fix format (#583)

colossalai/kernel/cuda_native/csrc/moe_cuda_kernel.cu

-#include <torch/extension.h>
+#include "block_reduce.h"
+#include <cub/cub.cuh>
 #include <cuda.h>
 #include <cuda_fp16.h>
-#include <cub/cub.cuh>
-#include "block_reduce.h"
+#include <torch/extension.h>
 
-template<typename T, int block_size, int pack_size>
+template <typename T, int block_size, int pack_size>
 __device__ void moe_dpch_one_fwd(T *src_row, T *dst_row, const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
-    int tps = threadIdx.x * pack_size; T pack[pack_size];
+  int tps = threadIdx.x * pack_size;
+  T pack[pack_size];
   for (int idx = 0; idx + tps < cols; idx += bpack_size) {
     BlockLoad(ts_load).Load(src_row + idx, pack);
     BlockStore(ts_store).Store(dst_row + idx, pack);
   }
 }
 
-template<typename T, int block_size, int pack_size>
+template <typename T, int block_size, int pack_size>
 __device__ void moe_dpch_one_bwd(T *src_row, T *dst_row, const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
-    int tps = threadIdx.x * pack_size; T pack[pack_size];
+  int tps = threadIdx.x * pack_size;
+  T pack[pack_size];
   for (int idx = 0; idx + tps < cols; idx += bpack_size) {
     BlockLoad(ts_load).Load(dst_row + idx, pack);
     BlockStore(ts_store).Store(src_row + idx, pack);
   }
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_dpch_two_fwd(T *src_row, T *dst_row1, T *dst_row2, const int cols) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_dpch_two_fwd(T *src_row, T *dst_row1, T *dst_row2,
+                                 const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
-    int tps = threadIdx.x * pack_size; T pack[pack_size];
+  int tps = threadIdx.x * pack_size;
+  T pack[pack_size];
   for (int idx = 0; idx + tps < cols; idx += bpack_size) {
     BlockLoad(ts_load).Load(src_row + idx, pack);
     BlockStore(ts_store).Store(dst_row1 + idx, pack);
@@ -68,18 +72,19 @@ __device__ void moe_dpch_two_fwd(T *src_row, T *dst_row1, T *dst_row2, const int
   }
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_dpch_two_bwd(T *src_row, T *dst_row1, T *dst_row2, const int cols) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_dpch_two_bwd(T *src_row, T *dst_row1, T *dst_row2,
+                                 const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
   int tps = threadIdx.x * pack_size;
@@ -88,7 +93,7 @@ __device__ void moe_dpch_two_bwd(T *src_row, T *dst_row1, T *dst_row2, const int
     BlockLoad(ts_load).Load(dst_row1 + idx, pack1);
     BlockLoad(ts_load).Load(dst_row2 + idx, pack2);
 
-        #pragma unroll
+#pragma unroll
     for (int i = 0; i < pack_size; ++i) {
       pack1[i] += pack2[i];
     }
@@ -97,27 +102,27 @@ __device__ void moe_dpch_two_bwd(T *src_row, T *dst_row1, T *dst_row2, const int
   }
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_cb_one_fwd(
-    T *src_row, T *dst_row,
-    const T weight, const int cols) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_cb_one_fwd(T *src_row, T *dst_row, const T weight,
+                               const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
-    int tps = threadIdx.x * pack_size; T pack[pack_size];
+  int tps = threadIdx.x * pack_size;
+  T pack[pack_size];
   for (int idx = 0; idx + tps < cols; idx += bpack_size) {
     BlockLoad(ts_load).Load(src_row + idx, pack);
 
-        #pragma unroll
+#pragma unroll
     for (int i = 0; i < pack_size; ++i) {
       pack[i] *= weight;
     }
@@ -126,20 +131,19 @@ __device__ void moe_cb_one_fwd(
   }
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_cb_one_bwd(
-    T *src_row, T *dst_row, T *tks_row, T *weight_grad,
-    const T weight, const int cols) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_cb_one_bwd(T *src_row, T *dst_row, T *tks_row,
+                               T *weight_grad, const T weight, const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
   int tps = threadIdx.x * pack_size;
@@ -149,7 +153,7 @@ __device__ void moe_cb_one_bwd(
     BlockLoad(ts_load).Load(dst_row + idx, grad);
     BlockLoad(ts_load).Load(tks_row + idx, tokens);
 
