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Adding remaining conv, dynamic_op, and scaleadd_scaleadd_relu flavors for grouped conv fwd (#3529)

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* Adding remaining flavors for grouped conv fwd

As titled. Following variants are added:
- grouped_conv2d_fwd_dynamic_op
- grouped_conv3d_fwd_dynamic_op
- grouped_conv3d_fwd_bilinear
- grouped_conv3d_fwd_convscale
- grouped_conv3d_fwd_convinvscale
- grouped_conv3d_fwd_convscale_add
- grouped_conv3d_fwd_convscale_relu
- grouped_conv3d_fwd_scale
- grouped_conv3d_fwd_combconvscale
- grouped_conv3d_fwd_scaleadd_scaleadd_relu

* Fix incomplete parsing of types from source names in add_instance_library() cmakelists function so we don't build f8 on RDNA3.

* Do not build f8 / bf8 only flavor tests on RDNA3

* Make sure we have proper generic instances for all instance lists related to the post-ces extra flavors, with scalarPerVector = 1. Then disable all but one generic instance per instance list to reduce compile time.

* Post rebase fix: Template parameters for Grouped Conv Fwd Device Impl got tweaked upstream.

* adding int8 and fp16 overloads to the elementwise operations

* fixed copilot nits

* Addressing review comments:

- removed unnecessary examples for dynamic op
- removed unnecessary conv specalizations for all the flavors
- removed spurious bilinear and scale source files

* clang-format

* reduced no of tests

---------

Co-authored-by: Wojciech Laskowski <wojciech.laskowski@streamhpc.com>

[ROCm/composable_kernel commit: 2377a62837]
This commit is contained in:
Kiefer van Teutem
2026-01-30 17:02:14 +01:00
committed by GitHub
parent 09d443a7ad
commit 65c2e81817
72 changed files with 5178 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

