Run remod.py under include/ck_tile & example/ck_tile directories

example/ck_tile/12_moe_sorting/moe_sorting_api.cpp

@@ -7,9 +7,9 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_kargs a, ck_tile::stream_conf
 {
     if(t.weight_type == "fp32")
     {
-        using index_t         = ck_tile::index_t;
-        using ms_weight_type  = float;
-        using ms_problem      = ck_tile::MoeSortingProblem<index_t, ms_weight_type>;
+        using index_t        = ck_tile::index_t;
+        using ms_weight_type = float;
+        using ms_problem     = ck_tile::MoeSortingProblem<index_t, ms_weight_type>;
         // using ms_pipeline     = ck_tile::MoeSortingPipeline<ms_problem>;
         using kernel          = ck_tile::MoeSortingKernel<ms_problem>;
         auto kargs            = kernel::MakeKargs(a);

include/ck_tile/host.hpp

@@ -23,12 +23,12 @@
 #include "ck_tile/host/reference/reference_gemm.hpp"
 #include "ck_tile/host/reference/reference_im2col.hpp"
 #include "ck_tile/host/reference/reference_layernorm2d_fwd.hpp"
+#include "ck_tile/host/reference/reference_moe_sorting.hpp"
 #include "ck_tile/host/reference/reference_permute.hpp"
 #include "ck_tile/host/reference/reference_reduce.hpp"
 #include "ck_tile/host/reference/reference_rmsnorm2d_fwd.hpp"
 #include "ck_tile/host/reference/reference_rowwise_quantization2d.hpp"
 #include "ck_tile/host/reference/reference_softmax.hpp"
 #include "ck_tile/host/reference/reference_topk.hpp"
-#include "ck_tile/host/reference/reference_moe_sorting.hpp"
 #include "ck_tile/host/stream_config.hpp"
 #include "ck_tile/host/timer.hpp"

include/ck_tile/host/reference/reference_moe_sorting.hpp

@@ -19,11 +19,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
                                         const index_t unit_size)
 {
     const index_t num_token = topk_ids.mDesc.get_lengths()[0];
-    const index_t topk = topk_ids.mDesc.get_lengths()[1];
+    const index_t topk      = topk_ids.mDesc.get_lengths()[1];
     std::vector<std::vector<IndexType>> expert_tokens(experts,
                                                       std::vector<IndexType>(unit_size, num_token));
-    std::vector<std::vector<WeightType>> expert_token_weights(experts,
-                                                              std::vector<WeightType>(unit_size, 0));
+    std::vector<std::vector<WeightType>> expert_token_weights(
+        experts, std::vector<WeightType>(unit_size, 0));
     std::vector<IndexType> expert_slices(experts, 1);
     std::vector<IndexType> expert_slice_idxs(experts, 0);
 
@@ -31,7 +31,7 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
     {
         for(index_t k = 0; k < topk; k++)
         {
-            IndexType e    = topk_ids(t, k);
+            IndexType e  = topk_ids(t, k);
             WeightType w = weights(t, k);
             index_t idx  = expert_slice_idxs[e];
             if(idx > expert_slices[e] * unit_size - 1)
@@ -53,15 +53,16 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
         }
     }
 
-    IndexType* out_tokens   = sorted_token_ids.data();
-    WeightType* out_weights = sorted_weight.data();
-    IndexType* out_expert_id  = sorted_expert_ids.data();
+    IndexType* out_tokens    = sorted_token_ids.data();
+    WeightType* out_weights  = sorted_weight.data();
+    IndexType* out_expert_id = sorted_expert_ids.data();
     for(index_t e = 0; e < experts; e++)
     {
         memcpy(out_tokens, expert_tokens[e].data(), sizeof(index_t) * expert_slices[e] * unit_size);
         out_tokens += expert_slices[e] * unit_size;
-        memcpy(
-            out_weights, expert_token_weights[e].data(), sizeof(WeightType) * expert_slices[e] * unit_size);
+        memcpy(out_weights,
+               expert_token_weights[e].data(),
+               sizeof(WeightType) * expert_slices[e] * unit_size);
         out_weights += expert_slices[e] * unit_size;
 
         for(index_t s = 0; s < expert_slices[e]; s++)

include/ck_tile/ops/moe_sorting/kernel/moe_sorting_kernel.hpp

@@ -30,7 +30,7 @@ template <typename Problem_>
 struct MoeSortingKernel
 {
     // using Pipeline = remove_cvref_t<Pipeline_>;
-    using Problem  = remove_cvref_t<Problem_>;
+    using Problem = remove_cvref_t<Problem_>;
 
     using IndexType  = typename Problem::IndexType;
     using WeightType = typename Problem::WeightType;
@@ -71,7 +71,7 @@ struct MoeSortingKernel
             tokens_cnts[calc_index(num_experts, threadIdx.x + 1, i)] = 0;
         }
 
