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[CK][CK Tile] Conv Bwd Data flush cache and profiling improvements (#6090)

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## Motivation

Improve accuracy of conv bwd data perf measurements

## Technical Details
- enable flush cache
- for grouped conv we zero conv input(gemm output) inside device op, so
we also include this in time measurement
- for non-grouped conv we zero conv input(gemm output) outside device op
(in profile_conv_bwd_data_impl.hpp) so it is not included.
- In this pr I changed it to include zeroing if time_kernel/flush cache
is enabled so at now you should have more fair comparison. I changed it
only for time_kernel/flush_cache because MIOpen run own zeroing for
non-grouped solvers.

## Test Plan

test_grouped_conv_bwd_data_*

## Test Result

CI pending

## Submission Checklist

- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
This commit is contained in:
Bartłomiej Kocot
2026-04-04 02:22:22 +02:00
committed by GitHub
parent 9478c6c69f
commit 4112e08d0c
11 changed files with 362 additions and 144 deletions
Download Patch File Download Diff File Expand all files Collapse all files

113
include/ck/tensor_operation/gpu/device/impl/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp
View File

@@ -16,6 +16,7 @@
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/library/utility/numeric.hpp"
namespace ck {
namespace tensor_operation {
@@ -492,6 +493,10 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
c_grid_desc_m_n_container_.push_back(descs[I2]);
}
}
c_space_size_bytes =
ck::accumulate_n<long_index_t>(
input_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>()) *
Conv_N_ * Conv_C_ * sizeof(CDataType);
}
const ADataType* p_a_grid_;
@@ -512,6 +517,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
std::vector<ck::index_t> conv_filter_dilations_;
std::vector<ck::index_t> input_left_pads_;
std::vector<ck::index_t> input_right_pads_;
long_index_t c_space_size_bytes;
};
// Invoker
@@ -571,18 +578,47 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
DeviceOp::BGridDesc_K0_N_K1,
DeviceOp::CGridDesc_M_N,
true>;
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
if(stream_config.flush_cache)
{
// Clear input only for perf measurement.
// For non-grouped solver user has to clear input on his own.
const auto clear_input = [&]() {
if(i == 0)
{
hip_check_error(hipMemsetAsync(arg.p_c_grid_,
0,
arg.c_space_size_bytes,
stream_config.stream_id_));
}
};
ave_time += launch_and_time_kernel_with_preprocess_flush_cache(
stream_config,
clear_input,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
else
{
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
}
else
{
@@ -594,18 +630,47 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
DeviceOp::BGridDesc_K0_N_K1,
DeviceOp::CGridDesc_M_N,
false>;
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
if(stream_config.flush_cache)
{
// Clear input only for perf measurement.
// For non-grouped solver user has to clear input on his own.
const auto clear_input = [&]() {
if(i == 0)
{
hip_check_error(hipMemsetAsync(arg.p_c_grid_,
0,
arg.c_space_size_bytes,
stream_config.stream_id_));
}
};
ave_time += launch_and_time_kernel_with_preprocess_flush_cache(
stream_config,
clear_input,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
else
{
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
}
}
return ave_time;

113
include/ck/tensor_operation/gpu/device/impl/device_convnd_bwd_data_nwc_kxc_nwk_xdl.hpp
View File

@@ -16,6 +16,7 @@
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/library/utility/numeric.hpp"
namespace ck {
namespace tensor_operation {
@@ -1050,6 +1051,10 @@ struct DeviceConvNdBwdDataNwcKxcNwk_Xdl
input_right_pads_{input_right_pads}
{
CreateABCDesc<NDimSpatial>();
c_space_size_bytes =
ck::accumulate_n<long_index_t>(
input_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>()) *
Conv_N_ * Conv_C_ * sizeof(CDataType);
}
template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
@@ -1216,6 +1221,8 @@ struct DeviceConvNdBwdDataNwcKxcNwk_Xdl
std::vector<ck::index_t> conv_filter_dilations_;
std::vector<ck::index_t> input_left_pads_;
std::vector<ck::index_t> input_right_pads_;
long_index_t c_space_size_bytes;
};
// Invoker
@@ -1273,18 +1280,47 @@ struct DeviceConvNdBwdDataNwcKxcNwk_Xdl
DeviceOp::BGridDesc_K0_N_K1,
DeviceOp::CGridDesc_M_N,
true>;
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
if(stream_config.flush_cache)
{
// Clear input only for perf measurement.
// For non-grouped solver user has to clear input on his own.
const auto clear_input = [&]() {
if(i == 0)
{
hip_check_error(hipMemsetAsync(arg.p_c_grid_,
0,
arg.c_space_size_bytes,
stream_config.stream_id_));
}
};
ave_time += launch_and_time_kernel_with_preprocess_flush_cache(
stream_config,
clear_input,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
else
{
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
}
else
{
@@ -1296,18 +1332,47 @@ struct DeviceConvNdBwdDataNwcKxcNwk_Xdl
DeviceOp::BGridDesc_K0_N_K1,
DeviceOp::CGridDesc_M_N,
false>;
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
if(stream_config.flush_cache)
{
// Clear input only for perf measurement.
// For non-grouped solver user has to clear input on his own.
const auto clear_input = [&]() {
if(i == 0)
{
hip_check_error(hipMemsetAsync(arg.p_c_grid_,
0,
arg.c_space_size_bytes,
stream_config.stream_id_));
}
};
ave_time += launch_and_time_kernel_with_preprocess_flush_cache(
stream_config,
clear_input,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
else
{
ave_time += launch_and_time_kernel(stream_config,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],
arg.c_grid_desc_m_n_container_[i]);
}
}
}
return ave_time;

