support w4afp8 EP inference (#3044)

custom_ops/gpu_ops/cpp_extensions.cc

@@ -192,7 +192,8 @@ paddle::Tensor MoeExpertFFNFunc(
     const paddle::optional<paddle::Tensor>& down_proj_scale,
     const paddle::optional<paddle::Tensor>& down_proj_in_scale,
     const paddle::optional<paddle::Tensor>& expert_idx_per_token,
-    const std::string& quant_method, const bool used_in_ep_low_latency);
+    const std::string& quant_method, const bool used_in_ep_low_latency,
+    const int estimate_total_token_nums);
 
 paddle::Tensor MoeExpertFFNWint2Func(
     const paddle::Tensor& permute_input,

custom_ops/gpu_ops/helper.h

@@ -193,6 +193,12 @@ public:
   typedef uint8_t data_t;
 };
 
+template <> class PDTraits<paddle::DataType::FLOAT8_E4M3FN> {
+public:
+  typedef __nv_fp8_e4m3 DataType;
+  typedef paddle::float8_e4m3fn data_t;
+};
+
 template <typename T, int Size> struct alignas(sizeof(T) * Size) AlignedVector {
   T val[Size];
 

custom_ops/gpu_ops/moe/ep_moe_prefill_func.cu

@@ -314,7 +314,7 @@ std::vector<paddle::Tensor> EPMoeExpertCombine(
 }
 
 
-template <typename T, typename OutT, int NUM_EXPERTS_PER_RANK = 8, int RoundType = 1>
+template <typename T, typename OutT, int NUM_EXPERTS_PER_RANK = 8, int Kthread = 512, int RoundType = 1>
 __global__ void permute_x_kernel(const T *src_x,
                                  const int64_t *topk_idx,
                                  const float *topk_weights,
@@ -330,9 +330,9 @@ __global__ void permute_x_kernel(const T *src_x,
                                  int *dst_indices,
                                  int *cumsum_idx_gpu,
                                  int64_t *token_nums_per_expert_cumsum,
-                                 int64_t *expert_idx_per_token,
+                                 int64_t *expert_idx_per_token, // [num_rows, moe_topk]
                                  float max_bound = 127.0,
-                                 float min_bound = -127.0) { // [num_rows, moe_topk]
+                                 float min_bound = -127.0) {
     const int src_token_idx = blockIdx.x;
     const int tid = threadIdx.x;
     constexpr int vec_size = sizeof(int4) / sizeof(T);
@@ -375,10 +375,17 @@ __global__ void permute_x_kernel(const T *src_x,
             if (up_gate_proj_in_scale) {
               for (int i = 0; i < vec_size; i++) {
                 float quant_value = max_bound * up_gate_proj_in_scale[expert_now] * static_cast<float>(src_vec[i]);
-                if (RoundType == 0) {
+                if constexpr (std::is_same<OutT, int8_t>::value) {
-                  res_vec[i] = static_cast<OutT>(ClipFunc<float>(rint(quant_value), min_bound, max_bound));
+                  // w4aint8
+                  if (RoundType == 0) {
+                    res_vec[i] = static_cast<OutT>(ClipFunc<float>(rint(quant_value), min_bound, max_bound));
+                  } else {
+                    res_vec[i] = static_cast<OutT>(ClipFunc<float>(round(quant_value), min_bound, max_bound));
+                  }
                 } else {
-                  res_vec[i] = static_cast<OutT>(round(quant_value));
+                  // w4afp8
+                  float value = ClipFunc<float>(quant_value, min_bound, max_bound);
+                  res_vec[i] = static_cast<OutT>(value);
                 }
               }
             } else {
@@ -418,6 +425,10 @@ void EPMoeDispatchKernel(const paddle::Tensor& input,
   typedef typename traits_::DataType DataType_;
   typedef typename traits_::data_t data_t;
 
+  typedef PDTraits<paddle::DataType::FLOAT8_E4M3FN> traits_fp8;
+  typedef typename traits_fp8::DataType DataType_fp8;
+  typedef typename traits_fp8::data_t data_t_fp8;
+
   auto stream = input.stream();
   auto place = input.place();
   const int gridx = min(132 * 8, num_rows);
@@ -465,6 +476,50 @@ void EPMoeDispatchKernel(const paddle::Tensor& input,
         -127.0
       );
     }
+  } else if (moe_quant_type == "w4afp8") {
+    if (num_experts_per_rank == 8) {
+      permute_x_kernel<data_t, data_t_fp8, 8, 512><<<gridx, 512, 0, stream>>>(
+        input.data<data_t>(),
+        topk_ids.data<int64_t>(),
+        topk_weights.data<float>(),
+        token_nums_per_expert.data<int>(),
+        up_gate_proj_in_scale ? up_gate_proj_in_scale.get().data<float>() : nullptr,
+        moe_topk,
+        num_rows,
+        token_nums_this_rank,
+        hidden_size,
+        permute_input->data<data_t_fp8>(),
+        permute_indices_per_token->data<int>(),
+        dst_weights->data<float>(),
+        dst_indices->data<int>(),
+        cumsum_idx_gpu->data<int>(),
+        token_nums_per_expert_cumsum->data<int64_t>(),
+        expert_idx_per_token->data<int64_t>(),
+        448.0f,
+        -448.0f
+      );
+    } else if (num_experts_per_rank == 16) {
+      permute_x_kernel<data_t, data_t_fp8, 16, 512><<<gridx, 512, 0, stream>>>(
+        input.data<data_t>(),
+        topk_ids.data<int64_t>(),
+        topk_weights.data<float>(),
+        token_nums_per_expert.data<int>(),
+        up_gate_proj_in_scale ? up_gate_proj_in_scale.get().data<float>() : nullptr,
+        moe_topk,
+        num_rows,
+        token_nums_this_rank,
+        hidden_size,
+        permute_input->data<data_t_fp8>(),
+        permute_indices_per_token->data<int>(),
+        dst_weights->data<float>(),
+        dst_indices->data<int>(),
+        cumsum_idx_gpu->data<int>(),
+        token_nums_per_expert_cumsum->data<int64_t>(),
+        expert_idx_per_token->data<int64_t>(),
+        448.0f,
+        -448.0f
+      );
+    }
   } else {
     if (num_experts_per_rank == 8) {
       permute_x_kernel<data_t, data_t, 8><<<gridx, 512, 0, stream>>>(
@@ -538,7 +593,7 @@ std::vector<paddle::Tensor> EPMoeExpertDispatch(
 
   auto permute_input = GetEmptyTensor(
     {token_nums_this_rank, hidden_size},
-    moe_quant_type == "w4a8" ? paddle::DataType::INT8 : input_type,
+    moe_quant_type == "w4a8" ? paddle::DataType::INT8 : moe_quant_type == "w4afp8" ? paddle::DataType::FLOAT8_E4M3FN : input_type,
     place);
   auto num_experts_per_rank_tensor = GetEmptyTensor(
     {num_experts_per_rank},

custom_ops/gpu_ops/moe/fast_hardamard_kernel.cu

@@ -88,7 +88,7 @@ struct nv_type_traits<int8_t> {
     constexpr int kLogN = 7;                                                     \
     __VA_ARGS__                                                                  \
   } else {                                                                       \
-    PADDLE_THROW(phi::errors::Unimplemented("logN = %d is unsupport!", logN));   \
+    PADDLE_THROW(phi::errors::Unimplemented("logN = %d is unsupported!", logN));   \
   }
 
 #define DISPATCH_SP_VS(vec_size, VEC_SIZE, ...)                                          \
@@ -108,7 +108,7 @@ struct nv_type_traits<int8_t> {
     constexpr int VEC_SIZE = 1;                                                          \
     __VA_ARGS__                                                                          \
   } else {                                                                               \
-    PADDLE_THROW(phi::errors::Unimplemented("vec_size = %d is unsupport!", vec_size));   \
+    PADDLE_THROW(phi::errors::Unimplemented("vec_size = %d is unsupported!", vec_size));   \
   }
 
 #define DISPATCH_logN(logN, kLogN, ...)                                  \
@@ -605,26 +605,6 @@ void moe_fast_hardamard_kernel(const T *x,
         exchange_smem_pre<kNChunks, kChunksPerSmemSize, VecSize, kWarpSize, kNWarps, false, vec_t>(x_vals, smem_exchange);
     }
     if constexpr (kNChunks > 1) {
-//       T x_vals_transposed[VecSize][kNChunks] = {init_value};
-// #pragma unroll
-//       for (int c = 0; c < kNChunks; ++c) {
-// #pragma unroll
-//           for (int i = 0; i < VecSize; ++i) { x_vals_transposed[i][c] = x_vals[c][i]; }
-//       }
-//       if constexpr (kNChunks == 28) {
-//         hadamard_mult_thread_chunk_28<VecSize>(x_vals_transposed);
-//       } else if constexpr (kNChunks == 36) {
-//         hadamard_mult_thread_chunk_36<VecSize>(x_vals_transposed);
-//       } else {
-//         constexpr int kLogNChunks = cilog2(kNChunks);
-//         static_assert(1 << kLogNChunks == kNChunks, "kNChunks must be a power of 2");
-//         hadamard_mult_thread<kLogNChunks, VecSize>(x_vals_transposed);
-//       }
-// #pragma unroll
-//       for (int c = 0; c < kNChunks; ++c) {
-// #pragma unroll
-//           for (int i = 0; i < VecSize; ++i) { x_vals[c][i] = x_vals_transposed[i][c]; }
-//       }
       if constexpr (kNChunks == 28) {
         hadamard_mult_thread_28_transpose<T, VecSize>(x_vals);
       } else if constexpr (kNChunks == 36) {

custom_ops/gpu_ops/moe/fused_moe_op.h

@@ -72,6 +72,287 @@ __host__ __device__ constexpr static U arrayConvert(T const& input)
     return u;
 }
 