-        #pragma unroll
+#pragma unroll
     for (int i = 0; i < pack_size; ++i) {
       thread_sum += grad[i] * tokens[i];
       grad[i] *= weight;
@@ -164,20 +168,20 @@ __device__ void moe_cb_one_bwd(
     *weight_grad = static_cast<T>(thread_sum);
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_cb_two_fwd(
-    T *src_row1, T *src_row2, T *dst_row,
-    const T weight1, const T weight2, const int cols) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_cb_two_fwd(T *src_row1, T *src_row2, T *dst_row,
+                               const T weight1, const T weight2,
+                               const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
   int tps = threadIdx.x * pack_size;
@@ -186,7 +190,7 @@ __device__ void moe_cb_two_fwd(
     BlockLoad(ts_load).Load(src_row1 + idx, pack1);
     BlockLoad(ts_load).Load(src_row2 + idx, pack2);
 
-        #pragma unroll
+#pragma unroll
     for (int i = 0; i < pack_size; ++i) {
       pack1[i] = pack1[i] * weight1 + pack2[i] * weight2;
     }
@@ -195,33 +199,33 @@ __device__ void moe_cb_two_fwd(
   }
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_cb_two_bwd(
-    T *src_row1, T *src_row2, T *dst_row,
-    T *tks_row1, T *tks_row2, T *weight_grad1, T *weight_grad2,
-    const T weight1, const T weight2, const int cols) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_cb_two_bwd(T *src_row1, T *src_row2, T *dst_row,
+                               T *tks_row1, T *tks_row2, T *weight_grad1,
+                               T *weight_grad2, const T weight1,
+                               const T weight2, const int cols) {
 
   assert(cols % pack_size == 0);
   const int bpack_size = block_size * pack_size;
 
-    typedef cub::BlockLoad<T, block_size, pack_size,
-        cub::BLOCK_LOAD_VECTORIZE> BlockLoad;
+  typedef cub::BlockLoad<T, block_size, pack_size, cub::BLOCK_LOAD_VECTORIZE>
+      BlockLoad;
   __shared__ typename BlockLoad::TempStorage ts_load;
 
-    typedef cub::BlockStore<T, block_size, pack_size,
-        cub::BLOCK_STORE_VECTORIZE> BlockStore;
+  typedef cub::BlockStore<T, block_size, pack_size, cub::BLOCK_STORE_VECTORIZE>
+      BlockStore;
   __shared__ typename BlockStore::TempStorage ts_store;
 
   int tps = threadIdx.x * pack_size;
-    T grad[pack_size], tokens1[pack_size], tokens2[pack_size],
-        sgrad1[pack_size], sgrad2[pack_size];
+  T grad[pack_size], tokens1[pack_size], tokens2[pack_size], sgrad1[pack_size],
+      sgrad2[pack_size];
   float thread_sum[2] = {0, 0};
   for (int idx = 0; idx + tps < cols; idx += bpack_size) {
     BlockLoad(ts_load).Load(dst_row + idx, grad);
     BlockLoad(ts_load).Load(tks_row1 + idx, tokens1);
     BlockLoad(ts_load).Load(tks_row2 + idx, tokens2);
 
-        #pragma unroll
+#pragma unroll
     for (int i = 0; i < pack_size; ++i) {
       thread_sum[0] += grad[i] * tokens1[i];
       thread_sum[1] += grad[i] * tokens2[i];
@@ -239,142 +243,125 @@ __device__ void moe_cb_two_bwd(
     *weight_grad1 = static_cast<T>(thread_sum[0]);
   else if (threadIdx.x == 1)
     *weight_grad2 = static_cast<T>(thread_sum[1]);
-
 }
 
 // DISPATCH KERNELS --------------------------------
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_dpch_fwd_selector(
-    T *src_row, T *dst_row1, T *dst_row2, const int cols,
-    const int indicator1, const int indicator2) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_dpch_fwd_selector(T *src_row, T *dst_row1, T *dst_row2,
+                                      const int cols, const int indicator1,
+                                      const int indicator2) {
 