311
profiler/include/profiler/profile_grouped_conv_fwd_convscale_add_impl.hpp Normal file
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@@ -0,0 +1,311 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include <iomanip>
#include <iostream>
#include <typeinfo>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale_add.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
namespace ck {
namespace profiler {
template <ck::index_t NDimSpatial,
typename InLayout,
typename WeiLayout,
typename DLayout,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename DDataType,
typename OutDataType,
typename AComputeType = InDataType,
typename BComputeType = AComputeType,
typename IndexType = ck::index_t>
bool profile_grouped_conv_fwd_convscale_add_impl(
int do_verification,
int init_method,
bool do_log,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param,
const ck::tensor_operation::element_wise::ConvScaleAdd& convscaleadd_op =
ck::tensor_operation::element_wise::ConvScaleAdd{})
{
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::ConvScaleAdd;
bool pass = true;
auto f_host_tensor_descriptor =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
auto f_host_tensor_descriptor_packed =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);
auto e_host_tensor_descriptor =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
auto d_host_tensor_descriptor =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<DLayout>(conv_param);
std::array<IndexType, NDimSpatial + 3> a_g_n_c_wis_lengths{};
std::array<IndexType, NDimSpatial + 3> a_g_n_c_wis_strides{};
std::array<IndexType, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<IndexType, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<IndexType, NDimSpatial + 3> d_g_n_k_wos_lengths{};
std::array<IndexType, NDimSpatial + 3> d_g_n_k_wos_strides{};
std::array<IndexType, NDimSpatial + 3> e_g_n_k_wos_lengths{};
std::array<IndexType, NDimSpatial + 3> e_g_n_k_wos_strides{};
std::array<IndexType, NDimSpatial> conv_filter_strides{};
std::array<IndexType, NDimSpatial> conv_filter_dilations{};
std::array<IndexType, NDimSpatial> input_left_pads{};
std::array<IndexType, NDimSpatial> input_right_pads{};
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(f_host_tensor_descriptor.GetLengths(), a_g_n_c_wis_lengths);
copy(f_host_tensor_descriptor.GetStrides(), a_g_n_c_wis_strides);
copy(f_host_tensor_descriptor_packed.GetLengths(), b_g_k_c_xs_lengths);
copy(f_host_tensor_descriptor_packed.GetStrides(), b_g_k_c_xs_strides);
copy(d_host_tensor_descriptor.GetLengths(), d_g_n_k_wos_lengths);
copy(d_host_tensor_descriptor.GetStrides(), d_g_n_k_wos_strides);
copy(e_host_tensor_descriptor.GetLengths(), e_g_n_k_wos_lengths);
copy(e_host_tensor_descriptor.GetStrides(), e_g_n_k_wos_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
copy(conv_param.input_right_pads_, input_right_pads);
Tensor<InDataType> input(f_host_tensor_descriptor);
Tensor<WeiDataType> weight(f_host_tensor_descriptor_packed);
Tensor<DDataType> d_tensor(d_host_tensor_descriptor);
Tensor<OutDataType> host_output(e_host_tensor_descriptor);
Tensor<OutDataType> device_output(e_host_tensor_descriptor);
std::cout << "input: " << input.mDesc << std::endl;
std::cout << "weight: " << weight.mDesc << std::endl;
std::cout << "d_tensor: " << d_tensor.mDesc << std::endl;
std::cout << "output: " << host_output.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
weight.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
d_tensor.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
break;
default:
input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
weight.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
d_tensor.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
}
DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * weight.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_tensor.mDesc.GetElementSpaceSize());
DeviceMem out_device_buf(sizeof(OutDataType) * device_output.mDesc.GetElementSpaceSize());
in_device_buf.ToDevice(input.mData.data());
wei_device_buf.ToDevice(weight.mData.data());
d_device_buf.ToDevice(d_tensor.mData.data());
if(do_verification)
{
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<
NDimSpatial,
InDataType,
WeiDataType,
float,
InElementOp,
WeiElementOp,
ck::tensor_operation::element_wise::PassThrough>{};
Tensor<float> c_tensor(e_host_tensor_descriptor);
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument_c =
ref_conv.MakeArgument(input,
weight,
c_tensor,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
InElementOp{},
WeiElementOp{},
ck::tensor_operation::element_wise::PassThrough{});
c_tensor.SetZero();
ref_invoker.Run(ref_argument_c);
host_output.ForEach([&](auto&, auto idx) {
convscaleadd_op(host_output(idx), c_tensor(idx), d_tensor(idx));
});
}
std::string best_op_name;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
int valids = 0;
using DeviceOp =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<DLayout>,
OutLayout,
InDataType,
WeiDataType,
ck::Tuple<DDataType>,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
AComputeType,
BComputeType>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
for(std::size_t i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
std::array<const void*, 1>{static_cast<DDataType*>(d_device_buf.GetDeviceBuffer())},
static_cast<OutDataType*>(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,
std::array<std::array<IndexType, NDimSpatial + 3>, 1>{d_g_n_k_wos_lengths},
std::array<std::array<IndexType, NDimSpatial + 3>, 1>{d_g_n_k_wos_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,
InElementOp{},
WeiElementOp{},
convscaleadd_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
++valids;
std::string op_name = op_ptr->GetTypeString();
if(do_log)
{
std::cout << "Evaluating [" << i << "] " << op_name << std::endl;
}
out_device_buf.SetZero();
auto ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
auto flop = conv_param.GetFlops();
auto num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>() +
sizeof(DDataType) * (conv_param.G_ * conv_param.N_ * conv_param.K_);
for(std::size_t j = 0; j < conv_param.filter_spatial_lengths_.size(); ++j)
{
num_btype += sizeof(DDataType) * conv_param.output_spatial_lengths_[j];
}
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
if(do_log)
{
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << std::endl;
}
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_avg_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
out_device_buf.FromDevice(device_output.mData.data());
double rtol = 1e-3, atol = 1e-3;
if(std::is_same<OutDataType, ck::f8_t>::value)
{
rtol = 1e-1;
atol = 16.1;
}
bool is_valid = ck::utils::check_err(
device_output, host_output, "incorrect results", rtol, atol);
if(!is_valid)
{
pass = false;
}
if(do_log)
{
LogRangeAsType<float>(std::cout << "input : ", input.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "weight: ", weight.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "d_tensor: ", d_tensor.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "host_output : ", host_output.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "device_output: ", device_output.mData, ",")
<< std::endl;
}
}
}
else
{
if(do_log)
{
std::cout << op_ptr->GetTypeString() << " does not support this problem"
<< std::endl;
}
}
}
printf("\033[36mvalids: %d\033[0m\n", valids);
if(valids > 0)
{
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
}
return pass;
}
} // namespace profiler
} // namespace ck