+struct uint8 {
+    uint4 u;
+    uint4 v;
+};
+
+template<int BYTES> struct BytesToType {};
+
+template<>
+struct BytesToType<32> {
+    using Type = uint8;
+    static_assert(sizeof(Type) == 32);
+};
+
+template<> struct BytesToType<16> {
+    using Type = uint4;
+    static_assert(sizeof(Type) == 16);
+};
+
+template<> struct BytesToType<8> {
+    using Type = uint64_t;
+    static_assert(sizeof(Type) == 8);
+};
+
+template<> struct BytesToType<4> {
+    using Type = uint32_t;
+    static_assert(sizeof(Type) == 4);
+};
+
+template<> struct BytesToType<2> {
+    using Type = uint16_t;
+    static_assert(sizeof(Type) == 2);
+};
+
+template<> struct BytesToType<1> {
+    using Type = uint8_t;
+    static_assert(sizeof(Type) == 1);
+};
+
+template <template <typename> class ReductionOp, typename T, int block_size>
+__inline__ __device__ T BlockAllReduce(T val) {
+  typedef cub::BlockReduce<T, block_size> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T result_broadcast;
+  T result = BlockReduce(temp_storage).Reduce(val, ReductionOp<T>());
+  if (threadIdx.x == 0) {
+    result_broadcast = result;
+  }
+  __syncthreads();
+  return result_broadcast;
+}
+
+template <typename T>
+struct SumOp {
+  __device__ __forceinline__ T operator()(T const& x, T const& y) { return x + y; }
+};
+
+template <typename InType, typename OutType>
+__forceinline__ __device__ OutType QuantHelperFunc(const InType input,
+                                                   const float scale,
+                                                   const float max_bound,
+                                                   const float min_bound) {
+  float quant_value = max_bound * scale * static_cast<float>(input);
+  return static_cast<OutType>(ClipFunc<float>(quant_value, min_bound, max_bound));
+}
+
+template <typename T, typename OutT, int VecSize, int Kthread>
+__global__ void masked_quantize_moe_input_kernel(const T* permuted_inputs,
+const int64_t* expert_idx_per_token,
+const float* quant_scales,
+const float quant_max_bound,
+const float quant_min_bound,
+const int64_t token_num,
+const int64_t dim,
+float* permuted_input_row_sum,
+const int64_t* recv_expert_count,
+const int num_max_tokens_per_expert,
+OutT* out) {
+using LoadT = AlignedVector<T, VecSize>;
+using LoadOutT = AlignedVector<OutT, VecSize>;
+LoadT input_vec;
+LoadOutT output_vec;
+float scale_factor = -7.0f / 512.0f;
+using vec_t = typename BytesToType<sizeof(OutT) * VecSize>::Type;
+for (int token_idx = blockIdx.x; token_idx < token_num; token_idx += gridDim.x) {
+  const auto token_idx_in_expert = token_idx % num_max_tokens_per_expert;
+  const auto expert_id = token_idx / num_max_tokens_per_expert;
+  if (token_idx_in_expert >= recv_expert_count[expert_id]) {
+      auto next_expert_start_idx = (expert_id + 1) * num_max_tokens_per_expert;
+      auto num_iters_to_next_expert = (next_expert_start_idx - token_idx - 1) / gridDim.x;
+      token_idx += num_iters_to_next_expert * gridDim.x;
+      continue;
+  }
+  int64_t expert_idx = expert_idx_per_token[token_idx];
+  float quant_scale = quant_scales[expert_idx];
+  float thread_row_sum = 0.0f;
+  for(int idx = threadIdx.x; idx < dim / VecSize; idx += blockDim.x) {
+    int64_t offset = token_idx * dim + idx * VecSize;
+    Load<T, VecSize>(&permuted_inputs[offset], &input_vec);
+    #pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      output_vec[i] = QuantHelperFunc<T, OutT>(input_vec[i], quant_scale, quant_max_bound, quant_min_bound);
+      thread_row_sum += static_cast<float>(output_vec[i]);
+    }
+    *(reinterpret_cast<vec_t*>(&out[offset])) = *(reinterpret_cast<const vec_t*>(&output_vec));
+  }
+  float block_row_sum = BlockAllReduce<SumOp, float, Kthread>(thread_row_sum);
+  permuted_input_row_sum[token_idx] = block_row_sum * scale_factor;
+  }
+}
+
+template <typename T, typename OutT, int VecSize, int Kthread>
+__global__ void quantize_moe_input_kernel(const T* permuted_inputs,
+const int64_t* expert_idx_per_token,
+const float* quant_scales,
+const float quant_max_bound,
+const float quant_min_bound,
+const int64_t token_num,
+const int64_t dim,
+float* permuted_input_row_sum,
+const int64_t* recv_expert_count,
+const int num_max_tokens_per_expert,
+OutT* out) {
+using LoadT = AlignedVector<T, VecSize>;
+using LoadOutT = AlignedVector<OutT, VecSize>;
+LoadT input_vec;
+LoadOutT output_vec;
+using vec_t = typename BytesToType<sizeof(OutT) * VecSize>::Type;
+float scale_factor = -7.0f / 512.0f;
+for (int token_idx = blockIdx.x; token_idx < token_num; token_idx += gridDim.x) {
+  int64_t expert_idx = expert_idx_per_token[token_idx];
+  float quant_scale = quant_scales[expert_idx];
+  float thread_row_sum = 0.0f;
+  for(int idx = threadIdx.x; idx < dim / VecSize; idx += blockDim.x) {
+    int64_t offset = token_idx * dim + idx * VecSize;
+    Load<T, VecSize>(&permuted_inputs[offset], &input_vec);
+    #pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      output_vec[i] = QuantHelperFunc<T, OutT>(input_vec[i], quant_scale, quant_max_bound, quant_min_bound);
+      thread_row_sum += static_cast<float>(output_vec[i]);
+    }
+    *(reinterpret_cast<vec_t*>(&out[offset])) = *(reinterpret_cast<const vec_t*>(&output_vec));
+  }
+  float block_row_sum = BlockAllReduce<SumOp, float, Kthread>(thread_row_sum);
+  permuted_input_row_sum[token_idx] = block_row_sum * scale_factor;
+  }
+}
+
+template <typename T, typename OutT>
+void quantize_moe_input(
+  const T* permuted_inputs,
+  const int64_t* expert_idx_per_token,
+  const float* quant_scales,
+  const float quant_max_bound,
+  const float quant_min_bound,
+  const int64_t token_num,
+  const int64_t dim,
+  float* permuted_input_row_sum,
+  const int64_t* recv_expert_count,
+  const int num_max_tokens_per_expert,
+  bool used_in_ep_low_latency,
+  OutT* out,
+  cudaStream_t stream) {
+    constexpr int VecSize = 16 / sizeof(T);
+    constexpr int threads_per_block = 128;
+    const int dev_id = 0;
+    int sm_count;
+    int act_blocks_per_sm;
+    cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev_id);
+    assert(dim % VecSize == 0);
+    auto kernel = used_in_ep_low_latency ? masked_quantize_moe_input_kernel<T, OutT, VecSize, threads_per_block> : quantize_moe_input_kernel<T, OutT, VecSize, threads_per_block>;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+        &act_blocks_per_sm, kernel, threads_per_block, 0);
+    const int num_blocks_per_wave = sm_count * act_blocks_per_sm;
+    dim3 grid;
+    grid.x = min(static_cast<int64_t>(num_blocks_per_wave), token_num);
+    kernel<<<grid, threads_per_block, 0, stream>>>(
+      permuted_inputs,
+      expert_idx_per_token,
+      quant_scales,
+      quant_max_bound,
+      quant_min_bound,
+      token_num,
+      dim,
+      permuted_input_row_sum,
+      recv_expert_count,
+      num_max_tokens_per_expert,
+      out);
+  }
+
+template <typename T, int VecSize, int Kthread>
+__global__ void masked_compute_row_sum_kernel(
+const T* permuted_inputs,
+const int64_t token_num,
+const int64_t dim,
+float* permuted_input_row_sum,
+const int64_t* recv_expert_count,
+const int num_max_tokens_per_expert) {
+using LoadT = AlignedVector<T, VecSize>;
+LoadT input_vec;
+float scale_factor = -7.0f / 512.0f;
+for (int token_idx = blockIdx.x; token_idx < token_num; token_idx += gridDim.x) {
+  const auto token_idx_in_expert = token_idx % num_max_tokens_per_expert;
+  const auto expert_id = token_idx / num_max_tokens_per_expert;
+  if (token_idx_in_expert >= recv_expert_count[expert_id]) {
+      auto next_expert_start_idx = (expert_id + 1) * num_max_tokens_per_expert;
+      auto num_iters_to_next_expert = (next_expert_start_idx - token_idx - 1) / gridDim.x;
+      token_idx += num_iters_to_next_expert * gridDim.x;
+      continue;
+  }
+  float thread_row_sum = 0.0f;
+  for(int idx = threadIdx.x; idx < dim / VecSize; idx += blockDim.x) {
+    int64_t offset = token_idx * dim + idx * VecSize;
+    Load<T, VecSize>(&permuted_inputs[offset], &input_vec);
+    #pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      thread_row_sum += static_cast<float>(input_vec[i]);
+    }
+  }
+  float block_row_sum = BlockAllReduce<SumOp, float, Kthread>(thread_row_sum);
+  permuted_input_row_sum[token_idx] = block_row_sum * scale_factor;
+  }
+}
+
+template <typename T, int VecSize, int Kthread>
+__global__ void compute_row_sum_kernel(
+const T* permuted_inputs,
+const int64_t token_num,
+const int64_t dim,
+float* permuted_input_row_sum,
+const int64_t* recv_expert_count,
+const int num_max_tokens_per_expert) {
+using LoadT = AlignedVector<T, VecSize>;
+LoadT input_vec;
+float scale_factor = -7.0f / 512.0f;
+for (int token_idx = blockIdx.x; token_idx < token_num; token_idx += gridDim.x) {
+  float thread_row_sum = 0.0f;
+  for(int idx = threadIdx.x; idx < dim / VecSize; idx += blockDim.x) {
+    int64_t offset = token_idx * dim + idx * VecSize;
+    Load<T, VecSize>(&permuted_inputs[offset], &input_vec);
+    #pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      thread_row_sum += static_cast<float>(input_vec[i]);
+    }
+  }
+  float block_row_sum = BlockAllReduce<SumOp, float, Kthread>(thread_row_sum);
+  permuted_input_row_sum[token_idx] = block_row_sum * scale_factor;
+  }
+}
+
+template <typename T>
+void compute_row_sum(
+  const T* permuted_inputs,
+  const int64_t token_num,
+  const int64_t dim,
+  float* permuted_input_row_sum,
+  const int64_t* recv_expert_count,
+  const int num_max_tokens_per_expert,
+  bool used_in_ep_low_latency,
+  cudaStream_t stream) {
+    constexpr int VecSize = 16 / sizeof(T);
+    constexpr int threads_per_block = 128;
+    const int dev_id = 0;
+    int sm_count;
+    int act_blocks_per_sm;
+    cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev_id);
+    assert(dim % VecSize == 0);
+    auto kernel = used_in_ep_low_latency ? masked_compute_row_sum_kernel<T, VecSize, threads_per_block> : compute_row_sum_kernel<T, VecSize, threads_per_block>;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+        &act_blocks_per_sm, kernel, threads_per_block, 0);
+    const int num_blocks_per_wave = sm_count * act_blocks_per_sm;
+    dim3 grid;
+    grid.x = min(static_cast<int64_t>(num_blocks_per_wave), token_num);
+    kernel<<<grid, threads_per_block, 0, stream>>>(
+      permuted_inputs,
+      token_num,
+      dim,
+      permuted_input_row_sum,
+      recv_expert_count,
+      num_max_tokens_per_expert);
+  }
+
 // ====================== Softmax things ===============================
 // We have our own implementation of softmax here so we can support transposing
 // the output in the softmax kernel when we extend this module to support