   if (indicator1 != 0 && indicator2 != 0)
-        moe_dpch_two_fwd<T, block_size, pack_size>(
-            src_row, dst_row1, dst_row2, cols);
+    moe_dpch_two_fwd<T, block_size, pack_size>(src_row, dst_row1, dst_row2,
+                                               cols);
   else if (indicator1 != 0)
-        moe_dpch_one_fwd<T, block_size, pack_size>(
-            src_row, dst_row1, cols);
+    moe_dpch_one_fwd<T, block_size, pack_size>(src_row, dst_row1, cols);
   else if (indicator2 != 0)
-        moe_dpch_one_fwd<T, block_size, pack_size>(
-            src_row, dst_row2, cols);
+    moe_dpch_one_fwd<T, block_size, pack_size>(src_row, dst_row2, cols);
   else
     return;
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_dpch_bwd_selector(
-    T *src_row, T *dst_row1, T *dst_row2, const int cols,
-    const int indicator1, const int indicator2) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_dpch_bwd_selector(T *src_row, T *dst_row1, T *dst_row2,
+                                      const int cols, const int indicator1,
+                                      const int indicator2) {
 
   if (indicator1 != 0 && indicator2 != 0)
-        moe_dpch_two_bwd<T, block_size, pack_size>(
-            src_row, dst_row1, dst_row2, cols);
+    moe_dpch_two_bwd<T, block_size, pack_size>(src_row, dst_row1, dst_row2,
+                                               cols);
   else if (indicator1 != 0)
-        moe_dpch_one_bwd<T, block_size, pack_size>(
-            src_row, dst_row1, cols);
+    moe_dpch_one_bwd<T, block_size, pack_size>(src_row, dst_row1, cols);
   else if (indicator2 != 0)
-        moe_dpch_one_bwd<T, block_size, pack_size>(
-            src_row, dst_row2, cols);
+    moe_dpch_one_bwd<T, block_size, pack_size>(src_row, dst_row2, cols);
   else
     return;
 }
 
-template<typename T, int block_size, int pack_size>
-__global__ void moe_dpch_fwd_kernel(
-    T *batch_tokens, T *expert_input,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2, const int h) {
+template <typename T, int block_size, int pack_size>
+__global__ void moe_dpch_fwd_kernel(T *batch_tokens, T *expert_input,
+                                    int *mask1, int *mask2, int *dest1,
+                                    int *dest2, const int h) {
 
   int row = blockIdx.x;
   int indicator2 = mask2 == nullptr ? 0 : mask2[row];
   moe_dpch_fwd_selector<T, block_size, pack_size>(
-        batch_tokens + (row * h),
-        expert_input + (dest1[row] * h), expert_input + (dest2[row] * h),
-        h, mask1[row], indicator2);
+      batch_tokens + (row * h), expert_input + (dest1[row] * h),
+      expert_input + (dest2[row] * h), h, mask1[row], indicator2);
 }
 
-template<typename T, int block_size, int pack_size>
-__global__ void moe_dpch_bwd_kernel(
-    T *tokens_grad, T *expert_grad,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2, const int h) {
+template <typename T, int block_size, int pack_size>
+__global__ void moe_dpch_bwd_kernel(T *tokens_grad, T *expert_grad, int *mask1,
+                                    int *mask2, int *dest1, int *dest2,
+                                    const int h) {
 
   int row = blockIdx.x;
   int indicator2 = mask2 == nullptr ? 0 : mask2[row];
   moe_dpch_bwd_selector<T, block_size, pack_size>(
-        tokens_grad + (row * h),
-        expert_grad + (dest1[row] * h), expert_grad + (dest2[row] * h),
-        h, mask1[row], indicator2);
+      tokens_grad + (row * h), expert_grad + (dest1[row] * h),
+      expert_grad + (dest2[row] * h), h, mask1[row], indicator2);
 }
 
 // COMBINE KERNELS --------------------------------
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_cb_fwd_selector(
-    T *src_row1, T *src_row2, T *dst_row, const int cols,
-    const T weight1, const T weight2,
-    const int indicator1, const int indicator2) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_cb_fwd_selector(T *src_row1, T *src_row2, T *dst_row,
+                                    const int cols, const T weight1,
+                                    const T weight2, const int indicator1,
+                                    const int indicator2) {
 
   if (indicator1 != 0 && indicator2 != 0)
-        moe_cb_two_fwd<T, block_size, pack_size>(
-            src_row1, src_row2, dst_row, weight1, weight2, cols);
+    moe_cb_two_fwd<T, block_size, pack_size>(src_row1, src_row2, dst_row,
+                                             weight1, weight2, cols);
   else if (indicator1 != 0)
-        moe_cb_one_fwd<T, block_size, pack_size>(
-            src_row1, dst_row, weight1, cols);
+    moe_cb_one_fwd<T, block_size, pack_size>(src_row1, dst_row, weight1, cols);
   else if (indicator2 != 0)
-        moe_cb_one_fwd<T, block_size, pack_size>(
-            src_row2, dst_row, weight2, cols);
+    moe_cb_one_fwd<T, block_size, pack_size>(src_row2, dst_row, weight2, cols);
   else
     return;
 }
 
