res_padded[i] = (res[i] + 127) / 128 * 128;
+  if constexpr (kComputeCumsum) {
+    if (threadIdx.x == 0) {
+      int32_t running_sum = 0;
+      for (int i = 0; i < num_experts; i++) {
+        int32_t count = tokens_per_ep[i];
+        int32_t padded = (count + 127) / 128 * 128;
+        res[i] = count;
+        res_padded[i] = padded;
+        running_sum += padded;
+        res_padded_cumsum[i] = running_sum;
+      }
+    }
+  } else {
+    for (size_t i = threadIdx.x; i < num_experts; i += blockDim.x) {
+      res[i] = tokens_per_ep[i];
+      res_padded[i] = (tokens_per_ep[i] + 127) / 128 * 128;
+    }
   }
 }
 
 std::vector<paddle::Tensor> count_tokens_per_expert_func(
-    const paddle::Tensor &topk_ids, int64_t num_experts) {
+    const paddle::Tensor &topk_ids,
+    int64_t num_experts,
+    bool compute_padded_cumsum) {
   int topk_ids_numel = topk_ids.shape()[0] * topk_ids.shape()[1];
 
+  int64_t num_rows = compute_padded_cumsum ? 3 : 2;
   auto token_nums_per_expert = paddle::empty(
-      {2, num_experts}, paddle::DataType::INT32, topk_ids.place());
+      {num_rows, num_experts}, paddle::DataType::INT32, topk_ids.place());
 
   auto stream = topk_ids.stream();
   using scalar_t = int64_t;
 
-  // CUDA_CHECK(cudaGetLastError());
-  cuda_kernel<<<1, 1024, num_experts * sizeof(int32_t), stream>>>(
-      topk_ids.data<scalar_t>(),
-      token_nums_per_expert.data<int32_t>(),
-      token_nums_per_expert.data<int32_t>() + num_experts,
-      topk_ids_numel,
-      num_experts);
+  if (compute_padded_cumsum) {
+    cuda_kernel<scalar_t, true>
+        <<<1, 1024, num_experts * sizeof(int32_t), stream>>>(
+            topk_ids.data<scalar_t>(),
+            token_nums_per_expert.data<int32_t>(),
+            token_nums_per_expert.data<int32_t>() + num_experts,
+            token_nums_per_expert.data<int32_t>() + 2 * num_experts,
+            topk_ids_numel,
+            num_experts);
+  } else {
+    cuda_kernel<scalar_t, false>
+        <<<1, 1024, num_experts * sizeof(int32_t), stream>>>(
+            topk_ids.data<scalar_t>(),
+            token_nums_per_expert.data<int32_t>(),
+            token_nums_per_expert.data<int32_t>() + num_experts,
+            nullptr,
+            topk_ids_numel,
+            num_experts);
+  }
 
-  // CUDA_CHECK(cudaGetLastError());
   return {token_nums_per_expert};
 }
 
 std::vector<paddle::DataType> count_tokens_per_expert_func_infer_dtype(
-    const paddle::DataType &topk_ids_dtype, int64_t num_experts) {
+    const paddle::DataType &topk_ids_dtype,
+    int64_t num_experts,
+    bool compute_padded_cumsum) {
   return {paddle::DataType::INT32};
 }
 
 std::vector<std::vector<int64_t>> count_tokens_per_expert_func_infer_shape(
-    const std::vector<int64_t> &topk_ids_shape, int64_t num_experts) {
-  return {{2, num_experts}};
+    const std::vector<int64_t> &topk_ids_shape,
+    int64_t num_experts,
+    bool compute_padded_cumsum) {
+  int64_t num_rows = compute_padded_cumsum ? 3 : 2;
+  return {{num_rows, num_experts}};
 }
 
 PD_BUILD_STATIC_OP(count_tokens_per_expert_func)
     .Inputs({"topk_ids"})
     .Outputs({"token_nums_per_expert"})
-    .Attrs({"num_experts:int64_t"})
+    .Attrs({"num_experts:int64_t", "compute_padded_cumsum:bool"})
     .SetKernelFn(PD_KERNEL(count_tokens_per_expert_func))
     .SetInferShapeFn(PD_INFER_SHAPE(count_tokens_per_expert_func_infer_shape))
     .SetInferDtypeFn(PD_INFER_DTYPE(count_tokens_per_expert_func_infer_dtype));

fastdeploy/model_executor/layers/moe/fused_moe_cutlass_backend.py

@@ -28,7 +28,11 @@
 from .fused_moe_backend_base import UnquantizedFusedMoEMethod
 
 if current_platform.is_cuda():
-    from fastdeploy.model_executor.ops.gpu import moe_expert_dispatch, moe_expert_reduce
+    from fastdeploy.model_executor.ops.gpu import (
+        count_tokens_per_expert_func,
+        moe_expert_dispatch,
+        moe_expert_reduce,
+    )
 