custom_ops/gpu_ops/moe/moe_ffn.cu

@@ -20,6 +20,7 @@
 #include "helper.h"
 #include "moe/fast_hardamard_kernel.h"
 #include "moe/fused_moe_helper.h"
+#include "w4afp8_gemm/w4afp8_gemm.h"
 
 template <paddle::DataType T>
 void MoeFFNKernel(const paddle::Tensor& permute_input,
@@ -33,7 +34,8 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
                   const paddle::optional<paddle::Tensor>& expert_idx_per_token,
                   const std::string& quant_method,
                   paddle::Tensor ffn_out,
-                  bool used_in_ep_low_latency) {
+                  bool used_in_ep_low_latency,
+                  const int estimate_total_token_nums) {
     using namespace phi;
     typedef PDTraits<T> traits_;
     typedef typename traits_::DataType DataType_;
@@ -60,19 +62,22 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
     constexpr size_t workspace_size = 1 * 1024 * 1024 * 1024; // for nf4 stream-k
     Allocator* allocator = paddle::GetAllocator(place);
     Allocator::AllocationPtr workspace;
-    if (quant_method == "weight_only_int4" || quant_method == "w4a8") {
+    if (quant_method == "weight_only_int4" || quant_method == "w4a8" || quant_method == "w4afp8") {
         inter_dim = inter_dim * 2;
     }
-    if (quant_method == "w4a8") {
+    if (quant_method == "w4a8" || quant_method == "w4afp8") {
         workspace = allocator->Allocate(
             SizeOf(paddle::DataType::INT8) * workspace_size);
     }
 
     const int64_t inter_size = inter_dim;
 
+    typedef PDTraits<paddle::DataType::FLOAT8_E4M3FN> traits_fp8;
+    typedef typename traits_fp8::DataType DataType_fp8;
+    typedef typename traits_fp8::data_t data_t_fp8;
 
     int num_experts_ = num_experts;
-    int num_max_tokens_per_expert;
+    int num_max_tokens_per_expert = 256;
     int expanded_active_expert_rows;
 
     paddle::Tensor fc1_out_tensor;
@@ -161,13 +166,49 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
             reinterpret_cast<NvType *>(fc1_out),
             const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
             total_rows_in_ll_else_minus1,
-            tune_total_rows,
+            used_in_ep_low_latency ? estimate_total_token_nums : tune_total_rows,
             inter_size,
             hidden_size,
             reinterpret_cast<char*>(workspace->ptr()),
             workspace_size,
             num_experts,
             stream);
+    } else if (quant_method == "w4afp8") {
+        typedef PDTraits<paddle::DataType::FLOAT8_E4M3FN> traits_fp8;
+        typedef typename traits_fp8::DataType DataType_fp8;
+        typedef typename traits_fp8::data_t data_t_fp8;
+
+        Allocator::AllocationPtr ffn1_input_row_sum;
+        ffn1_input_row_sum = allocator->Allocate(
+            sizeof(float) * expanded_active_expert_rows);
+
+        compute_row_sum(
+            permute_input.data<data_t_fp8>(),
+            expanded_active_expert_rows,
+            hidden_size,
+            reinterpret_cast<float*>(ffn1_input_row_sum->ptr()),
+            const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
+            num_max_tokens_per_expert,
+            used_in_ep_low_latency,
+            stream);
+
+
+        float* row_scale = nullptr;
+        DisPatchW4AFp8GemmWrapper(
+            reinterpret_cast<const DataType_fp8 *>(permute_input.data<data_t_fp8>()),
+            reinterpret_cast<const DataType_fp8 *>(up_gate_proj_weight.data<int8_t>()),
+            const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
+            reinterpret_cast<float*>(ffn1_input_row_sum->ptr()),
+            row_scale,
+            const_cast<paddle::Tensor*>(up_gate_proj_scale.get_ptr())
+                ->data<float>(),
+            reinterpret_cast<NvType *>(fc1_out),
+            used_in_ep_low_latency ? num_max_tokens_per_expert : 0,
+            num_max_tokens_per_expert,
+            num_experts,
+            inter_size,
+            hidden_size,
+            stream);
     } else {
         typename cutlass::WintQuantTraits<DataType_, cutlass::WintQuantMethod::kNone>::Arguments quant_args;
         fp16_moe_gemm_runner.moe_gemm_bias_act(
@@ -194,7 +235,6 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
         act_out_tensor = paddle::experimental::swiglu(fc1_out_tensor, nullptr);
     }
     auto act_out = act_out_tensor.data<data_t>();
-
     if (quant_method == "weight_only_int8") {
         typename cutlass::WintQuantTraits<DataType_, cutlass::WintQuantMethod::kWeightOnlyInt8>::Arguments quant_args;
         int8_moe_gemm_runner.moe_gemm(
@@ -267,13 +307,73 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
             reinterpret_cast<NvType *>(ffn_out_data),
             const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
             total_rows_in_ll_else_minus1,
-            tune_total_rows,
+            used_in_ep_low_latency ? estimate_total_token_nums : tune_total_rows,
             hidden_size,
             inter_size / 2,
             reinterpret_cast<char*>(workspace->ptr()),
             workspace_size,
             num_experts,
             stream);
+    } else if (quant_method == "w4afp8") {
+        data_t *ffn2_shift = nullptr;
+        data_t *ffn2_smooth = nullptr;
+        float* row_scale = nullptr;
+        Allocator::AllocationPtr fp8_act_out;
+        fp8_act_out = allocator->Allocate(
+            SizeOf(paddle::DataType::INT8) * act_out_tensor.numel());
+        Allocator::AllocationPtr ffn2_input_row_sum;
+        ffn2_input_row_sum = allocator->Allocate(
+            sizeof(float) * expanded_active_expert_rows);
+
+        // note(yuanxiaolan): optimize this
+        MoeFastHardamardWrapper<data_t, data_t>(
+            act_out_tensor.data<data_t>(),
+            expert_idx_per_token ? expert_idx_per_token.get().data<int64_t>() : nullptr,
+            const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
+            ffn2_shift, // ffn2_shift->data<T>(),
+            ffn2_smooth, // ffn2_smooth->data<T>(),
+            nullptr,
+            1,
+            448.0f,
+            -448.0f,
+            expanded_active_expert_rows,
+            inter_size / 2,
+            num_max_tokens_per_expert,
+            used_in_ep_low_latency,
+            act_out_tensor.data<data_t>(),
+            stream
+        );
+
+        quantize_moe_input<data_t, data_t_fp8>(act_out_tensor.data<data_t>(),
+            expert_idx_per_token ? expert_idx_per_token.get().data<int64_t>() : nullptr,
+            down_proj_in_scale ? const_cast<paddle::Tensor*>(down_proj_in_scale.get_ptr())->data<float>() : nullptr,
+            448.0f,
+            -448.0f,
+            expanded_active_expert_rows,
+            inter_size / 2,
+            reinterpret_cast<float*>(ffn2_input_row_sum->ptr()),
+            const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
+            num_max_tokens_per_expert,
+            used_in_ep_low_latency,
+            reinterpret_cast<data_t_fp8 *>(fp8_act_out->ptr()),
+            stream
+            );
+
+        DisPatchW4AFp8GemmWrapper(
+            reinterpret_cast<const DataType_fp8 *>(fp8_act_out->ptr()),
+            reinterpret_cast<const DataType_fp8 *>(down_proj_weight.data<int8_t>()),
+            const_cast<int64_t*>(tokens_expert_prefix_sum.data<int64_t>()),
+            reinterpret_cast<float*>(ffn2_input_row_sum->ptr()),
+            row_scale,
+            const_cast<paddle::Tensor*>(down_proj_scale.get_ptr())
+                    ->data<float>(),
+            reinterpret_cast<NvType*>(ffn_out_data),
+            used_in_ep_low_latency ? num_max_tokens_per_expert : 0,
+            num_max_tokens_per_expert,
+            num_experts,
+            hidden_size,
+            inter_size / 2,
+            stream);
     } else {
         typename cutlass::WintQuantTraits<DataType_, cutlass::WintQuantMethod::kNone>::Arguments quant_args;
         fp16_moe_gemm_runner.moe_gemm(
@@ -302,10 +402,12 @@ paddle::Tensor MoeExpertFFNFunc(
     const paddle::optional<paddle::Tensor>& down_proj_scale,
     const paddle::optional<paddle::Tensor>& down_proj_in_scale,
     const paddle::optional<paddle::Tensor>& expert_idx_per_token,
-    const std::string& quant_method, const bool used_in_ep_low_latency) {
+    const std::string& quant_method, const bool used_in_ep_low_latency,
+    const int estimate_total_token_nums) {
 