-template<typename T, int block_size, int pack_size>
-__device__ void moe_cb_bwd_selector(
-    T *src_row1, T *src_row2, T *dst_row, const int cols,
-    T *tks_row1, T *tks_row2, T *wt_grad1, T *wt_grad2,
-    const T weight1, const T weight2,
-    const int indicator1, const int indicator2) {
+template <typename T, int block_size, int pack_size>
+__device__ void moe_cb_bwd_selector(T *src_row1, T *src_row2, T *dst_row,
+                                    const int cols, T *tks_row1, T *tks_row2,
+                                    T *wt_grad1, T *wt_grad2, const T weight1,
+                                    const T weight2, const int indicator1,
+                                    const int indicator2) {
 
   if (indicator1 != 0 && indicator2 != 0)
-        moe_cb_two_bwd<T, block_size, pack_size>(
-            src_row1, src_row2, dst_row,
-            tks_row1, tks_row2, wt_grad1, wt_grad2,
-            weight1, weight2, cols);
+    moe_cb_two_bwd<T, block_size, pack_size>(src_row1, src_row2, dst_row,
+                                             tks_row1, tks_row2, wt_grad1,
+                                             wt_grad2, weight1, weight2, cols);
   else if (indicator1 != 0)
-        moe_cb_one_bwd<T, block_size, pack_size>(
-            src_row1, dst_row, tks_row1, wt_grad1, weight1, cols);
+    moe_cb_one_bwd<T, block_size, pack_size>(src_row1, dst_row, tks_row1,
+                                             wt_grad1, weight1, cols);
   else if (indicator2 != 0)
-        moe_cb_one_bwd<T, block_size, pack_size>(
-            src_row2, dst_row, tks_row2, wt_grad2, weight2, cols);
+    moe_cb_one_bwd<T, block_size, pack_size>(src_row2, dst_row, tks_row2,
+                                             wt_grad2, weight2, cols);
   else
     return;
 }
 
-
-template<typename T, int block_size, int pack_size>
-__global__ void moe_cb_fwd_kernel(
-    T *expert_tokens, T *combine_tokens, T *logits,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2,
-    const int e, const int c, const int h) {
+template <typename T, int block_size, int pack_size>
+__global__ void moe_cb_fwd_kernel(T *expert_tokens, T *combine_tokens,
+                                  T *logits, int *mask1, int *mask2, int *dest1,
+                                  int *dest2, const int e, const int c,
+                                  const int h) {
 
   int row = blockIdx.x, eid1 = dest1[row] / c, eid2 = dest2[row] / c;
   int indicator2 = mask2 == nullptr ? 0 : mask2[row];
   T *row_log = logits + (row * e);
   moe_cb_fwd_selector<T, block_size, pack_size>(
       expert_tokens + (dest1[row] * h), expert_tokens + (dest2[row] * h),
-        combine_tokens + (row * h), h,
-        row_log[eid1], row_log[eid2],
-        mask1[row], indicator2);
+      combine_tokens + (row * h), h, row_log[eid1], row_log[eid2], mask1[row],
+      indicator2);
 }
 
-template<typename T, int block_size, int pack_size>
-__global__ void moe_cb_bwd_kernel(
-    T *tokens_grad, T *expert_grad, T *tks,
-    T *logits, T *logits_grad,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2,
+template <typename T, int block_size, int pack_size>
+__global__ void moe_cb_bwd_kernel(T *tokens_grad, T *expert_grad, T *tks,
+                                  T *logits, T *logits_grad, int *mask1,
+                                  int *mask2, int *dest1, int *dest2,
                                   const int e, const int c, const int h) {
 
   int row = blockIdx.x, eid1 = dest1[row] / c, eid2 = dest2[row] / c;
@@ -382,36 +369,33 @@ __global__ void moe_cb_bwd_kernel(
   T *row_log = logits + (row * e), *row_grad = logits_grad + (row * e);
   moe_cb_bwd_selector<T, block_size, pack_size>(
       expert_grad + (dest1[row] * h), expert_grad + (dest2[row] * h),
-        tokens_grad + (row * h), h,
-        tks + (dest1[row] * h), tks + (dest2[row] * h),
-        row_grad + eid1, row_grad + eid2,
-        row_log[eid1], row_log[eid2],
-        mask1[row], indicator2);
+      tokens_grad + (row * h), h, tks + (dest1[row] * h),
+      tks + (dest2[row] * h), row_grad + eid1, row_grad + eid2, row_log[eid1],
+      row_log[eid2], mask1[row], indicator2);
 }
 