     try:
         from fastdeploy.model_executor.ops.gpu import (
@@ -126,14 +130,15 @@ def apply_ep_prefill(
         # 1. Select topk experts and weights
         topk_idx, topk_weights = self.ep_prefill_runner.moe_select(layer, gate_out)
         # 2. EP Dispatch
+        dispatch_kwargs = {"expert_alignment": 128} if fastdeploy.envs.FD_USE_PHI_MOE_PERMUTE else {}
         (
             recv_x,
             recv_topk_idx,
             recv_topk_weights,
             recv_num_tokens_per_expert_list,
             handle,
             event,
-        ) = self.ep_prefill_runner.dispatch(x, topk_idx, topk_weights)
+        ) = self.ep_prefill_runner.dispatch(x, topk_idx, topk_weights, **dispatch_kwargs)
 
         if topk_ids_hookfunc is not None:
             topk_ids_hookfunc(topk_ids=topk_idx)
@@ -146,54 +151,91 @@ def apply_ep_prefill(
         # 3. Compute ffn
         if token_all_num > 0:
             logger.debug(f"token_all_num {token_all_num}")
-            (
-                permute_input,
-                permute_indices_per_token,
-                recv_num_tokens_per_expert_list_cumsum,
-                dst_weights,
-                dst_indices,
-                cumsum_idx_gpu,
-                expert_idx_per_token,
-                dequant_scale,
-            ) = fastdeploy.model_executor.ops.gpu.ep_moe_expert_dispatch(
-                recv_x,
-                recv_topk_idx,
-                recv_topk_weights,
-                (layer.up_gate_proj_in_scale if hasattr(layer, "up_gate_proj_in_scale") else None),
-                recv_num_tokens_per_expert_list,
-                token_all_num,
-                self.moe_quant_type,
-            )
-            if not layer.with_bias and self.moe_quant_type != "w4a8" and self.moe_quant_type != "w4afp8":
-                # only w4a8 and w4afp8 need expert_idx_per_token
-                # Other need not this tensor, so we make it None.
-                expert_idx_per_token = None
+
+            if fastdeploy.envs.FD_USE_PHI_MOE_PERMUTE and self.moe_quant_type == "w16a16":
+                # --- moe_permute / moe_unpermute path ---
+                recv_topk_idx_i32 = recv_topk_idx.astype(paddle.int32)
+                (permute_input, permute_indices_per_token, dst_weights, _scale_out) = paddle.nn.functional.moe_permute(
+                    hidden_states=recv_x,
+                    scale=None,
+                    expert_routemap_topk=recv_topk_idx_i32,
+                    expert_prob_topk=recv_topk_weights,
+                    num_experts=layer.num_local_experts,
+                    tokens_per_expert=[],
+                    padding_alignment=128,
+                    override_buffer_size=token_all_num,
+                )
+
+                token_nums_per_expert_cumsum = count_tokens_per_expert_func(
+                    recv_topk_idx, layer.num_local_experts, True
+                )[2].cast(paddle.int64)
+                ffn_out = self.compute_ffn(
+                    layer,
+                    permute_input,
+                    token_nums_per_expert_cumsum,
+                    None,
+                    False,
+                    -1,
+                    None,
+                    None,
+                )
+
+                tmp_ffn_out, _out_probs = paddle.nn.functional.moe_unpermute(
+                    hidden_states_unzipped=ffn_out,
+                    zipped_expertwise_rowmap=permute_indices_per_token,
+                    expert_routemap_topk=recv_topk_idx_i32,
+                    token_prob_unzipped=dst_weights,
+                    total_zipped_tokens=recv_x.shape[0],
+                    num_experts=layer.num_local_experts,
+                    using_weighted_combine=True,
+                )
             else:
-                expert_idx_per_token = expert_idx_per_token.cast("int64")
+                # --- original ep_moe_expert_dispatch / combine path ---
+                (
+                    permute_input,
+                    permute_indices_per_token,
+                    recv_num_tokens_per_expert_list_cumsum,
+                    dst_weights,
+                    dst_indices,
+                    cumsum_idx_gpu,
+                    expert_idx_per_token,
+                    dequant_scale,
+                ) = fastdeploy.model_executor.ops.gpu.ep_moe_expert_dispatch(
+                    recv_x,
+                    recv_topk_idx,
+                    recv_topk_weights,
+                    (layer.up_gate_proj_in_scale if hasattr(layer, "up_gate_proj_in_scale") else None),
+                    recv_num_tokens_per_expert_list,
+                    token_all_num,
+                    self.moe_quant_type,
+                )
+                if not layer.with_bias and self.moe_quant_type != "w4a8" and self.moe_quant_type != "w4afp8":
+                    expert_idx_per_token = None
+                else:
+                    expert_idx_per_token = expert_idx_per_token.cast("int64")
 