-    cudaCheckError();
+const auto t_type = (quant_method == "w4a8") ? up_gate_proj_scale.get().dtype() :
-    const auto t_type = quant_method == "w4a8" ? up_gate_proj_scale.get().dtype() : permute_input.dtype();
+                    (quant_method == "w4afp8") ? paddle::DataType::BFLOAT16 :
+                    permute_input.dtype();
     auto ffn_out = paddle::empty_like(permute_input, t_type);
 
     switch (t_type) {
@@ -320,7 +422,9 @@ paddle::Tensor MoeExpertFFNFunc(
                                                      down_proj_in_scale,
                                                      expert_idx_per_token,
                                                      quant_method,
-                                                     ffn_out, used_in_ep_low_latency);
+                                                     ffn_out,
+                                                     used_in_ep_low_latency,
+                                                     estimate_total_token_nums);
             break;
         case paddle::DataType::FLOAT16:
             MoeFFNKernel<paddle::DataType::FLOAT16>(permute_input,
@@ -333,7 +437,9 @@ paddle::Tensor MoeExpertFFNFunc(
                                                     down_proj_in_scale,
                                                     expert_idx_per_token,
                                                     quant_method,
-                                                    ffn_out, used_in_ep_low_latency);
+                                                    ffn_out,
+                                                    used_in_ep_low_latency,
+                                                    estimate_total_token_nums);
             break;
         default:
             PD_THROW("Unsupported data type for MoeExpertFFN");
@@ -351,7 +457,8 @@ std::vector<paddle::Tensor> MoeExpertFFN(
     const paddle::optional<paddle::Tensor>& down_proj_scale,
     const paddle::optional<paddle::Tensor>& down_proj_in_scale,
     const paddle::optional<paddle::Tensor>& expert_idx_per_token,
-    const std::string& quant_method, const bool used_in_ep_low_latency) {
+    const std::string& quant_method, const bool used_in_ep_low_latency,
+    const int estimate_total_token_nums) {
     return {MoeExpertFFNFunc(permute_input,
                              tokens_expert_prefix_sum,
                              up_gate_proj_weight,
@@ -361,7 +468,9 @@ std::vector<paddle::Tensor> MoeExpertFFN(
                              down_proj_scale,
                              down_proj_in_scale,
                              expert_idx_per_token,
-                             quant_method, used_in_ep_low_latency)};
+                             quant_method,
+                             used_in_ep_low_latency,
+                             estimate_total_token_nums)};
 }
 
 std::vector<std::vector<int64_t>> MoeExpertFFNInferShape(
@@ -375,7 +484,8 @@ std::vector<std::vector<int64_t>> MoeExpertFFNInferShape(
     const paddle::optional<std::vector<int64_t>>& down_proj_in_scale_shape,
     const paddle::optional<std::vector<int64_t>>& expert_idx_per_token_shape,
     const std::string& quant_method,
-    const bool used_in_ep_low_latency) {
+    const bool used_in_ep_low_latency,
+    const int estimate_total_token_nums) {
     return {permute_input_shape};
 }
 
@@ -388,8 +498,9 @@ std::vector<paddle::DataType> MoeExpertFFNInferDtype(
     const paddle::optional<paddle::DataType> &up_gate_proj_scale_dtype,
     const paddle::optional<paddle::DataType> &down_proj_scale_dtype,
     const paddle::optional<paddle::DataType> &down_proj_in_scale_dtype,
-    const std::string &quant_method, const bool used_in_ep_low_latency) {
+    const std::string &quant_method, const bool used_in_ep_low_latency,
-  if (quant_method == "w4a8") {
+    const int estimate_total_token_nums) {
+  if (quant_method == "w4a8" || quant_method == "w4afp8") {
     return {up_gate_proj_scale_dtype.get()};
   } else {
     return {permute_input_dtype};
@@ -460,7 +571,7 @@ PD_BUILD_STATIC_OP(moe_expert_ffn)
              paddle::Optional("down_proj_in_scale"),
              paddle::Optional("expert_idx_per_token")})
     .Outputs({"output_tensor"})
-    .Attrs({"quant_method:std::string", "used_in_ep_low_latency:bool"})
+    .Attrs({"quant_method:std::string", "used_in_ep_low_latency:bool", "estimate_total_token_nums:int"})
     .SetKernelFn(PD_KERNEL(MoeExpertFFN))
     .SetInferShapeFn(PD_INFER_SHAPE(MoeExpertFFNInferShape))
     .SetInferDtypeFn(PD_INFER_DTYPE(MoeExpertFFNInferDtype));

custom_ops/gpu_ops/w4afp8_gemm/mainloop_fwd.h

@@ -103,7 +103,7 @@ struct CollectiveMainloopFwd {
         LayoutT layout_C;
         const float *weight_scale;
         const float *input_row_sum;
-        const int * tokens;
+        const int64_t * tokens;
     };
 
     struct Params {
@@ -114,7 +114,7 @@ struct CollectiveMainloopFwd {
         ElementOutput * ptr_C;
         const float *weight_scale;
         const float *input_row_sum;
-        const int * tokens;
+        const int64_t * tokens;
     };
 
 
@@ -153,8 +153,8 @@ struct CollectiveMainloopFwd {
         TiledMma tiled_mma,
         const float *input_row_sum,
         const float *weight_scale,
-        const int tokens,
+        const int64_t tokens,
-        const int pre_fix_tokens,
+        const int64_t pre_fix_tokens,
         const int bidm,
         const int bidn,
         const int bidb,

custom_ops/gpu_ops/w4afp8_gemm/w4afp8_gemm.cu

@@ -19,6 +19,7 @@
 #include "helper.h"
 #include "paddle/extension.h"
 #include "w4afp8_gemm_template.h"
+#include "w4afp8_gemm.h"
 
 
 void weight_convert(const uint8_t *weight, uint8_t *weight_new, int batch, int M, int K) {
@@ -39,7 +40,22 @@ void weight_convert(const uint8_t *weight, uint8_t *weight_new, int batch, int M
     }
 }
 
+template <typename T> class NVTraits;
 