-//CUMSUM KERNEL --------------------------------
+// CUMSUM KERNEL --------------------------------
 
-template<int block_size, int pack_size>
-__global__ void cumsum_kernel(
-    int *inputs, int *outputs,
-    const int s, const int e) {
+template <int block_size, int pack_size>
+__global__ void cumsum_kernel(int *inputs, int *outputs, const int s,
+                              const int e) {
 
   assert(s % pack_size == 0);
   constexpr int bpack_size = block_size * pack_size;
-    int tid = threadIdx.x, bid = blockIdx.x,
-        tps = tid * pack_size, last_sum = -1;
-    __shared__ int temp[block_size + 1]; int pack[pack_size];
+  int tid = threadIdx.x, bid = blockIdx.x, tps = tid * pack_size, last_sum = -1;
+  __shared__ int temp[block_size + 1];
+  int pack[pack_size];
 
   for (int idx = 0; idx < s; idx += bpack_size) {
     int offset = 1;
 
     if (idx + tps < s) {
       temp[tid] = inputs[tps * e + bid];
-            #pragma unroll
+#pragma unroll
       for (int i = 1; i < pack_size; ++i) {
         pack[i] = inputs[(tps + i) * e + bid];
       }
-            #pragma unroll
+#pragma unroll
       for (int i = 1; i < pack_size; ++i) {
         temp[tid] += pack[i];
       }
@@ -435,8 +419,7 @@ __global__ void cumsum_kernel(
       offset >>= 1;
       __syncthreads();
       if (tid < i) {
-                int j = offset * (2 * tid + 1) - 1,
-                    k = j + offset, ts = temp[j];
+        int j = offset * (2 * tid + 1) - 1, k = j + offset, ts = temp[j];
         temp[j] = temp[k];
         temp[k] += ts;
       }
@@ -449,7 +432,7 @@ __global__ void cumsum_kernel(
 
     if (idx + tps < s) {
       temp[tid + 1] += last_sum;
-            #pragma unroll
+#pragma unroll
       for (int i = pack_size - 1; i > 0; --i) {
         outputs[(tps + i) * e + bid] = temp[tid + 1];
         temp[tid + 1] -= pack[i];
@@ -464,95 +447,103 @@ __global__ void cumsum_kernel(
   }
 }
 
-//LAUNCH FUNCTIONS --------------------------------
+// LAUNCH FUNCTIONS --------------------------------
 
-template<typename T>
-void moe_dpch_fwd_launch(
-    T *batch_tokens, T *expert_input,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2,
-    const int s, const int h) {
+template <typename T>
+void moe_dpch_fwd_launch(T *batch_tokens, T *expert_input, int *mask1,
+                         int *mask2, int *dest1, int *dest2, const int s,
+                         const int h) {
 
   if (h < 256)
-        moe_dpch_fwd_kernel<T, 32, 4><<<s, 32>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
+    moe_dpch_fwd_kernel<T, 32, 4>
+        <<<s, 32>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
   else if (h < 512)
-        moe_dpch_fwd_kernel<T, 32, 8><<<s, 32>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
+    moe_dpch_fwd_kernel<T, 32, 8>
+        <<<s, 32>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
   else if (h < 1024)
-        moe_dpch_fwd_kernel<T, 32, 16><<<s, 32>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
+    moe_dpch_fwd_kernel<T, 32, 16>
+        <<<s, 32>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
   else if (h < 2048)
-        moe_dpch_fwd_kernel<T, 64, 16><<<s, 64>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
+    moe_dpch_fwd_kernel<T, 64, 16>
+        <<<s, 64>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
   else
-        moe_dpch_fwd_kernel<T, 128, 16><<<s, 128>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
+    moe_dpch_fwd_kernel<T, 128, 16>
+        <<<s, 128>>>(batch_tokens, expert_input, mask1, mask2, dest1, dest2, h);
 }
 
