[rocm-libraries] ROCm/rocm-libraries#5387 (commit 0c259bd)

[CK][CK Tile] Grouped Convolution Backward Weight set of
 fixes (#5387)
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## Motivation

Grouped Convolution Backward Weight split k fixes for CK tile kernels

## Technical Details

- get k batch from kargs to get deduced k batch
- multiply zeroing size by data type size
- disable v6 (producing a incorrect results)

## Test Plan

test_grouped_convnd_bwd_weight_tile

## Test Result

Pass

## Submission Checklist

- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.

example/ck_tile/20_grouped_convolution/grouped_convolution_backward_weight_invoker.hpp

@@ -126,7 +126,7 @@ struct GroupedConvolutionBackwardWeightInvoker
         }
 
         auto preprocess = [&]() {
-            if(args.k_batch > 1)
+            if(kargs.k_batch > 1)
             {
                 ck_tile::hip_check_error(hipMemsetAsync(
                     kargs.wei_ptr, 0, args.template GetWeightByte<WeiDataType>(), s.stream_id_));

example/ck_tile/20_grouped_convolution/grouped_convolution_backward_weight_two_stage_invoker.hpp

@@ -180,7 +180,7 @@ struct GroupedConvolutionBackwardWeightTwoStageInvoker
         }
 
         auto preprocess = [&]() {
-            if(args.k_batch > 1)
+            if(kargs.k_batch > 1)
                 ck_tile::hip_check_error(
                     hipMemsetAsync(ws_args.wei_ptr,
                                    0,

experimental/builder/include/ck_tile/builder/testing/conv/ck_tile.hpp

@@ -6,6 +6,7 @@
 #include "ck_tile/builder/testing/testing.hpp"
 #include "ck_tile/builder/testing/conv/fwd.hpp"
 #include "ck_tile/builder/testing/conv/bwd_weight.hpp"
+#include "ck_tile/builder/factory/helpers/ck_tile/conv_tile_tensor_type.hpp"
 #include "ck_tile/host/kernel_launch.hpp"
 #include "ck_tile/ops/gemm.hpp"
 #include "ck_tile/ops/grouped_convolution.hpp"
@@ -56,6 +57,7 @@ template <auto SIGNATURE, typename InDataType, typename WeiDataType, typename Ou
     if(!Conv::IsSupportedArgument(kargs))
         return RunResult::not_supported("unsupported ck_tile arguments");
 
+    using Types = ck_tile::builder::factory::internal::TileConvTensorTypes<SIGNATURE.data_type>;
     const std::size_t zeroing_size = std::accumulate(std::begin(kargs.wei_g_k_c_xs_lengths.data),
                                                      std::end(kargs.wei_g_k_c_xs_lengths.data),
                                                      1,
@@ -64,10 +66,13 @@ template <auto SIGNATURE, typename InDataType, typename WeiDataType, typename Ou
     auto preprocess = [&]() {
         if constexpr(ConvDirectionIsBackwardWeight<SIGNATURE>)
         {
-            if(args.k_batch > 1)
+            if(kargs.k_batch > 1)
             {
                 ck_tile::hip_check_error(
-                    hipMemsetAsync(kargs.wei_ptr, 0, zeroing_size, s_conf.stream_id_));
+                    hipMemsetAsync(kargs.wei_ptr,
+                                   0,
+                                   zeroing_size * sizeof(typename Types::EDataType),
+                                   s_conf.stream_id_));
             }
         }
     };
@@ -156,7 +161,7 @@ template <auto SIGNATURE, typename InDataType, typename WeiDataType, typename Ou
     auto preprocess = [&]() {
         if constexpr(ConvDirectionIsBackwardWeight<SIGNATURE>)
         {
-            if(args.k_batch > 1)
+            if(kargs.k_batch > 1)
             {
                 ck_tile::hip_check_error(
                     hipMemsetAsync(ws_args.wei_ptr,

