refactor grouped bwd example and fix some bugs

example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward.cpp

@@ -25,7 +25,7 @@ Kernel outputs:
 
 #define PRINT_HOST 0
 #define USING_MASK 0
-#define RANGE_HDKO 2 // 0~2
+#define RANGE_HDKO 1 // 0~2
 
 #include <iostream>
 #include <numeric>
@@ -523,7 +523,7 @@ int run(int argc, char* argv[])
         G0 = std::stoi(argv[8]);
         G1 = std::stoi(argv[9]);
 
-        alpha = std::stof(argv[10]);
+        alpha  = std::stof(argv[10]);
         p_drop = std::stof(argv[11]);
 
         input_permute  = std::stoi(argv[12]);
@@ -540,9 +540,9 @@ int run(int argc, char* argv[])
         exit(0);
     }
 
-    float p_dropout                 = 1 - p_drop;
-    uint16_t p_dropout_in_16bits    = uint16_t(std::floor(p_dropout * 65535.0));
-    float rp_dropout                = 1.0 / p_dropout;
+    float p_dropout              = 1 - p_drop;
+    uint16_t p_dropout_in_16bits = uint16_t(std::floor(p_dropout * 65535.0));
+    float rp_dropout             = 1.0 / p_dropout;
 
     std::cout << "do_verification: " << do_verification << std::endl;
     std::cout << "init_method: " << init_method << std::endl;
@@ -678,7 +678,6 @@ int run(int argc, char* argv[])
         //    = 0
     }
 
-    // calculate y & log-sum-exp beforehand
     Tensor<DataType> q_g_m_k({BatchCount, M, K});
     Tensor<DataType> k_g_n_k({BatchCount, N, K});
     Tensor<ZDataType> z_g_m_n({BatchCount, M, N});

include/ck/tensor_operation/gpu/device/impl/device_grouped_multihead_attention_backward_xdl_cshuffle_pt1.hpp → include/ck/tensor_operation/gpu/device/impl/device_grouped_multihead_attention_backward_xdl_cshuffle_v1.hpp

@@ -98,7 +98,7 @@ __global__ void
     unsigned short* z_matrix_ptr =
         (arg_ptr[group_id].p_z_grid_ == nullptr ? nullptr
                                                 : arg_ptr[group_id].p_z_grid_ + z_batch_offset);
-    
+
     GridwiseGemm::template Run<HasMainKBlockLoop>(
         arg_ptr[group_id].p_a_grid_ + a_batch_offset,
         arg_ptr[group_id].p_b_grid_ + b_batch_offset,
@@ -211,7 +211,7 @@ template <index_t NumDimG,
           index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
           MaskingSpecialization MaskingSpec,
           LoopScheduler LoopSched = LoopScheduler::Default>
-struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
+struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_V1
     : public BaseOperator // TODO inherit atten bwd op once API stablizes
 {
     static_assert(NumDimG > 0 && NumDimM > 0 && NumDimN > 0 && NumDimK > 0 && NumDimO > 0,
@@ -223,7 +223,7 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
     // TODO: implement bias combination
     static_assert(NumAcc0Bias == 0 && NumAcc0Bias == 0, "Bias addition is unimplemented");
 
-    using DeviceOp = DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1;
+    using DeviceOp = DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_V1;
     struct ProblemDesc
     {
         std::vector<index_t> a_gs_ms_ks_lengths;
@@ -448,7 +448,6 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
         }
     }
 
