[Navi3x Bug Fix] fix typo to accept MNKPadding flag correctly. (#597)

* fix a bug blocking wmma_gemm_multipleD

* Utilize matrix padder in device_wmma_op

* cosmetic change for gemmpadding format

* clang format

* Change gridwise gemm from FIFO to KMN loop fashion

example/01_gemm/gemm_wmma_fp16.cpp

@@ -19,15 +19,15 @@ using AElementOp = PassThrough;
 using BElementOp = PassThrough;
 using CElementOp = PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 // clang-format off
 using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmWmma_CShuffle
-// ######| ALayout| BLayout| CLayout|     AData|     BData|     CData|     AccData|         CShuffle|           A|           B|           C|           GEMM| Block|  MPer|  NPer| K0Per|  K1| MPer| NPer|MRepeat|NRepeat|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-// ######|        |        |        |      Type|      Type|      Type|        Type|         DataType| Elementwise| Elementwise| Elementwise| Spacialization|  Size| Block| Block| Block|    | WMMA| WMMA|       |       |   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|MWmmaPerWave|NWmmaPerWave|        _MBlock_MWaveMPerWmma| ScalarPerVector|
-// ######|        |        |        |          |          |          |            |                 |   Operation|   Operation|   Operation|               |      |      |      |      |    |     |     |       |       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|        _NBlock_NWaveNPerWmma|  _NWaveNPerWmma|
-// ######|        |        |        |          |          |          |            |                 |            |            |            |               |      |      |      |      |    |     |     |       |       |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,   256,   128,   256,     8,   8,   16,   16,      4,      4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,           1,           1,              S<1, 32, 1,  8>,               8, 1>;
+// ######| ALayout| BLayout| CLayout|     AData|     BData|     CData|     AccData|         CShuffle|           A|           B|           C|              GEMM| Block|  MPer|  NPer| K0Per|  K1| MPer| NPer|MRepeat|NRepeat|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+// ######|        |        |        |      Type|      Type|      Type|        Type|         DataType| Elementwise| Elementwise| Elementwise|    Spacialization|  Size| Block| Block| Block|    | WMMA| WMMA|       |       |   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|MWmmaPerWave|NWmmaPerWave|        _MBlock_MWaveMPerWmma| ScalarPerVector|
+// ######|        |        |        |          |          |          |            |                 |   Operation|   Operation|   Operation|                  |      |      |      |      |    |     |     |       |       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|        _NBlock_NWaveNPerWmma|  _NWaveNPerWmma|
+// ######|        |        |        |          |          |          |            |                 |            |            |            |                  |      |      |      |      |    |     |     |       |       |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmMNKPadding,   256,   128,   256,     8,   8,   16,   16,      4,      4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,           1,           1,              S<1, 32, 1,  8>,               8, 1>;
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::

example/20_grouped_conv_bwd_weight/run_grouped_conv_bwd_weight_example.inc

@@ -26,7 +26,7 @@ bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
     {
         split_k = 1;
     }
-    
+
     const auto in_g_n_c_wis_desc =
         ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<
             InputLayout<NDimSpatial>>(conv_param);

include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp

@@ -86,120 +86,84 @@ struct DeviceGemmMultipleD_Wmma_CShuffle : public DeviceGemmMultipleD<ALayout,
     // K1 = Max Vector Access Pixels
     static constexpr auto K1Number = Number<K1>{};
 
-    static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
-    {
-        assert(K % K1 == 0);
-
-        const index_t K0 = K / K1;
+    static constexpr auto matrix_padder =
+        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, K0PerBlock* K1};
 
-        const auto a_grid_desc_m_k = [&]() {
-            if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+    static auto MakeAGridDescriptor_K0_M_K1(index_t MRaw, index_t KRaw, index_t StrideA)
+    {
+        const auto a_grid_desc_mraw_kraw = [&]() {
+            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(StrideA, I1));
             }
-#ifdef ENABLE_COLMAJOR
-            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(I1, StrideA));
             }
-#endif
         }();
 
-        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
-        {
-            const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
-
-            return transform_tensor_descriptor(
-                a_grid_desc_m_k,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_right_pad_transform(M, PadM)),
-                make_tuple(Sequence<1>{}, Sequence<0>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
-        else
-        {
-            return transform_tensor_descriptor(
-                a_grid_desc_m_k,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_pass_through_transform(M)),
-                make_tuple(Sequence<1>{}, Sequence<0>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
-    }
-
-    static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
-    {
+        const auto a_grid_desc_m_k = matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+        const auto M               = a_grid_desc_m_k.GetLength(I0);
+        const auto K               = a_grid_desc_m_k.GetLength(I1);
         assert(K % K1 == 0);
-
         const index_t K0 = K / K1;
 
-        const auto b_grid_desc_k_n = [&]() {
-            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+        return transform_tensor_descriptor(
+            a_grid_desc_m_k,
+            make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                       make_pass_through_transform(M)),
+            make_tuple(Sequence<1>{}, Sequence<0>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+    }
+
+    static auto MakeBGridDescriptor_K0_N_K1(index_t KRaw, index_t NRaw, index_t StrideB)
+    {
+        const auto b_grid_desc_nraw_kraw = [&]() {
+            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(StrideB, I1));
             }
-            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(I1, StrideB));
             }
         }();
 