-template<typename T>
-void moe_dpch_bwd_launch(
-    T *tokens_grad, T *expert_grad,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2,
-    const int s, const int h) {
+template <typename T>
+void moe_dpch_bwd_launch(T *tokens_grad, T *expert_grad, int *mask1, int *mask2,
+                         int *dest1, int *dest2, const int s, const int h) {
 
   if (h < 256)
-        moe_dpch_bwd_kernel<T, 32, 4><<<s, 32>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
+    moe_dpch_bwd_kernel<T, 32, 4>
+        <<<s, 32>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
   else if (h < 512)
-        moe_dpch_bwd_kernel<T, 32, 8><<<s, 32>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
+    moe_dpch_bwd_kernel<T, 32, 8>
+        <<<s, 32>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
   else if (h < 1024)
-        moe_dpch_bwd_kernel<T, 32, 16><<<s, 32>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
+    moe_dpch_bwd_kernel<T, 32, 16>
+        <<<s, 32>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
   else if (h < 2048)
-        moe_dpch_bwd_kernel<T, 64, 16><<<s, 64>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
+    moe_dpch_bwd_kernel<T, 64, 16>
+        <<<s, 64>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
   else
-        moe_dpch_bwd_kernel<T, 128, 16><<<s, 128>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
+    moe_dpch_bwd_kernel<T, 128, 16>
+        <<<s, 128>>>(tokens_grad, expert_grad, mask1, mask2, dest1, dest2, h);
 }
 
-template<typename T>
-void moe_cb_fwd_launch(
-    T *expert_tokens, T *combine_tokens, T *logits,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2,
+template <typename T>
+void moe_cb_fwd_launch(T *expert_tokens, T *combine_tokens, T *logits,
+                       int *mask1, int *mask2, int *dest1, int *dest2,
                        const int s, const int e, const int c, const int h) {
 
   if (h < 256)
-        moe_cb_fwd_kernel<T, 32, 4><<<s, 32>>>
-            (expert_tokens, combine_tokens, logits, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_fwd_kernel<T, 32, 4><<<s, 32>>>(expert_tokens, combine_tokens,
+                                           logits, mask1, mask2, dest1, dest2,
+                                           e, c, h);
   else if (h < 512)
-        moe_cb_fwd_kernel<T, 32, 8><<<s, 32>>>
-            (expert_tokens, combine_tokens, logits, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_fwd_kernel<T, 32, 8><<<s, 32>>>(expert_tokens, combine_tokens,
+                                           logits, mask1, mask2, dest1, dest2,
+                                           e, c, h);
   else if (h < 1024)
-        moe_cb_fwd_kernel<T, 32, 16><<<s, 32>>>
-            (expert_tokens, combine_tokens, logits, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_fwd_kernel<T, 32, 16><<<s, 32>>>(expert_tokens, combine_tokens,
+                                            logits, mask1, mask2, dest1, dest2,
+                                            e, c, h);
   else if (h < 2048)
-        moe_cb_fwd_kernel<T, 64, 16><<<s, 64>>>
-            (expert_tokens, combine_tokens, logits, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_fwd_kernel<T, 64, 16><<<s, 64>>>(expert_tokens, combine_tokens,
+                                            logits, mask1, mask2, dest1, dest2,
+                                            e, c, h);
   else
-        moe_cb_fwd_kernel<T, 128, 16><<<s, 128>>>
-            (expert_tokens, combine_tokens, logits, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_fwd_kernel<T, 128, 16><<<s, 128>>>(expert_tokens, combine_tokens,
+                                              logits, mask1, mask2, dest1,
+                                              dest2, e, c, h);
 }
 
-template<typename T>
-void moe_cb_bwd_launch(
-    T *tokens_grad, T *expert_grad, T *tks,
-    T *logits, T *logits_grad,
-    int *mask1, int *mask2,
-    int *dest1, int *dest2,
-    const int s, const int e, const int c, const int h) {
+template <typename T>
+void moe_cb_bwd_launch(T *tokens_grad, T *expert_grad, T *tks, T *logits,
+                       T *logits_grad, int *mask1, int *mask2, int *dest1,
+                       int *dest2, const int s, const int e, const int c,
+                       const int h) {
 