128
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp
View File

@@ -1225,26 +1225,50 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
has_main_loop,
no_main_loop,
CTranspose>;
return launch_and_time_kernel_with_preprocess(
stream_config,
clear_workspace,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
p_b_grid,
p_a_grid,
arg.p_ds_grid_,
p_e_grid,
gemm_kernel_args,
gemms_count_for_set,
arg.b_element_op_,
arg.a_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.compute_ptr_offset_of_n_,
arg.k_batch_);
if(stream_config.flush_cache)
{
return launch_and_time_kernel_with_preprocess_flush_cache(
stream_config,
clear_workspace,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
p_b_grid,
p_a_grid,
arg.p_ds_grid_,
p_e_grid,
gemm_kernel_args,
gemms_count_for_set,
arg.b_element_op_,
arg.a_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.compute_ptr_offset_of_n_,
arg.k_batch_);
}
else
{
return launch_and_time_kernel_with_preprocess(
stream_config,
clear_workspace,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
p_b_grid,
p_a_grid,
arg.p_ds_grid_,
p_e_grid,
gemm_kernel_args,
gemms_count_for_set,
arg.b_element_op_,
arg.a_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.compute_ptr_offset_of_n_,
arg.k_batch_);
}
}
else
{
@@ -1264,26 +1288,50 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
has_main_loop,
no_main_loop,
CTranspose>;
return launch_and_time_kernel_with_preprocess(
stream_config,
clear_workspace,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
arg.p_ds_grid_,
p_e_grid,
gemm_kernel_args,
gemms_count_for_set,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.compute_ptr_offset_of_n_,
arg.k_batch_);
if(stream_config.flush_cache)
{
return launch_and_time_kernel_with_preprocess_flush_cache(
stream_config,
clear_workspace,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
arg.p_ds_grid_,
p_e_grid,
gemm_kernel_args,
gemms_count_for_set,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.compute_ptr_offset_of_n_,
arg.k_batch_);
}
else
{
return launch_and_time_kernel_with_preprocess(
stream_config,
clear_workspace,
kernel,
dim3(gdx, gdy, gdz),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
arg.p_ds_grid_,
p_e_grid,
gemm_kernel_args,
gemms_count_for_set,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.compute_ptr_offset_of_n_,
arg.k_batch_);
}
}
};
if(has_loop_in_all_gemm)

6
include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_backward_data_kernel.hpp
View File

@@ -903,13 +903,11 @@ struct GroupedConvolutionBackwardDataKernel
const auto& d_block_window =
MakeDBlockWindows(ds_ptr, kargs, group_id, block_idx_m, block_idx_n);
const index_t num_loop = amd_wave_read_first_lane(TilePartitioner::GetLoopNum(splitted_k));
const bool has_hot_loop = GemmPipeline::BlockHasHotloop(num_loop);
const TailNumber tail_num = GemmPipeline::GetBlockLoopTailNum(num_loop);
const index_t num_loop = amd_wave_read_first_lane(TilePartitioner::GetLoopNum(splitted_k));
// Run GEMM cooperatively by whole workgroup.
const auto& c_block_tile = GemmPipeline{}.template operator()(
a_block_window, b_block_window, num_loop, has_hot_loop, tail_num, smem_ptr_0);
a_block_window, b_block_window, num_loop, smem_ptr_0);
const index_t k_batch = amd_wave_read_first_lane(kargs.k_batch);