-        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
-        {
-            const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
-
-            return transform_tensor_descriptor(
-                b_grid_desc_k_n,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_right_pad_transform(N, PadN)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
-        else
-        {
-            return transform_tensor_descriptor(
-                b_grid_desc_k_n,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_pass_through_transform(N)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
+        const auto b_grid_desc_n_k = matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+        const auto N               = b_grid_desc_n_k.GetLength(I0);
+        const auto K               = b_grid_desc_n_k.GetLength(I1);
+        assert(K % K1 == 0);
+        const index_t K0 = K / K1;
+
+        return transform_tensor_descriptor(
+            b_grid_desc_n_k,
+            make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                       make_pass_through_transform(N)),
+            make_tuple(Sequence<1>{}, Sequence<0>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
     }
 
     template <typename ELayout_>
-    static auto MakeEGridDescriptor_M_N(index_t M, index_t N, index_t StrideE)
+    static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
     {
-        const auto e_grid_desc_m_n = [&]() {
+        const auto e_grid_desc_mraw_nraw = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, ELayout_>::value)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideE, I1));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(StrideE, I1));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELayout_>::value)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideE));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(I1, StrideE));
             }
         }();
 
-        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
-        {
-            const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
-            const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
-
-            return transform_tensor_descriptor(
-                e_grid_desc_m_n,
-                make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0>{}, Sequence<1>{}));
-        }
-        else
-        {
-
-            return transform_tensor_descriptor(
-                e_grid_desc_m_n,
-                make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0>{}, Sequence<1>{}));
-        }
+        return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
     }
 
     static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& Ms,

include/ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp

@@ -12,6 +12,7 @@
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/device_gemm.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
@@ -78,119 +79,83 @@ struct DeviceGemmWmma_CShuffle : public DeviceGemm<ALayout,
     // K1 = Max Vector Access Pixels
     static constexpr auto K1Number = Number<K1>{};
 
-    static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
-    {
-        assert(K % K1 == 0);
-
-        const index_t K0 = K / K1;
+    static constexpr auto matrix_padder =
+        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, K0PerBlock* K1};
 
-        const auto a_grid_desc_m_k = [&]() {
-            if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+    static auto MakeAGridDescriptor_K0_M_K1(index_t MRaw, index_t KRaw, index_t StrideA)
+    {
+        const auto a_grid_desc_mraw_kraw = [&]() {
+            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(StrideA, I1));
             }
-#ifdef ENABLE_COLMAJOR
-            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(I1, StrideA));
             }
-#endif
         }();
 
-        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
-        {
-            const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
-
-            return transform_tensor_descriptor(
-                a_grid_desc_m_k,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_right_pad_transform(M, PadM)),
-                make_tuple(Sequence<1>{}, Sequence<0>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
-        else
-        {
-            return transform_tensor_descriptor(
-                a_grid_desc_m_k,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_pass_through_transform(M)),
-                make_tuple(Sequence<1>{}, Sequence<0>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
-    }
-
-    static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
-    {
+        const auto a_grid_desc_m_k = matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+        const auto M               = a_grid_desc_m_k.GetLength(I0);
+        const auto K               = a_grid_desc_m_k.GetLength(I1);
         assert(K % K1 == 0);
-
         const index_t K0 = K / K1;
 
-        const auto b_grid_desc_k_n = [&]() {
-            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+        return transform_tensor_descriptor(
+            a_grid_desc_m_k,
+            make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                       make_pass_through_transform(M)),
+            make_tuple(Sequence<1>{}, Sequence<0>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+    }
+
+    static auto MakeBGridDescriptor_K0_N_K1(index_t KRaw, index_t NRaw, index_t StrideB)
+    {
+        const auto b_grid_desc_nraw_kraw = [&]() {
+            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(StrideB, I1));
             }
-            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
-                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(I1, StrideB));
             }
         }();
 
-        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
-        {
-            const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
-
-            return transform_tensor_descriptor(
-                b_grid_desc_k_n,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_right_pad_transform(N, PadN)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
-        else
-        {
-            return transform_tensor_descriptor(
-                b_grid_desc_k_n,
-                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
-                           make_pass_through_transform(N)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-        }
+        const auto b_grid_desc_n_k = matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+        const auto N               = b_grid_desc_n_k.GetLength(I0);
+        const auto K               = b_grid_desc_n_k.GetLength(I1);
+        assert(K % K1 == 0);
+        const index_t K0 = K / K1;
+
+        return transform_tensor_descriptor(
+            b_grid_desc_n_k,
+            make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                       make_pass_through_transform(N)),
+            make_tuple(Sequence<1>{}, Sequence<0>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
     }
 
-    static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
+    static auto MakeCGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideC)
     {
-        const auto c_grid_desc_m_n = [&]() {
+        const auto c_grid_desc_mraw_nraw = [&]() {
             if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(StrideC, I1));
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
             {
-                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(I1, StrideC));
             }
         }();
 
-        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
-        {
-            const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
-            const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
-
-            return transform_tensor_descriptor(
-                c_grid_desc_m_n,
-                make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0>{}, Sequence<1>{}));
-        }
-        else
-        {
-
-            return transform_tensor_descriptor(
-                c_grid_desc_m_n,
-                make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                make_tuple(Sequence<0>{}, Sequence<1>{}));
-        }
+        return matrix_padder.PadCDescriptor_M_N(c_grid_desc_mraw_nraw);
     }
 
     // Gridwise descriptor, mapping to whole given provblem.

include/ck/tensor_operation/gpu/grid/gridwise_gemm_wmma.hpp

@@ -414,7 +414,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_wmma
         constexpr auto KPack = math::integer_least_multiple(K1, WmmaK);
 
         auto blockwise_gemm =
-            BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO<BlockSize,
+            BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle<BlockSize,
                                                          FloatA,
                                                          FloatB,
                                                          FloatAcc,