   if (h < 256)
-        moe_cb_bwd_kernel<T, 32, 4><<<s, 32>>>
-            (tokens_grad, expert_grad, tks, logits, logits_grad, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_bwd_kernel<T, 32, 4><<<s, 32>>>(tokens_grad, expert_grad, tks,
+                                           logits, logits_grad, mask1, mask2,
+                                           dest1, dest2, e, c, h);
   else // if (h < 512)
-        moe_cb_bwd_kernel<T, 64, 4><<<s, 64>>>
-            (tokens_grad, expert_grad, tks, logits, logits_grad, mask1, mask2, dest1, dest2, e, c, h);
+    moe_cb_bwd_kernel<T, 64, 4><<<s, 64>>>(tokens_grad, expert_grad, tks,
+                                           logits, logits_grad, mask1, mask2,
+                                           dest1, dest2, e, c, h);
   // else if (h < 1024)
   //     moe_cb_bwd_kernel<T, 128, 4><<<s, 128>>>
-    //         (tokens_grad, expert_grad, tks, logits, logits_grad, mask1, mask2, dest1, dest2, e, c, h);
+  //         (tokens_grad, expert_grad, tks, logits, logits_grad, mask1, mask2,
+  //         dest1, dest2, e, c, h);
   // else
   //     moe_cb_bwd_kernel<T, 256, 4><<<s, 256>>>
-    //         (tokens_grad, expert_grad, tks, logits, logits_grad, mask1, mask2, dest1, dest2, e, c, h);
+  //         (tokens_grad, expert_grad, tks, logits, logits_grad, mask1, mask2,
+  //         dest1, dest2, e, c, h);
 }
 
-void cumsum_launch(
-    int *inputs, int *outputs,
-    const int s, const int e) {
+void cumsum_launch(int *inputs, int *outputs, const int s, const int e) {
 
   if (s <= 256)
     cumsum_kernel<256, 1><<<e, 256>>>(inputs, outputs, s, e);
@@ -569,32 +560,29 @@ void cumsum_launch(
 // API FUNCTIONS --------------------------------
 
 #define DISPATCH_FLOAT_AND_HALF(TYPE, NAME, ...)                               \
-    switch (TYPE)                                                       \
-    {                                                                   \
-    case at::ScalarType::Float:                                         \
-    {                                                                   \
+  switch (TYPE) {                                                              \
+  case at::ScalarType::Float: {                                                \
     using scalar_t = float;                                                    \
     __VA_ARGS__;                                                               \
     break;                                                                     \
   }                                                                            \
-    case at::ScalarType::Half:                                          \
-    {                                                                   \
+  case at::ScalarType::Half: {                                                 \
     using scalar_t = at::Half;                                                 \
     __VA_ARGS__;                                                               \
     break;                                                                     \
   }                                                                            \
   default:                                                                     \
-        AT_ERROR(#NAME, " not implemented yet for specific data type.");\
+    AT_ERROR(#NAME, " not implemented yet for specific data type.");           \
   }
 
-torch::Tensor moe_dispatch_cuda_forward(
-    int s, int ec, int h,
+torch::Tensor moe_dispatch_cuda_forward(int s, int ec, int h,
                                         torch::Tensor batch_tokens,
                                         torch::Tensor mask,
                                         torch::Tensor dest_idx) {
 
   assert(h % 16 == 0);
-    auto res = torch::zeros({ec, h},
+  auto res = torch::zeros(
+      {ec, h},
       torch::dtype(batch_tokens.dtype()).device(batch_tokens.device()));
   auto k = mask.size(0);
 
@@ -603,22 +591,20 @@ torch::Tensor moe_dispatch_cuda_forward(
       moe_dpch_fwd_launch<scalar_t>(
           batch_tokens.data<scalar_t>(), res.data<scalar_t>(),
           mask[0].data<int>(), k == 1 ? nullptr : mask[1].data<int>(),
-            dest_idx[0].data<int>(), k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(),
-            s, h)
-    );
+          dest_idx[0].data<int>(),
+          k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(), s, h));
 
   return res;
 }
 
-torch::Tensor moe_dispatch_cuda_backward(
-    int s, int ec, int h,
+torch::Tensor moe_dispatch_cuda_backward(int s, int ec, int h,
                                          torch::Tensor expert_grad,
                                          torch::Tensor mask,
                                          torch::Tensor dest_idx) {
 
   assert(h % 16 == 0);
-    auto res = torch::zeros({s, h},
-        torch::dtype(expert_grad.dtype()).device(expert_grad.device()));
+  auto res = torch::zeros(
+      {s, h}, torch::dtype(expert_grad.dtype()).device(expert_grad.device()));
   auto k = mask.size(0);
 