-            if hasattr(layer, "up_gate_proj_in_scale"):
-                dequant_scale = None
+                if hasattr(layer, "up_gate_proj_in_scale"):
+                    dequant_scale = None
 
-            ffn_out = self.compute_ffn(
-                layer,
-                permute_input,
-                recv_num_tokens_per_expert_list_cumsum,
-                expert_idx_per_token,
-                False,
-                -1,
-                dequant_scale,
-            )
+                ffn_out = self.compute_ffn(
+                    layer,
+                    permute_input,
+                    recv_num_tokens_per_expert_list_cumsum,
+                    expert_idx_per_token,
+                    False,
+                    -1,
+                    dequant_scale,
+                )
 
-            # prmt back per rank
-            tmp_ffn_out = fastdeploy.model_executor.ops.gpu.ep_moe_expert_combine(
-                ffn_out,
-                dst_weights,
-                permute_indices_per_token,
-                dst_indices,
-                None,  # down_proj_bias,
-                False,  # norm_topk_prob
-                1.0,
-            )
+                tmp_ffn_out = fastdeploy.model_executor.ops.gpu.ep_moe_expert_combine(
+                    ffn_out,
+                    dst_weights,
+                    permute_indices_per_token,
+                    dst_indices,
+                    None,  # down_proj_bias,
+                    False,  # norm_topk_prob
+                    1.0,
+                )
         else:
             tmp_ffn_out = recv_x
 
@@ -276,6 +318,69 @@ def apply_tp(
         """
         gate_out = gate(x)
         gate_out = gate_out.cast("float32")
+        if fastdeploy.envs.FD_USE_PHI_MOE_PERMUTE and self.moe_quant_type == "w16a16":
+            if layer.topk_method == "noaux_tc":
+                gate_out, topk_weights, topk_idx = get_moe_scores(
+                    gate_out,
+                    layer.n_group,
+                    layer.topk_group,
+                    layer.top_k,
+                    layer.routed_scaling_factor,
+                    layer.gate_correction_bias,
+                    getattr(layer, "renormalize", True),
+                )
+            else:
+                topk_idx, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
+                    gate_out,
+                    layer.gate_correction_bias,
+                    layer.top_k,
+                    True,  # apply_norm_weight
+                    False,
+                )
+            topk_idx_i32 = topk_idx.astype(paddle.int32)
+            override_buffer_size = x.shape[0] * layer.top_k + layer.num_experts * (128 - 1)
+            (permute_input, permute_indices_per_token, dst_weights, _scale_out) = (  # zipped_expertwise_rowmap
+                paddle.nn.functional.moe_permute(
+                    hidden_states=x,
+                    scale=None,
+                    expert_routemap_topk=topk_idx_i32,
+                    expert_prob_topk=topk_weights,
+                    num_experts=layer.num_experts,
+                    tokens_per_expert=[],
+                    padding_alignment=128,
+                    override_buffer_size=override_buffer_size,
+                )
+            )
+
+            # Row 2 of count_tokens_per_expert_func is the prefix sum token_nums_per_expert.
+            token_nums_per_expert_cumsum = count_tokens_per_expert_func(topk_idx, layer.num_experts, True)[2].cast(
+                paddle.int64
+            )
+            if topk_ids_hookfunc is not None:
+                topk_ids_hookfunc(topk_ids=topk_idx)
+
+            ffn_out = self.compute_ffn(
+                layer,
+                permute_input,
+                token_nums_per_expert_cumsum,
+                None,  # expert_idx_per_token not needed for w16a16 without bias
+                False,
+                -1,
+                None,  # dequant_scale
+                None,  # max_tokens_per_expert
+            )
+
+            fused_moe_out, _out_probs = paddle.nn.functional.moe_unpermute(
+                hidden_states_unzipped=ffn_out,
+                zipped_expertwise_rowmap=permute_indices_per_token,
+                expert_routemap_topk=topk_idx_i32,
+                token_prob_unzipped=dst_weights,
+                total_zipped_tokens=x.shape[0],
+                num_experts=layer.num_experts,
+                using_weighted_combine=True,
+            )
+            return fused_moe_out
+
         if layer.topk_method == "noaux_tc":
             gate_out, topk_weights, topk_idx = get_moe_scores(
                 gate_out,
@@ -286,6 +391,7 @@ def apply_tp(
                 layer.gate_correction_bias,
                 getattr(layer, "renormalize", True),
             )
+
             (
                 permute_input,
                 token_nums_per_expert,
@@ -340,7 +446,6 @@ def apply_tp(
             expert_idx_per_token = None
         else:
             expert_idx_per_token = expert_idx_per_token.cast("int64")
-
         ffn_out = self.compute_ffn(
             layer,
             permute_input,
@@ -362,7 +467,6 @@ def apply_tp(
             norm_topk_prob=False if layer.topk_method == "noaux_tc" else True,
             routed_scaling_factor=1.0,
         )
-
         return fused_moe_out