+template <> class NVTraits<__nv_fp8_e4m3> {
+public:
+    typedef cutlass::float_e4m3_t data_t;
+};
+
+template <> class NVTraits<__nv_bfloat16>{
+public:
+    typedef cutlass::bfloat16_t data_t;
+};
+
+template <> class NVTraits<half>{
+public:
+    typedef cutlass::half_t data_t;
+};
 
 
 
@@ -48,15 +64,15 @@ template <typename OutputType>
 void DisPatchW4AFp8Gemm(
         const cutlass::float_e4m3_t* input,
         const cutlass::float_e4m3_t* weight,
-        const int * tokens,
+        const int64_t * tokens,
         const float * input_row_sum,
         const float * weight_scale,
         OutputType * out,
-        const int token_padding_size,
+        const int64_t token_padding_size,
-        const int max_tokens,
+        const int64_t max_tokens,
         const int batch_size,
-        const int M,
+        const int64_t M,
-        const int K,
+        const int64_t K,
         cudaStream_t stream) {
 
     int kBlockN = (max_tokens + 15) / 16 * 16;
@@ -87,9 +103,10 @@ std::vector<paddle::Tensor> W4AFp8Gemm(
         const paddle::Tensor& tokens, // If tokenpadding=0, this tensor represents the prefix sum of tensors, otherwise it represents the number of tokens in each group
         const paddle::Tensor& input_row_sum,
         const paddle::Tensor& weight_scale,
-        const int token_padding_size,
+        const int64_t token_padding_size,
-        const int max_tokens,
+        const int64_t max_tokens,
-        const bool is_bflot16) {
+        const bool is_bfloat16) {
+
 
     const int batch_size = weight.dims()[0];
     const int M = weight.dims()[1];
@@ -101,13 +118,13 @@ std::vector<paddle::Tensor> W4AFp8Gemm(
 
     if (token_padding_size == 0) {
         const int all_tokens = input.dims()[0];
-        if (is_bflot16) {
+        if (is_bfloat16) {
             paddle::Tensor out = paddle::empty({all_tokens, M}, paddle::DataType::BFLOAT16, input.place());
             phi::dtype::bfloat16 *out_data = out.data<phi::dtype::bfloat16>();
             DisPatchW4AFp8Gemm(
                 reinterpret_cast<const cutlass::float_e4m3_t*>(input.data<phi::dtype::float8_e4m3fn>()),
                 reinterpret_cast<const cutlass::float_e4m3_t*>(weight.data<uint8_t>()),
-                tokens.data<int>(),
+                tokens.data<int64_t>(),
                 input_row_sum.data<float>(),
                 weight_scale.data<float>(),
                 reinterpret_cast<cutlass::bfloat16_t*>(out_data),
@@ -122,13 +139,13 @@ std::vector<paddle::Tensor> W4AFp8Gemm(
             PD_THROW("Only supported dtype in ['BFLOAT16'].");
         }
     } else {
-        if (is_bflot16) {
+        if (is_bfloat16) {
             paddle::Tensor out = paddle::empty({batch_size, token_padding_size, M}, paddle::DataType::BFLOAT16, input.place());
             phi::dtype::bfloat16 * out_data = out.data<phi::dtype::bfloat16>();
             DisPatchW4AFp8Gemm(
                 reinterpret_cast<const cutlass::float_e4m3_t*>(input.data<phi::dtype::float8_e4m3fn>()),
                 reinterpret_cast<const cutlass::float_e4m3_t*>(weight.data<uint8_t>()),
-                tokens.data<int>(),
+                tokens.data<int64_t>(),
                 input_row_sum.data<float>(),
                 weight_scale.data<float>(),
                 reinterpret_cast<cutlass::bfloat16_t*>(out_data),
@@ -145,6 +162,38 @@ std::vector<paddle::Tensor> W4AFp8Gemm(
     }
 }
 
+template <typename InputType, typename OutputType>
+void DisPatchW4AFp8GemmWrapper(
+        const InputType* input,
+        const InputType* weight,
+        const int64_t* total_rows_before_expert,
+        const float* input_row_sum,
+        const float* row_scale,
+        const float* weight_scale,
+        OutputType * out,
+        const int64_t token_padding_size,
+        const int64_t max_tokens,
+        const int num_experts,
+        const int64_t M,
+        const int64_t K,
+        cudaStream_t stream) {
+    using InType = typename NVTraits<InputType>::data_t;
+    using OutType = typename NVTraits<OutputType>::data_t;
+    DisPatchW4AFp8Gemm(
+        reinterpret_cast<const InType*>(input),
+        reinterpret_cast<const InType*>(weight),
+        total_rows_before_expert,
+        input_row_sum,
+        weight_scale,
+        reinterpret_cast<OutType*>(out),
+        token_padding_size,
+        max_tokens,
+        num_experts,
+        M,
+        K,
+        stream);
+}
+
 
 std::vector<paddle::Tensor> W4AFp8GemmWeightConvert(const paddle::Tensor& weight) {
     const int batch_size = weight.dims()[0];
@@ -155,6 +204,63 @@ std::vector<paddle::Tensor> W4AFp8GemmWeightConvert(const paddle::Tensor& weight
     return {weight_new};
 }
 
+template <typename T, int kPackSize>
+__global__ void permute_scale_kernel(
+        T* input_data,
+        const int numel) {
+    using LoadT = AlignedVector<T, kPackSize>;
+    LoadT input_vec;
+    LoadT dst_vec;
+    const int load_idx = (blockIdx.x * blockDim.x + threadIdx.x) * kPackSize;
+    if (load_idx >= numel) {
+        return;
+    }
+    Load<T, kPackSize>(&input_data[load_idx], &input_vec);
+
+    for (int i = 0; i < kPackSize; i+=2) {
+        dst_vec[i] = input_vec[i / 2];
+        dst_vec[i + 1] = input_vec[i / 2 + 8];
+    }
+
+    Store<T, kPackSize>(dst_vec, &input_data[load_idx]);
+}
+
+void W4AFp8GemmScalePermute(const paddle::Tensor& scale) {
+    const int row = scale.dims()[0];
+    const int col = scale.dims()[1];
+    if (col % 16 != 0) {
+        PD_THROW("Only supported when col is divisible by 16.");
+    }
+    const int numel = row * col;
+    const int threads = 128;
+    const int kPackSize = 16;
+    const int grid_size = (numel / kPackSize + threads - 1) / threads;
+
+    if (scale.dtype() == paddle::DataType::BFLOAT16) {
+        permute_scale_kernel<phi::dtype::bfloat16, kPackSize><<<grid_size, threads, 0, scale.stream()>>>(
+            const_cast<phi::dtype::bfloat16*>(scale.data<phi::dtype::bfloat16>()),
+            numel
+        );
+    } else if (scale.dtype() == paddle::DataType::FLOAT16) {
+        permute_scale_kernel<phi::dtype::float16, kPackSize><<<grid_size, threads, 0, scale.stream()>>>(
+            const_cast<phi::dtype::float16*>(scale.data<phi::dtype::float16>()),
+            numel
+        );
+    } else if (scale.dtype() == paddle::DataType::FLOAT32) {
+        permute_scale_kernel<float, kPackSize><<<grid_size, threads, 0, scale.stream()>>>(
+            const_cast<float*>(scale.data<float>()),
+            numel
+        );
+    }
+
+}
+
+PD_BUILD_STATIC_OP(w4afp8_gemm_scale_permute)
+    .Inputs({"weight_scale"})
+    .Outputs({"permute_scale"})
+    .SetInplaceMap({{"weight_scale", "permute_scale"}})
+    .SetKernelFn(PD_KERNEL(W4AFp8GemmScalePermute));
+
 PD_BUILD_STATIC_OP(w4afp8_gemm)
     .Inputs({"input",
              "weight",
@@ -162,12 +268,44 @@ PD_BUILD_STATIC_OP(w4afp8_gemm)
              "input_row_sum",
              "weight_scale"})
     .Outputs({"out"})
-    .Attrs({"token_padding_size: int",
+    .Attrs({"token_padding_size: int64_t",
-            "max_tokens: int",
+            "max_tokens: int64_t",
-            "is_bflot16: bool"})
+            "is_bfloat16: bool"})
     .SetKernelFn(PD_KERNEL(W4AFp8Gemm));
 