   DISPATCH_FLOAT_AND_HALF(
@@ -626,65 +612,62 @@ torch::Tensor moe_dispatch_cuda_backward(
       moe_dpch_bwd_launch<scalar_t>(
           res.data<scalar_t>(), expert_grad.data<scalar_t>(),
           mask[0].data<int>(), k == 1 ? nullptr : mask[1].data<int>(),
-            dest_idx[0].data<int>(), k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(),
-            s, h)
-    );
+          dest_idx[0].data<int>(),
+          k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(), s, h));
 
   return res;
 }
 
-torch::Tensor moe_combine_cuda_forward(
-    int s, int e, int c, int h,
+torch::Tensor moe_combine_cuda_forward(int s, int e, int c, int h,
                                        torch::Tensor expert_tokens,
-    torch::Tensor logits,
-    torch::Tensor mask,
+                                       torch::Tensor logits, torch::Tensor mask,
                                        torch::Tensor dest_idx) {
 
   assert(h % 16 == 0);
   assert(expert_tokens.dtype() == logits.dtype());
 
-    auto res = torch::zeros({s, h},
+  auto res = torch::zeros(
+      {s, h},
       torch::dtype(expert_tokens.dtype()).device(expert_tokens.device()));
   auto k = mask.size(0);
 
   DISPATCH_FLOAT_AND_HALF(
       expert_tokens.scalar_type(), "moe combine forward",
       moe_cb_fwd_launch<scalar_t>(
-            expert_tokens.data<scalar_t>(), res.data<scalar_t>(), logits.data<scalar_t>(),
-            mask[0].data<int>(), k == 1 ? nullptr : mask[1].data<int>(),
-            dest_idx[0].data<int>(), k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(),
-            s, e, c, h)
-    );
+          expert_tokens.data<scalar_t>(), res.data<scalar_t>(),
+          logits.data<scalar_t>(), mask[0].data<int>(),
+          k == 1 ? nullptr : mask[1].data<int>(), dest_idx[0].data<int>(),
+          k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(), s, e, c,
+          h));
 
   return res;
 }
 
-std::vector<torch::Tensor> moe_combine_cuda_backward(
-    int s, int e, int c, int h,
-    torch::Tensor tokens_grad,
-    torch::Tensor expert_tokens,
-    torch::Tensor logits,
-    torch::Tensor mask,
-    torch::Tensor dest_idx) {
+std::vector<torch::Tensor>
+moe_combine_cuda_backward(int s, int e, int c, int h, torch::Tensor tokens_grad,
+                          torch::Tensor expert_tokens, torch::Tensor logits,
+                          torch::Tensor mask, torch::Tensor dest_idx) {
 
   assert(h % 16 == 0);
   assert(tokens_grad.dtype() == expert_tokens.dtype());
   assert(expert_tokens.dtype() == logits.dtype());
 
-    auto egrad = torch::zeros({e * c, h},
+  auto egrad = torch::zeros(
+           {e * c, h},
            torch::dtype(tokens_grad.dtype()).device(tokens_grad.device())),
-         wgrad = torch::zeros({s, e}, torch::dtype(logits.dtype()).device(logits.device()));
+       wgrad = torch::zeros(
+           {s, e}, torch::dtype(logits.dtype()).device(logits.device()));
   auto k = mask.size(0);
 
   DISPATCH_FLOAT_AND_HALF(
       tokens_grad.scalar_type(), "moe combine backward",
       moe_cb_bwd_launch<scalar_t>(
-            tokens_grad.data<scalar_t>(), egrad.data<scalar_t>(), expert_tokens.data<scalar_t>(),
-            logits.data<scalar_t>(), wgrad.data<scalar_t>(),
-            mask[0].data<int>(), k == 1 ? nullptr : mask[1].data<int>(),
-            dest_idx[0].data<int>(), k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(),
-            s, e, c, h)
-    );
+          tokens_grad.data<scalar_t>(), egrad.data<scalar_t>(),
+          expert_tokens.data<scalar_t>(), logits.data<scalar_t>(),
+          wgrad.data<scalar_t>(), mask[0].data<int>(),
+          k == 1 ? nullptr : mask[1].data<int>(), dest_idx[0].data<int>(),
+          k == 1 ? dest_idx[0].data<int>() : dest_idx[1].data<int>(), s, e, c,
+          h));
 
   return {egrad, wgrad};
 }
@@ -695,7 +678,8 @@ torch::Tensor cumsum_sub_one_in_dim0(torch::Tensor mask) {
   assert(mask.dtype() == torch::kInt32);
 
   const int s = mask.size(0), e = mask.size(1);
-    auto res = torch::empty({s, e}, torch::dtype(torch::kInt32).device(mask.device()));
+  auto res =
+      torch::empty({s, e}, torch::dtype(torch::kInt32).device(mask.device()));
   cumsum_launch(mask.data<int>(), res.data<int>(), s, e);
 
   return res;