-        for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) 
+        for(int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i)
         {
             ++tokens_cnts[calc_index(num_experts, threadIdx.x + 1, topk_id[i])];
         }
@@ -95,7 +95,8 @@ struct MoeSortingKernel
             {
                 cumsum[i] =
                     cumsum[i - 1] +
-                    max(integer_divide_ceil(tokens_cnts[calc_index(num_experts, blockDim.x, i - 1)], unit_size),
+                    max(integer_divide_ceil(tokens_cnts[calc_index(num_experts, blockDim.x, i - 1)],
+                                            unit_size),
                         1) *
                         unit_size;
             }
@@ -137,12 +138,12 @@ struct MoeSortingKernel
     CK_TILE_DEVICE void operator()(Kargs kargs) const
     {
         const size_t numel = kargs.tokens * kargs.topk;
-        return moe_align_block_size_kernel(static_cast<const IndexType *>(kargs.p_topk_ids),
-                                           static_cast<const WeightType *>(kargs.p_weights),
-                                           static_cast<IndexType *>(kargs.sorted_token_ids),
-                                           static_cast<WeightType *>(kargs.sorted_weights),
-                                           static_cast<IndexType *>(kargs.expert_ids),
-                                           static_cast<IndexType *>(kargs.total_tokens_post_pad),
+        return moe_align_block_size_kernel(static_cast<const IndexType*>(kargs.p_topk_ids),
+                                           static_cast<const WeightType*>(kargs.p_weights),
+                                           static_cast<IndexType*>(kargs.sorted_token_ids),
+                                           static_cast<WeightType*>(kargs.sorted_weights),
+                                           static_cast<IndexType*>(kargs.expert_ids),
+                                           static_cast<IndexType*>(kargs.total_tokens_post_pad),
                                            kargs.num_experts,
                                            kargs.unit_size,
                                            numel,

include/ck_tile/ops/reduce/block/block_reduce.hpp

@@ -16,8 +16,8 @@ namespace ck_tile {
 // synchronize reduce result (cross lane reduction and broadcast on replicated dimension)
 template <typename AccDistributedTensor_, typename ReduceFunc, bool WithBroadcast = true>
 CK_TILE_DEVICE void block_tile_reduce_sync(AccDistributedTensor_& acc_tensor,
-                                                          const ReduceFunc& reduce_func,
-                                                          bool_constant<WithBroadcast> = {})
+                                           const ReduceFunc& reduce_func,
+                                           bool_constant<WithBroadcast> = {})
 {
     using Dstr             = typename AccDistributedTensor_::StaticTileDistribution;
     using DstrEncode       = typename Dstr::DstrEncode;
@@ -116,7 +116,7 @@ CK_TILE_DEVICE void block_tile_reduce_sync(AccDistributedTensor_& acc_tensor,
  */
 template <typename AccDistributedTensor_, typename ReduceFunc>
 CK_TILE_DEVICE void block_tile_reduce_xor_sync(AccDistributedTensor_& acc_tensor,
-                                                              const ReduceFunc& reduce_func)
+                                               const ReduceFunc& reduce_func)
 {
     using Dstr             = typename AccDistributedTensor_::StaticTileDistribution;
     using DstrEncode       = typename Dstr::DstrEncode;
@@ -175,9 +175,9 @@ template <typename AccDistributedTensor_,
           index_t... InReduceDims,
           typename ReduceFunc>
 CK_TILE_DEVICE void block_tile_reduce(AccDistributedTensor_& acc_tensor,
-                                                     const InDistributedTensor_& in_tensor,
-                                                     sequence<InReduceDims...>,
-                                                     const ReduceFunc& reduce_func)
+                                      const InDistributedTensor_& in_tensor,
+                                      sequence<InReduceDims...>,
+                                      const ReduceFunc& reduce_func)
 {
     constexpr auto I0 = number<0>{};
     constexpr auto I1 = number<1>{};
@@ -250,9 +250,9 @@ template <typename AccDataType_,
           typename ReduceFunc,
           typename InDataType_>
 CK_TILE_DEVICE auto block_tile_reduce(const InDistributedTensor_& in_tensor,
-                                                     sequence<InReduceDims...> in_reduce_dims,
-                                                     const ReduceFunc& reduce_func,
-                                                     const InDataType_& reduce_init)
+                                      sequence<InReduceDims...> in_reduce_dims,
+                                      const ReduceFunc& reduce_func,
+                                      const InDataType_& reduce_init)
 {
     using InDataType  = typename InDistributedTensor_::DataType;
     using AccDataType = remove_cvref_t<AccDataType_>;