experimental/grouped_convolution_tile_instances/generate_instances.py

@@ -434,7 +434,9 @@ def parse_bwd_weight_instances(instances, problem_name):
         if check_vectors(a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector) == False:
             print(f"Skipping instance {instance_id} with irregular load since it's not supported yet.")
             continue
-
+        if pipeline_version == "V6":
+            print(f"Skipping instance {instance_id} with V6 since it's not supported yet.")
+            continue
 
         conv = ConvInstanceTemplateParams(
             spec,

profiler/include/profiler/grouped_convolution_backward_weight_tile_algs.hpp

@@ -73,7 +73,7 @@ void run_cpu_validation(const ckt::Args<SIGNATURE>& args,
 
 template <auto SIGNATURE>
 std::tuple<double, double>
-get_rtol_atol(const int num_accums, const int num_accums_split_k, const float max_accumulated_value)
+get_rtol_atol(const int num_accums, const int k_batch, const float max_accumulated_value)
 {
     using WeiDataType =
         std::conditional_t<SIGNATURE.data_type == ckb::DataType::FP32,
@@ -84,6 +84,8 @@ get_rtol_atol(const int num_accums, const int num_accums_split_k, const float ma
     using ComputeType = WeiDataType;
     using AccDataType = float;
 
+    // Assign middle value of the range for auto deduce
+    const int num_accums_split_k = k_batch > 0 ? k_batch : 64;
     auto rtol = ck_tile::get_relative_threshold<ComputeType, WeiDataType, AccDataType>(
         num_accums / num_accums_split_k);
     auto atol = ck_tile::get_absolute_threshold<ComputeType, WeiDataType, AccDataType>(
@@ -150,14 +152,17 @@ run_grouped_conv_backward_weight_tile_algs(const ckt::Args<SIGNATURE>& args,
     auto run_alg = [&](auto&& run_alg_func) {
         for(auto& k_batch : split_k_values)
         {
-            std::tie(is_supported, avg_time, op_name) = run_alg_func(args, inputs, outputs, s_conf);
+            ckt::Args<SIGNATURE> args_k_batch = args;
+            args_k_batch.k_batch              = k_batch;
+            std::tie(is_supported, avg_time, op_name) =
+                run_alg_func(args_k_batch, inputs, outputs, s_conf);
             if(is_supported)
             {
                 ckt::ValidationReport report;
                 auto&& [rtol, atol] =
                     get_rtol_atol<SIGNATURE>(num_accums, k_batch, max_accumulated_value);
                 ckt::Outputs<SIGNATURE>::reflect(
-                    args,
+                    args_k_batch,
                     [&](std::string_view name,
                         const auto& desc,
                         void* ckt::Outputs<SIGNATURE>::*ptr) {
@@ -182,7 +187,7 @@ run_grouped_conv_backward_weight_tile_algs(const ckt::Args<SIGNATURE>& args,
                                   << " Is all zero:" << error.is_all_zero()
                                   << " max err: " << error.max_error << std::endl;
                         // Check with cpu verification to get a values
-                        run_cpu_validation<SIGNATURE>(args, outputs, reference.get());
+                        run_cpu_validation<SIGNATURE>(args_k_batch, outputs, reference.get());
                     }
                     all_instances_valid = false;
                 }

profiler/src/profile_grouped_conv_bwd_weight_tile.cpp

@@ -136,7 +136,12 @@ int call_profiler(const ckt::Args<SIGNATURE>& args, const std::string& split_k,
             split_k,
             inputs.get(),
             outputs.get(),
-            ck_tile::stream_config{nullptr, time_kernel});
+            ck_tile::stream_config{nullptr,
+                                   time_kernel,
+                                   0 /*log_level*/,
+                                   5 /*cold_iters*/,
+                                   50 /*nrepeat_*/,
+                                   true /*is_gpu_timer_*/});
     if(time_kernel)
     {
         std::cout << "\nBest configuration parameters:" << "\n\tname: " << op_name