11
profiler/include/profiler/grouped_convolution_backward_data_tile_algs.hpp
View File

@@ -65,7 +65,9 @@ run_grouped_conv_backward_data_tile_algs(const ckt::Args<SIGNATURE>& args,
const ckt::Outputs<SIGNATURE>& outputs,
const ck_tile::stream_config& s_conf)
{
float best_avg_time = std::numeric_limits<float>::max();
// Run first instance as dummy to get proper time from the first instance
bool dummy_run_executed = false;
float best_avg_time = std::numeric_limits<float>::max();
std::string best_op_name, op_name;
int best_split_k = 0;
ck::index_t best_instance_index = -1;
@@ -121,6 +123,13 @@ run_grouped_conv_backward_data_tile_algs(const ckt::Args<SIGNATURE>& args,
run_alg_func(args_k_batch, inputs, outputs, s_conf);
if(is_supported)
{
if((s_conf.time_kernel_ || s_conf.flush_cache_) && !dummy_run_executed)
{
// Run first instance twice
std::tie(is_supported, avg_time, op_name) =
run_alg_func(args_k_batch, inputs, outputs, s_conf);
dummy_run_executed = true;
}
ckt::ValidationReport report;
auto&& [rtol, atol] =
get_rtol_atol<SIGNATURE>(num_accums, k_batch, max_accumulated_value);

14
profiler/include/profiler/grouped_convolution_backward_weight_tile_algs.hpp
View File

@@ -106,17 +106,17 @@ run_grouped_conv_backward_weight_tile_algs(const ckt::Args<SIGNATURE>& args,
{
ckt::Args<SIGNATURE> args_k_batch = args;
args_k_batch.k_batch = k_batch;
if((s_conf.time_kernel_ || s_conf.flush_cache_) && !dummy_run_executed)
{
// Run first instance twice when profiling to stabilize timing
std::tie(is_supported, avg_time, op_name) =
run_alg_func(args_k_batch, inputs, outputs, s_conf);
dummy_run_executed = true;
}
std::tie(is_supported, avg_time, op_name) =
run_alg_func(args_k_batch, inputs, outputs, s_conf);
if(is_supported)
{
if((s_conf.time_kernel_ || s_conf.flush_cache_) && !dummy_run_executed)
{
// Run first instance twice when profiling to stabilize timing
std::tie(is_supported, avg_time, op_name) =
run_alg_func(args_k_batch, inputs, outputs, s_conf);
dummy_run_executed = true;
}
ckt::ValidationReport report;
auto&& [rtol, atol] =
get_rtol_atol<SIGNATURE>(num_accums, k_batch, max_accumulated_value);

13
profiler/include/profiler/grouped_convolution_forward_tile_algs.hpp
View File

@@ -86,15 +86,16 @@ run_grouped_conv_forward_tile_algs(const ckt::Args<SIGNATURE>& args,
auto ref_conv = ReferenceInstance{};
auto ref_result = ckt::run(ref_conv, args, inputs, reference.get());
auto run_alg = [&](auto&& run_alg_func) {
if(!dummy_run_executed)
{
// Run first instance twice
std::tie(is_supported, avg_time, op_name) = run_alg_func(args, inputs, outputs, s_conf);
dummy_run_executed = true;
}
std::tie(is_supported, avg_time, op_name) = run_alg_func(args, inputs, outputs, s_conf);
if(is_supported)
{
if((s_conf.time_kernel_ || s_conf.flush_cache_) && !dummy_run_executed)
{
// Run first instance twice
std::tie(is_supported, avg_time, op_name) =
run_alg_func(args, inputs, outputs, s_conf);
dummy_run_executed = true;
}
best_avg_time = std::min(best_avg_time, avg_time);
best_op_name = best_avg_time < avg_time ? best_op_name : op_name;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms," << " " << op_name

