add mnk padding

example/24_batched_gemm_c_permute/batched_gemm_c_permute_xdl_fp16.cpp

@@ -39,7 +39,9 @@ using AElementOp = ck::tensor_operation::element_wise::PassThrough;
 using BElementOp = ck::tensor_operation::element_wise::PassThrough;
 using CElementOp = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+// static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+// static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
+static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 // clang-format off
 using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmCPermuteXdl
@@ -47,7 +49,8 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmCPermu
 //######|        |        |  Type|  Type|  Type|    Type| Elementwise| Elementwise| Elementwise|Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|  MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|  ScalarPerVector|
 //######|        |        |      |      |      |        |   Operation|   Operation|   Operation|              |         |      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |   PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|    _NWaveNPerXdl|
 //######|        |        |      |      |      |        |            |            |            |              |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |             |            |                             |                 |
-        <     Row,     Col,   F16,   F16,   F16,     F32, PassThrough, PassThrough, PassThrough,   GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     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>;
+//      <     Row,     Col,   F16,   F16,   F16,     F32, PassThrough, PassThrough, PassThrough,     MNPadding,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     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>;
+        <     Row,     Col,   F16,   F16,   F16,     F32, PassThrough, PassThrough, PassThrough,     MNKPadding,        1,   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,            1,           1,               S<1, 32, 1, 8>,                8>;
 // clang-format on
 
 using ReferenceBatchedGemmInstance = ck::tensor_operation::host::
@@ -59,13 +62,9 @@ int main(int argc, char* argv[])
     int init_method      = 1;
     bool time_kernel     = false;
 
-    // const int M = 88;
-    // const int N = 64;
-    // const int K = 88;
-
-    const int M = 256;
-    const int N = 128;
-    const int K = 64;
+    const int M = 88;
+    const int N = 64;
+    const int K = 88;
 
     const int stride_A = K;
     const int stride_B = K;
@@ -76,8 +75,8 @@ int main(int argc, char* argv[])
     const int batch_count = G0 * G1;
 
     // output layout - [G0, M, G1, N]
-    const int stride_B0 = M * G1 * N;
-    const int stride_B1 = N;
+    const int stride_G0 = M * G1 * N;
+    const int stride_G1 = N;
     const int stride_M  = G1 * N;
     const int stride_N  = 1;
 
@@ -97,7 +96,7 @@ int main(int argc, char* argv[])
 
     // GEMM shape
     ck::tensor_operation::device::BatchedGemmCPermuteDesc batched_gemm_c_permute_desc{
-        G0, G1, M, N, stride_B0, stride_B1, stride_M, stride_N};
+        G0, G1, M, N, stride_G0, stride_G1, stride_M, stride_N};
 
     auto f_host_tensor_descriptor = [](std::size_t batch_count_,
                                        std::size_t row,
@@ -119,24 +118,24 @@ int main(int argc, char* argv[])
     Tensor<ADataType> a_g_m_k(f_host_tensor_descriptor(batch_count, M, K, stride_A, ALayout{}));
     Tensor<BDataType> b_g_k_n(f_host_tensor_descriptor(batch_count, K, N, stride_B, BLayout{}));
 
-    auto f_host_c_tensor_descriptor = [](std::size_t B0_,
-                                         std::size_t B1_,
+    auto f_host_c_tensor_descriptor = [](std::size_t G0_,
+                                         std::size_t G1_,
                                          std::size_t M_,
                                          std::size_t N_,
-                                         std::size_t stride_B0_,
-                                         std::size_t stride_B1_,
+                                         std::size_t stride_G0_,
+                                         std::size_t stride_G1_,
                                          std::size_t stride_M_,
                                          std::size_t stride_N_) {
         return HostTensorDescriptor(
-            std::vector<std::size_t>({B0_, B1_, M_, N_}),
-            std::vector<std::size_t>({stride_B0_, stride_B1_, stride_M_, stride_N_}));
+            std::vector<std::size_t>({G0_, G1_, M_, N_}),
+            std::vector<std::size_t>({stride_G0_, stride_G1_, stride_M_, stride_N_}));
     };
 
     Tensor<CDataType> c_g0_g1_m_n_host_result(
-        f_host_c_tensor_descriptor(G0, G1, M, N, stride_B0, stride_B1, stride_M, stride_N));
+        f_host_c_tensor_descriptor(G0, G1, M, N, stride_G0, stride_G1, stride_M, stride_N));
 
     Tensor<CDataType> c_g0_g1_m_n_device_result(
-        f_host_c_tensor_descriptor(G0, G1, M, N, stride_B0, stride_B1, stride_M, stride_N));
+        f_host_c_tensor_descriptor(G0, G1, M, N, stride_G0, stride_G1, stride_M, stride_N));
 
     std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
     std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;