 PD_BUILD_STATIC_OP(w4afp8_gemm_weight_convert)
     .Inputs({"weight"})
     .Outputs({"converted_weight"})
     .SetKernelFn(PD_KERNEL(W4AFp8GemmWeightConvert));
+
+template void DisPatchW4AFp8GemmWrapper<__nv_fp8_e4m3, __nv_bfloat16>(
+        const __nv_fp8_e4m3* input,
+        const __nv_fp8_e4m3* weight,
+        const int64_t * tokens,
+        const float * input_row_sum,
+        const float * row_scale,
+        const float * weight_scale,
+        __nv_bfloat16 * out,
+        const int64_t token_padding_size,
+        const int64_t max_tokens,
+        const int num_experts,
+        const int64_t M,
+        const int64_t K,
+        cudaStream_t stream
+);
+
+template void DisPatchW4AFp8GemmWrapper<__nv_fp8_e4m3, half>(
+        const __nv_fp8_e4m3* input,
+        const __nv_fp8_e4m3* weight,
+        const int64_t * tokens,
+        const float * input_row_sum,
+        const float * row_scale,
+        const float * weight_scale,
+        half * out,
+        const int64_t token_padding_size,
+        const int64_t max_tokens,
+        const int num_experts,
+        const int64_t M,
+        const int64_t K,
+        cudaStream_t stream
+);

custom_ops/gpu_ops/w4afp8_gemm/w4afp8_gemm.h

@@ -0,0 +1,47 @@
+// Copyright (c) 2022 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.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "helper.h"
+
+
+
+std::vector<paddle::Tensor> W4AFp8Gemm(
+        const paddle::Tensor& input,
+        const paddle::Tensor& weight,
+        const paddle::Tensor& tokens, // If tokenpadding=0, this tensor represents the prefix sum of tensors, otherwise it represents the number of tokens in each group
+        const paddle::Tensor& input_row_sum,
+        const paddle::Tensor& weight_scale,
+        const int64_t token_padding_size,
+        const int64_t max_tokens,
+        const bool is_bfloat16);
+
+template <typename InputType, typename OutputType>
+void DisPatchW4AFp8GemmWrapper(
+        const InputType* input,
+        const InputType* weight,
+        const int64_t * tokens,
+        const float * input_row_sum,
+        const float * row_scale,
+        const float * weight_scale,
+        OutputType * out,
+        const int64_t token_padding_size,
+        const int64_t max_tokens,
+        const int num_experts,
+        const int64_t M,
+        const int64_t K,
+        cudaStream_t stream);

custom_ops/gpu_ops/w4afp8_gemm/w4afp8_gemm_kernel.hpp

@@ -27,7 +27,7 @@
 #include "mainloop_fwd.h"
 
 template <typename Ktraits>
-void  __global__ __launch_bounds__(Ktraits::kNWarps * cutlass::NumThreadsPerWarp, 1) w4afp8_geem_kernel(
+void  __global__ __launch_bounds__(Ktraits::kNWarps * cutlass::NumThreadsPerWarp, 1) w4afp8_gemm_kernel(
         CUTE_GRID_CONSTANT typename CollectiveMainloopFwd<Ktraits>::Params const mainloop_params) {
 
     using Element = typename Ktraits::Element;
@@ -87,9 +87,9 @@ void  __global__ __launch_bounds__(Ktraits::kNWarps * cutlass::NumThreadsPerWarp
         __syncthreads();
     }
 
-    const int pre_fix_tokens = TokenPackSize == 0 ? mainloop_params.tokens[bidb] : 0;
+    const int pre_fix_tokens = TokenPackSize == 0 ? (bidb == 0 ? 0 : mainloop_params.tokens[bidb - 1]) : 0;
 
-    const int tokens = TokenPackSize == 0 ? mainloop_params.tokens[bidb + 1] - pre_fix_tokens : mainloop_params.tokens[bidb];
+    const int tokens = TokenPackSize == 0 ? mainloop_params.tokens[bidb] - pre_fix_tokens : mainloop_params.tokens[bidb];
 
 
     if (bidn * kBlockN >= tokens) {
@@ -207,7 +207,7 @@ auto get_gmem_layout(const int Rows, const int Cols) {
 
 template <typename InputType, typename OutputType, typename Kernel_traits, int M, int K, int Batch, int TokenPackSize>
 void run_gemm(const InputType * A, const InputType * B, OutputType * C, const float *weight_scale,
-        const float *input_row_sum, const int * tokens, const int max_tokens, cudaStream_t stream) {
+        const float *input_row_sum, const int64_t * tokens, const int64_t max_tokens, cudaStream_t stream) {
 
     using ElementOutput = typename Kernel_traits::ElementOutput;
     using Element = typename Kernel_traits::Element;
@@ -231,7 +231,7 @@ void run_gemm(const InputType * A, const InputType * B, OutputType * C, const fl
         });
 
     void *kernel;
-    kernel = (void *)w4afp8_geem_kernel<Kernel_traits>;
+    kernel = (void *)w4afp8_gemm_kernel<Kernel_traits>;
 
     int smem_size = sizeof(typename Kernel_traits::SharedStorage) + sizeof(float) * Kernel_traits::kBlockN;
 

custom_ops/utils/auto_gen_w4afp8_gemm_kernel.py

@@ -36,8 +36,8 @@ void w4afp8_gemm_M{M}_N{N}_TAILN{TAILN}_K{K}_B{BATCH}_P{PADDING}_{TYPE}(
     {cutlass_type} * out,
     const float *weight_scale,
     const float *input_row_sum,
-    const int *tokens,
+    const int64_t *tokens,
-    const int max_tokens,
+    const int64_t max_tokens,
     cudaStream_t stream);
 """
 
@@ -54,8 +54,8 @@ void w4afp8_gemm_M{M}_N{N}_TAILN{TAILN}_K{K}_B{BATCH}_P{PADDING}_{TYPE}(
         {cutlass_type} * out,
         const float *weight_scale,
         const float *input_row_sum,
-        const int *tokens,
+        const int64_t *tokens,
-        const int max_tokens,
+        const int64_t max_tokens,
         cudaStream_t stream) {{
 
     constexpr static int M = {M};

fastdeploy/model_executor/layers/moe/__init__.py

@@ -12,13 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .fused_moe_cutlass_backend import CutlassW4A8MoEMethod, CutlassWeightOnlyMoEMethod
+from .fused_moe_cutlass_backend import (
+    CutlassW4A8MoEMethod,
+    CutlassW4AFP8MoEMethod,
+    CutlassWeightOnlyMoEMethod,
+)
 from .fused_moe_triton_backend import TritonWeightOnlyMoEMethod
 from .moe import FusedMoE
 
 __all__ = [
     CutlassWeightOnlyMoEMethod,
     CutlassW4A8MoEMethod,
+    CutlassW4AFP8MoEMethod,
     FusedMoE,
     TritonWeightOnlyMoEMethod,
 ]

fastdeploy/model_executor/layers/moe/ep.py

@@ -389,7 +389,7 @@ class EPPrefillRunner(EPRunner):
     ):
         (
             num_tokens_per_rank,
-            _,
+            num_tokens_per_rdma_rank,
             num_tokens_per_expert,
             is_token_in_rank,
             _,
@@ -399,6 +399,7 @@ class EPPrefillRunner(EPRunner):
         dispatch_args = {
             "x": (x, x_scale_tensor) if x_scale_tensor is not None else x,
             "num_tokens_per_rank": num_tokens_per_rank,
+            "num_tokens_per_rdma_rank": num_tokens_per_rdma_rank,
             "is_token_in_rank": is_token_in_rank,
             "num_tokens_per_expert": num_tokens_per_expert,
             "config": self.ep_engine.ep_config,

fastdeploy/model_executor/layers/moe/fused_moe_cutlass_backend.py

@@ -31,6 +31,7 @@ if current_platform.is_cuda():
         moe_expert_dispatch,
         moe_expert_reduce,
         noaux_tc,
+        w4afp8_gemm_scale_permute,
     )
 elif current_platform.is_iluvatar():
     from fastdeploy.model_executor.ops.iluvatar import (
@@ -87,6 +88,7 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
         token_nums_per_expert: paddle.Tensor,
         expert_idx_per_token: paddle.Tensor,
         used_in_ep_low_latency: bool = False,
+        estimate_total_token_nums: int = -1,
     ):
         """
         Paddle Cutlass compute Fused MoE.
@@ -104,6 +106,7 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
                 expert_idx_per_token,
                 self.moe_quant_type,
                 used_in_ep_low_latency,
+                estimate_total_token_nums,
             )
         return fastdeploy.model_executor.ops.gpu.moe_expert_ffn(
             permute_input,
@@ -117,6 +120,7 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
             expert_idx_per_token,
             self.moe_quant_type,
             used_in_ep_low_latency,
+            estimate_total_token_nums,
         )
 
     def apply_ep_prefill(
@@ -157,13 +161,13 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
                 recv_x,
                 recv_topk_idx,
                 recv_topk_weights,
-                (self.up_gate_proj_in_scale if hasattr(self, "up_gate_proj_in_scale") else None),
+                (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 self.moe_quant_type != "w4a8":
+            if self.moe_quant_type != "w4a8" and self.moe_quant_type != "w4afp8":
-                # only w4a8 need expert_idx_per_token
+                # only w4a8 and w4afp8 need expert_idx_per_token
                 # Other need not this tensor, so we make it None.
                 expert_idx_per_token = None
             else:
@@ -202,18 +206,19 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
         Apply the EP decoder method.
         """
         gate_out = gate(x.cast("float32"))
+        estimate_total_token_nums = gate_out.shape[0] * layer.top_k
         # 1. Select topk experts and weights
         topk_idx, topk_weights = self.ep_decoder_runner.moe_select(layer, gate_out)
         expertwise_scale = None
         if hasattr(layer, "up_gate_proj_in_scale_all_experts"):  # only use in w4a8
             expertwise_scale = getattr(layer, "up_gate_proj_in_scale_all_experts", None)
-
+        use_fp8 = self.moe_quant_type == "w4afp8"
         # 2. EP Dispatch
         permute_input, token_nums_per_expert, handle = self.ep_decoder_runner.dispatch(
-            x, topk_idx, topk_weights, expertwise_scale=expertwise_scale
+            x, topk_idx, topk_weights, expertwise_scale=expertwise_scale, use_fp8=use_fp8
         )
         # 3. Compute ffn
-        if self.moe_quant_type == "w4a8":
+        if self.moe_quant_type == "w4a8" or self.moe_quant_type == "w4afp8":
             num_local_experts, max_num, _ = permute_input.shape
             expert_idx_per_token = paddle.arange(num_local_experts)[:, None].tile([1, max_num])
         elif self.moe_quant_type in ["weight_only_int8", "weight_only_int4"]:
@@ -227,6 +232,7 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
             token_nums_per_expert.cast("int64"),
             expert_idx_per_token,
             True,
+            estimate_total_token_nums,
         )
 