-
     using AGridDesc_AK0_M_AK1  = decltype(MakeAGridDescriptor_AK0_M_AK1({}, {}));
     using BGridDesc_BK0_N_BK1  = decltype(MakeBGridDescriptor_BK0_N_BK1({}, {}));
     using B1GridDesc_BK0_N_BK1 = decltype(MakeBGridDescriptor_BK0_N_BK1({}, {}));
@@ -711,7 +710,7 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
             }
 
             grid_size_ = 0;
-            for(std::size_t i = 0; i < group_count_; i++)
+            for(index_t i = 0; i < group_count_; i++)
             {
                 const auto p_a_grid     = static_cast<const DataType*>(p_As[i]);
                 const auto p_b_grid     = static_cast<const DataType*>(p_Bs[i]);
@@ -895,7 +894,8 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
             // for(std::size_t i = 0; i < arg.group_count_; i++)
             // {
             //     const auto K =
-            //         arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2);
+            //         arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0) *
+            //         arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2);
             //     const bool y = GridwiseGemm::CalculateHasMainKBlockLoop(K);
             //     all_has_main_k_block_loop &= y;
             //     some_has_main_k_block_loop |= y;
@@ -976,7 +976,7 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
             return false;
         }
 
-        for(std::size_t i = 0; i < arg.group_count_; i++)
+        for(index_t i = 0; i < arg.group_count_; i++)
         {
             // TODO: Check if tensor specialization & strides mismatch
             const auto& kernel_arg = arg.group_kernel_args_[i];
@@ -986,7 +986,8 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
             const index_t c_m       = kernel_arg.y_grid_desc_m_o_.GetLength(I0);
             const index_t c_gemm1n  = kernel_arg.y_grid_desc_m_o_.GetLength(I1);
             const index_t a_m       = kernel_arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
-            const index_t b1_gemm1n = kernel_arg.b1_grid_desc_bk0_n_bk1_.GetLength(I0) * kernel_arg.b1_grid_desc_bk0_n_bk1_.GetLength(I2);
+            const index_t b1_gemm1n = kernel_arg.b1_grid_desc_bk0_n_bk1_.GetLength(I0) *
+                                      kernel_arg.b1_grid_desc_bk0_n_bk1_.GetLength(I2);
 
             if(!(c_g == device_arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n))
             {
@@ -1160,7 +1161,7 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1
         auto str = std::stringstream();
 
         // clang-format off
-        str << "DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_PT1"
+        str << "DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_V1"
             << "<"
             << BlockSize << ", "
             << MPerBlock << ", "

include/ck/tensor_operation/gpu/device/impl/device_grouped_multihead_attention_backward_xdl_cshuffle_v2.hpp

@@ -98,7 +98,7 @@ __global__ void
     unsigned short* z_matrix_ptr =
         (arg_ptr[group_id].p_z_grid_ == nullptr ? nullptr
                                                 : arg_ptr[group_id].p_z_grid_ + z_batch_offset);
-    
+
     GridwiseGemm::template Run<HasMainKBlockLoop>(
         arg_ptr[group_id].p_a_grid_ + a_batch_offset,
         arg_ptr[group_id].p_b_grid_ + b_batch_offset,
@@ -703,7 +703,7 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_V2
             }
 
             grid_size_ = 0;
-            for(std::size_t i = 0; i < group_count_; i++)
+            for(index_t i = 0; i < group_count_; i++)
             {
                 const auto p_a_grid     = static_cast<const DataType*>(p_As[i]);
                 const auto p_b_grid     = static_cast<const DataType*>(p_Bs[i]);
@@ -884,10 +884,10 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_V2
 
             bool all_has_main_k_block_loop  = true;
             bool some_has_main_k_block_loop = false;
-            for(std::size_t i = 0; i < arg.group_count_; i++)
+            for(index_t i = 0; i < arg.group_count_; i++)
             {
-                const auto K =
-                    arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2);
+                const auto K = arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0) *
+                               arg.group_kernel_args_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2);
                 const bool y = GridwiseGemm::CalculateHasMainKBlockLoop(K);
                 all_has_main_k_block_loop &= y;
                 some_has_main_k_block_loop |= y;
@@ -968,7 +968,7 @@ struct DeviceGroupedMultiheadAttentionBackward_Xdl_CShuffle_V2
             return false;
         }
 
-        for(std::size_t i = 0; i < arg.group_count_; i++)
+        for(index_t i = 0; i < arg.group_count_; i++)
         {
             // TODO: Check if tensor specialization & strides mismatch
             const auto& kernel_arg = arg.group_kernel_args_[i];