41
profiler/include/profiler/profile_conv_bwd_data_impl.hpp
View File

@@ -197,10 +197,11 @@ bool profile_conv_bwd_data_impl(int do_verification,
}
std::string best_op_name;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
int num_kernel = 0;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
int num_kernel = 0;
bool dummy_run_executed = false;
for(auto& op_ptr : op_ptrs)
{
@@ -230,16 +231,38 @@ bool profile_conv_bwd_data_impl(int do_verification,
// skip test if instance_index is specified
continue;
}
// for conv bwd data, some input tensor element are zero, but not written by kernel,
// need to set zero
in_device_buf.SetZero();
if(!time_kernel)
{
// Don't clear for perf measurement.
// For non-grouped solver user has to clear input on his own.
// for conv bwd data, some input tensor element are zero, but not written by kernel,
// need to set zero
in_device_buf.SetZero();
}
std::string op_name = op_ptr->GetTypeString();
auto invoker_ptr = op_ptr->MakeInvokerPointer();
float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
// Run first instance twice to get proper time
if(time_kernel && !dummy_run_executed)
{
invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
0 /*log_level*/,
5 /*cold_iters*/,
50 /*nrepeat_*/,
time_kernel /*flush_cache*/});
dummy_run_executed = true;
}
float avg_time = invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
0 /*log_level*/,
5 /*cold_iters*/,
50 /*nrepeat_*/,
time_kernel /*flush_cache*/});
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();

24
profiler/include/profiler/profile_grouped_conv_bwd_data_impl.hpp
View File

@@ -287,6 +287,8 @@ bool profile_grouped_conv_bwd_data_impl(int do_verification,
bool pass = true;
index_t num_kernel = 0;
index_t valid_instances = 0;
bool dummy_run_executed = false;
auto run_impl = [&](auto& op_ptr, auto& argument_ptr, const index_t& split_k_for_run) {
// workspace_sz will be equal to 0 for other layout than NGCHW
const std::size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
@@ -317,8 +319,25 @@ bool profile_grouped_conv_bwd_data_impl(int do_verification,
auto invoker_ptr = op_ptr->MakeInvokerPointer();
float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
// Run first instance twice to get proper time
if(time_kernel && !dummy_run_executed)
{
invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
0 /*log_level*/,
5 /*cold_iters*/,
50 /*nrepeat_*/,
time_kernel /*flush_cache*/});
dummy_run_executed = true;
}
float avg_time = invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
0 /*log_level*/,
5 /*cold_iters*/,
50 /*nrepeat_*/,
time_kernel /*flush_cache*/});
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
@@ -495,7 +514,6 @@ bool profile_grouped_conv_bwd_data_impl(int do_verification,
{
std::cout << "\nValid instances for this problem:" << std::endl;
}
for(auto& op_ptr : op_ptrs)
{
for(std::size_t split_k_id = 0; split_k_id < split_k_list.size(); split_k_id++)

40
profiler/include/profiler/profile_grouped_conv_fwd_impl.hpp
View File

@@ -296,7 +296,8 @@ bool profile_grouped_conv_fwd_impl(int do_verification,
index_t best_instance_index = 0;
// profile device op instances
bool pass = true;
bool pass = true;
bool dummy_run_executed = false;
auto run_impl = [&](auto& op_ptr, auto& argument_ptr) {
// workspace_sz will be equal to 0 for other layout than NGCHW
@@ -331,6 +332,19 @@ bool profile_grouped_conv_fwd_impl(int do_verification,
auto invoker_ptr = op_ptr->MakeInvokerPointer();
// Run first instance twice to get proper time
if(time_kernel && !dummy_run_executed)
{
invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
0 /*log_level*/,
5 /*cold_iters*/,
50 /*nrepeat_*/,
time_kernel /*flush_cache*/});
dummy_run_executed = true;
}
float avg_time = invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
@@ -437,30 +451,6 @@ bool profile_grouped_conv_fwd_impl(int do_verification,
std::cout << "\nValid instances for this problem:" << std::endl;
}
// Run first instance twice to get proper time
{
auto argument_ptr = op_ptrs[0]->MakeArgumentPointer(in_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
{},
out_device_buf.GetDeviceBuffer(),
a_g_n_c_wis_lengths,
a_g_n_c_wis_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
{},
{},
e_g_n_k_wos_lengths,
e_g_n_k_wos_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op);
run_impl(op_ptrs[0], argument_ptr);
}
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr = op_ptr->MakeArgumentPointer(in_device_buf.GetDeviceBuffer(),

3
profiler/src/profile_grouped_conv_bwd_data_tile.cpp
View File

@@ -89,7 +89,8 @@ int call_profiler(const ckt::Args<SIGNATURE>& args,
0 /*log_level*/,
5 /*cold_iters*/,
50 /*nrepeat_*/,
true /*is_gpu_timer_*/});
true /*is_gpu_timer_*/,
time_kernel /*flush_cache*/});
if(time_kernel)
{
std::cout << "\nBest configuration parameters:" << "\n\tname: " << op_name << " (instance "