         # 4. EP combine
@@ -290,7 +296,7 @@ class CutlassMoEMethod(UnquantizedFusedMoEMethod):
                 topk_only_mode=False,
             )
 
-        if self.moe_quant_type != "w4a8":
+        if self.moe_quant_type != "w4a8" and self.moe_quant_type != "w4afp8":
             # only w4a8 need expert_idx_per_token
             # Other need not this tensor, so we make it None.
             expert_idx_per_token = None
@@ -373,9 +379,9 @@ class CutlassW4A8MoEMethod(CutlassMoEMethod):
         down_proj_weight = paddle.stack(down_proj_weights, axis=0)
         up_gate_proj_weight_scale = paddle.stack(up_gate_proj_weight_scale, axis=0).cast(paddle.get_default_dtype())
         down_proj_weight_scale = paddle.stack(down_proj_weight_scale, axis=0).cast(paddle.get_default_dtype())
-        up_gate_proj_in_scale_all_experts = paddle.stack(up_gate_proj_in_scale_all_experts, axis=0)
+        up_gate_proj_in_scale_all_experts = paddle.stack(up_gate_proj_in_scale_all_experts, axis=0).unsqueeze()
-        up_gate_proj_in_scale = paddle.stack(up_gate_proj_in_scale, axis=0)
+        up_gate_proj_in_scale = paddle.stack(up_gate_proj_in_scale, axis=0).unsqueeze()
-        down_proj_in_scale = paddle.stack(down_proj_in_scale, axis=0)
+        down_proj_in_scale = paddle.stack(down_proj_in_scale, axis=0).unsqueeze()
 
         name_tensor_map = {
             "up_gate_proj_weight": up_gate_proj_weight,
@@ -448,7 +454,6 @@ class CutlassW4A8MoEMethod(CutlassMoEMethod):
         Args:
             layer (nn.Layer): The layer to add parameters to.
             weight_key_map (dict): The weight key map.
-            state_dict (dict): The state dict.
         """
         self.default_dtype = layer._helper.get_default_dtype()
         if layer.ep_size > 1:
@@ -572,6 +577,263 @@ class CutlassW4A8MoEMethod(CutlassMoEMethod):
             )
 
 
+class CutlassW4AFP8MoEMethod(CutlassMoEMethod):
+    """
+    w4a8 MoE Method
+    """
+
+    def __init__(self, quant_config):
+        super().__init__(quant_config)
+        self.quant_config = quant_config
+        self.moe_quant_type = "w4afp8"
+        self.pack_num = 2
+
+    def process_prequanted_weights(self, layer: nn.Layer, state_dict):
+        """
+        Paddle cutlass process prequanted weights.
+        """
+        up_gate_proj_expert_weight_key = layer.weight_key_map.get("up_gate_proj_expert_weight_key", None)
+        down_proj_expert_weight_key = layer.weight_key_map.get("down_proj_expert_weight_key", None)
+        up_gate_proj_expert_weight_scale_key = layer.weight_key_map.get("up_gate_proj_expert_weight_scale_key", None)
+        down_proj_expert_weight_scale_key = layer.weight_key_map.get("down_proj_expert_weight_scale_key", None)
+        up_gate_proj_expert_in_scale_key = layer.weight_key_map.get("up_gate_proj_expert_in_scale_key", None)
+        down_proj_expert_in_scale_key = layer.weight_key_map.get("down_proj_expert_in_scale_key", None)
+
+        up_gate_proj_weights, down_proj_weights, logical_expert_ids, ep_rank_to_expert_id_list = (
+            layer.load_experts_weight(
+                state_dict,
+                up_gate_proj_expert_weight_key,
+                down_proj_expert_weight_key,
+            )
+        )
+
+        up_gate_proj_weight_scale = []
+        down_proj_weight_scale = []
+        up_gate_proj_in_scale_all_experts = []
+        up_gate_proj_in_scale = []
+        down_proj_in_scale = []
+
+        if layer.ep_size > 1:
+            for expert_idx in ep_rank_to_expert_id_list:
+                scale_tensor = get_tensor(state_dict[up_gate_proj_expert_in_scale_key.format(expert_idx)])
+                up_gate_proj_in_scale_all_experts.append(scale_tensor)
+
+        for expert_idx in logical_expert_ids:
+            up_gate_proj_weight_scale.append(
+                get_tensor(state_dict.pop(up_gate_proj_expert_weight_scale_key.format(expert_idx)))
+            )
+            down_proj_weight_scale.append(
+                get_tensor(state_dict.pop(down_proj_expert_weight_scale_key.format(expert_idx)))
+            )
+            up_gate_proj_in_scale.append(
+                get_tensor(state_dict.pop(up_gate_proj_expert_in_scale_key.format(expert_idx)))
+            )
+            down_proj_in_scale.append(get_tensor(state_dict.pop(down_proj_expert_in_scale_key.format(expert_idx))))
+
+        up_gate_proj_weight = paddle.stack(up_gate_proj_weights, axis=0)
+        down_proj_weight = paddle.stack(down_proj_weights, axis=0)
+        up_gate_proj_weight_scale = paddle.stack(up_gate_proj_weight_scale, axis=0)
+        down_proj_weight_scale = paddle.stack(down_proj_weight_scale, axis=0)
+        up_gate_proj_in_scale_all_experts = paddle.stack(up_gate_proj_in_scale_all_experts, axis=0).squeeze()
+        up_gate_proj_in_scale = paddle.stack(up_gate_proj_in_scale, axis=0).squeeze()
+        down_proj_in_scale = paddle.stack(down_proj_in_scale, axis=0).squeeze()
+
+        name_tensor_map = {
+            "up_gate_proj_weight": up_gate_proj_weight,
+            "down_proj_weight": down_proj_weight,
+            "up_gate_proj_weight_scale": up_gate_proj_weight_scale,
+            "down_proj_weight_scale": down_proj_weight_scale,
+            "up_gate_proj_in_scale_all_experts": up_gate_proj_in_scale_all_experts,
+            "up_gate_proj_in_scale": up_gate_proj_in_scale,
+            "down_proj_in_scale": down_proj_in_scale,
+        }
+        for name, tensor in name_tensor_map.items():
+            getattr(layer, name).set_value(tensor)
+
+    def create_weights(self, layer: nn.Layer, **extra_weight_attrs):
+        """
+        Paddle cutlass create weight process.
+        """
+        self.weight_dtype = "int8"
+        self.ffn1_weight_shape = [
+            layer.num_local_experts,
+            layer.hidden_size // 2,
+            layer.moe_intermediate_size * 2,
+        ]
+        self.ffn2_weight_shape = [
+            layer.num_local_experts,
+            layer.moe_intermediate_size // 2,
+            layer.hidden_size,
+        ]
+        setattr(
+            layer,
+            self.added_weight_attrs[0],
+            layer.create_parameter(
+                shape=self.ffn1_weight_shape,
+                dtype=self.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        setattr(
+            layer,
+            self.added_weight_attrs[1],
+            layer.create_parameter(
+                shape=self.ffn2_weight_shape,
+                dtype=self.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+
+        self.create_w4afp8_scale_weights(layer, layer.weight_key_map)
+
+    def process_loaded_weights(self, layer: nn.Layer, state_dict):
+        """
+        Paddle cutlass load 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)
+        for idx, weight_tensor in enumerate([up_gate_proj_weights, down_proj_weights]):
+            weight_name = self.added_weight_attrs[idx]
+            weight_list = []
+            for i in range(layer.num_local_experts):
+                quant_weight, scale = weight_quantize(weight_tensor[i], algo=self.moe_quant_type, arch=80)
+                weight_list.append(quant_weight)
+            quanted_weight = paddle.stack(weight_list, axis=0)
+            getattr(layer, weight_name).set_value(quanted_weight)
+
+        self.load_w4afp8_scale_weights(layer, layer.weight_key_map, state_dict)
+
+    def create_w4afp8_scale_weights(self, layer: nn.Layer, weight_key_map: dict):
+        """
+        Get w4afp8 weights from state dict and process them.
+        Args:
+            layer (nn.Layer): The layer to add parameters to.
+            weight_key_map (dict): The weight key map.
+        """
+
+        self.default_dtype = layer._helper.get_default_dtype()
+        if layer.ep_size > 1:
+            setattr(
+                layer,
+                "up_gate_proj_in_scale_all_experts",
+                layer.create_parameter(
+                    shape=[layer.num_experts],
+                    dtype="float32",
+                    default_initializer=paddle.nn.initializer.Constant(0),
+                ),
+            )
+
+        # in_scales
+        for in_scale_name in ["up_gate_proj_in_scale", "down_proj_in_scale"]:
+            setattr(
+                layer,
+                in_scale_name,
+                layer.create_parameter(
+                    shape=[layer.num_local_experts],
+                    dtype="float32",
+                    default_initializer=paddle.nn.initializer.Constant(0),
+                ),
+            )
+
+        # weight_scales
+        setattr(
+            layer,
+            "up_gate_proj_weight_scale",
+            layer.create_parameter(
+                shape=[layer.num_local_experts, layer.moe_intermediate_size * 2],
+                dtype="float32",
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        setattr(
+            layer,
+            "down_proj_weight_scale",
+            layer.create_parameter(
+                shape=[layer.num_local_experts, layer.hidden_size],
+                dtype="float32",
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+
+    def load_w4afp8_scale_weights(self, layer: nn.Layer, weight_key_map: dict, state_dict: dict):
+        """
+        Get w4afp8 weights from state dict and process them.
+        Args:
+            layer (nn.Layer): The layer to add parameters to.
+            weight_key_map (dict): The weight key map.
+            state_dict (dict): The state dict.
+        """
+
+        def _extract_scale_tensor(state_dict, key_template, expert_idx):
+            return get_tensor(state_dict.pop(key_template.format(expert_idx)))
+
+        def _process_in_scale(name: str, in_scales: list[paddle.Tensor]):
+            processed_in_scale = 1 / paddle.concat(in_scales)
+            getattr(layer, name).set_value(processed_in_scale)
+            return processed_in_scale
+
+        def _permute_weight_scale(weight_scale: paddle.Tensor):
+            weight_scale = w4afp8_gemm_scale_permute(weight_scale)
+            return weight_scale
+
+        def _process_weight_scale(name: str, weight_scales: list[paddle.Tensor], processed_in_scale: paddle.Tensor):
+            processed_weight_scale = (
+                paddle.stack(weight_scales, axis=0) / (448 * 7 * 2 ** (-9)) / processed_in_scale[:, None]
+            )
+            processed_weight_scale = _permute_weight_scale(processed_weight_scale)
+            getattr(layer, name).set_value(processed_weight_scale)
+
+        # 1. Init scale containers and maps
+        up_gate_proj_weight_scales = []
+        down_proj_weight_scales = []
+        up_gate_proj_in_scales_all_experts = []
+        up_gate_proj_in_scales = []
+        down_proj_in_scales = []
+
+        scale_weight_map = {
+            "up_gate_proj_weight_scale": up_gate_proj_weight_scales,
+            "down_proj_weight_scale": down_proj_weight_scales,
+            "up_gate_proj_in_scale": up_gate_proj_in_scales,
+            "down_proj_in_scale": down_proj_in_scales,
+        }
+        scale_key_map = {
+            "up_gate_proj_weight_scale": weight_key_map.get("up_gate_proj_expert_weight_scale_key", None),
+            "down_proj_weight_scale": weight_key_map.get("down_proj_expert_weight_scale_key", None),
+            "up_gate_proj_in_scale": weight_key_map.get("up_gate_proj_expert_in_scale_key", None),
+            "down_proj_in_scale": weight_key_map.get("down_proj_expert_in_scale_key", None),
+        }
+        for name, value in scale_key_map.items():
+            if value is None:
+                raise ValueError(f"scale {name} should not be none in w4a8 mode.")
+
+        # 2. Extract scale tensor from state dict
+        if layer.ep_size > 1:
+            for expert_idx in range(layer.num_experts):
+                scale_tensor = get_tensor(state_dict[scale_key_map["up_gate_proj_in_scale"].format(expert_idx)])
+                up_gate_proj_in_scales_all_experts.append(1 / scale_tensor)
+            getattr(layer, "up_gate_proj_in_scale_all_experts").set_value(
+                paddle.concat(up_gate_proj_in_scales_all_experts)
+            )
+
+        for local_expert_idx in range(layer.num_local_experts):
+            expert_idx = local_expert_idx + layer.expert_id_offset
+            for name, scale_key_template in scale_key_map.items():
+                scale_tensor = _extract_scale_tensor(state_dict, scale_key_template, expert_idx)
+                scale_weight_map[name].append(scale_tensor)
+
+        # 3. Process scale tensor and set to layer
+        in_scales = []
+        for in_scale_name in ["up_gate_proj_in_scale", "down_proj_in_scale"]:
+            in_scales.append(_process_in_scale(in_scale_name, scale_weight_map[in_scale_name]))
+
+        for i, weight_scale_name in enumerate(["up_gate_proj_weight_scale", "down_proj_weight_scale"]):
+            _process_weight_scale(
+                weight_scale_name,
+                scale_weight_map[weight_scale_name],
+                in_scales[i],
+            )
+
+
 class CutlassWeightOnlyMoEMethod(CutlassMoEMethod):
     """
     weight only for moe

fastdeploy/model_executor/layers/quantization/w4afp8.py

@@ -20,6 +20,7 @@ import paddle
 
 import fastdeploy
 
+from ..moe import FusedMoE
 from .quant_base import QuantConfigBase, QuantMethodBase
 
 QUANT_SCALING_FACTOR = 448
@@ -30,24 +31,32 @@ class W4AFP8Config(QuantConfigBase):
     quantization config for weight 4bits and activation fp8
     """
 
-    def __init__(self, weight_scale_dict, act_scale_dict) -> None:
+    def __init__(self, weight_scale_dict, act_scale_dict, is_permuted) -> None:
         super().__init__()
         self.weight_scale_dict = weight_scale_dict
         self.act_scale_dict = act_scale_dict
         self.quant_max_bound = 448
         self.quant_min_bound = -448
         self.quant_round_type = 1
+        self.is_permuted = is_permuted
 
     def name(self) -> str:
         return "w4afp8"
 
     @classmethod
     def from_config(cls, config: dict) -> "W4AFP8Config":
-        weight_scale_dict = config["weight_scale_dict"]
+        weight_scale_dict = config.get("weight_scale_dict", None)
-        act_scale_dict = config["act_scale_dict"]
+        act_scale_dict = config.get("act_scale_dict", None)
-        return cls(weight_scale_dict, act_scale_dict)
+        is_permuted = config.get("is_permuted", True)
+        return cls(weight_scale_dict, act_scale_dict, is_permuted)
 
     def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
+        if isinstance(layer, FusedMoE):
+            from fastdeploy.model_executor.layers.moe.fused_moe_cutlass_backend import (
+                CutlassW4AFP8MoEMethod,
+            )
+
+            return CutlassW4AFP8MoEMethod(self)
         return W4AFP8LinearMethod(self)
 
 

fastdeploy/model_executor/models/ernie4_5_moe.py

@@ -103,7 +103,7 @@ class Ernie4_5_MoE(nn.Layer):
         if hasattr(fd_config.quant_config, "moe_quant_type"):
             moe_quant_type = fd_config.quant_config.moe_quant_type
 
-        if moe_quant_type == "w4a8":
+        if moe_quant_type == "w4a8" or moe_quant_type == "w4afp8":
             weight_key_map = {
                 "gate_weight_key": f"{prefix}.gate.weight",
                 "gate_correction_bias_key": f"{prefix}.moe_statics.e_score_correction_bias",

tests/operators/test_w4afp8_gemm.py

@@ -31,7 +31,7 @@ def w4afp8_gemm_naive(input_bf16, weight_quant, tokens, weight_dequant_scale, BA
     return out
 
 
-def peruate_scale(weight_scale):
+def permute_scale(weight_scale):
     weight_scale = weight_scale.reshape([BATCH, N])
     temp = paddle.zeros([16])
     for b in range(BATCH):
@@ -52,10 +52,10 @@ TokenPadding = 0
 
 tokens = [tokens_per_group] * BATCH
 tokens_perfix_sum = np.cumsum(tokens)
-tokens_perfix_sum = np.insert(tokens_perfix_sum, 0, 0)
 
-tokens = paddle.to_tensor(tokens, dtype="int32")
+
-tokens_perfix_sum = paddle.to_tensor(tokens_perfix_sum, dtype="int32")
+tokens = paddle.to_tensor(tokens, dtype="int64")
+tokens_perfix_sum = paddle.to_tensor(tokens_perfix_sum, dtype="int64")
 
 all_tokens = int(tokens.sum())
 
@@ -72,7 +72,7 @@ input_row_sum = input_bf16.sum(axis=1) * -7 / 512
 max_tokens = int(tokens.max())
 
 out_naive = w4afp8_gemm_naive(input_bf16, weight_quant, tokens, weight_dequant_scale, BATCH, N)
-weight_dequant_scale = paddle.to_tensor(peruate_scale(weight_dequant_scale) * 512)
+weight_dequant_scale = paddle.to_tensor(permute_scale(weight_dequant_scale) * 512)
 
 weight_int4 = w4afp8_gemm_weight_convert(weight_quant.astype("uint8").cpu())