2
profiler/include/profiler/profile_grouped_conv_fwd_impl.hpp
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@@ -13,6 +13,8 @@
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_clamp.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_dynamic_op.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convinvscale.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"

3
profiler/include/profiler/profile_grouped_conv_fwd_outelementop_impl.hpp
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@@ -5,6 +5,7 @@
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convinvscale.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale_relu.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scale.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
#include "ck/library/reference_tensor_operation/gpu/naive_conv_fwd_gpu.hpp"
@@ -43,7 +44,7 @@ bool profile_grouped_conv_fwd_outelementop_impl(int do_verification,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param)
{
auto pass = true; // return status
auto pass = true;
using CShuffleDataType = float;

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profiler/include/profiler/profile_grouped_conv_fwd_scaleadd_scaleadd_relu_impl.hpp Normal file
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@@ -0,0 +1,391 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scaleadd_scaleadd_relu.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/host_tensor.hpp"
namespace ck {
namespace profiler {
template <typename DataType>
inline constexpr double get_rtol_scaleadd()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline constexpr double get_atol_scaleadd()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else
{
return 1e-3;
}
}
template <ck::index_t NDimSpatial,
typename InLayout,
typename WeiLayout,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename OutElementOp,
typename AComputeType = InDataType,
typename BComputeType = AComputeType>
bool profile_grouped_conv_fwd_scaleadd_scaleadd_relu_impl(
int do_verification,
int init_method,
bool do_log,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param)
{
auto pass = true;
using CShuffleDataType = float;
using BiasDataType = std::conditional_t<std::is_same_v<InDataType, int8_t>, float, InDataType>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{1.0f, 2.0f};
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
const index_t G = conv_param.G_;
const index_t K = conv_param.K_;
auto bias1_ndhwgk_desc = out_g_n_k_wos_desc;
auto bias2_g_k_desc = HostTensorDescriptor({G, K});
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial + 3> bias1_ndhwgk_lengths{};
std::array<ck::index_t, NDimSpatial + 3> bias1_ndhwgk_strides{};
std::array<ck::index_t, NDimSpatial + 3> bias2_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> bias2_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
copy(out_g_n_k_wos_desc.GetLengths(), bias1_ndhwgk_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), bias1_ndhwgk_strides);
copy(out_g_n_k_wos_desc.GetLengths(), bias2_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), bias2_g_n_k_wos_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
copy(conv_param.input_right_pads_, input_right_pads);
constexpr ck::index_t spatial_offset = 3;
bias2_g_n_k_wos_strides[1] = 0;
for(int i = 0; i < NDimSpatial; i++)
{
bias2_g_n_k_wos_strides[i + spatial_offset] = 0;
}
Tensor<InDataType> input(in_g_n_c_wis_desc);
Tensor<WeiDataType> weight(wei_g_k_c_xs_desc);
Tensor<CShuffleDataType> c(out_g_n_k_wos_desc);
Tensor<OutDataType> host_output(out_g_n_k_wos_desc);
Tensor<OutDataType> device_output(out_g_n_k_wos_desc);
Tensor<BiasDataType> bias1(bias1_ndhwgk_desc);
Tensor<BiasDataType> bias2(bias2_g_k_desc);
std::cout << "input: " << input.mDesc << std::endl;
std::cout << "weight: " << weight.mDesc << std::endl;
std::cout << "output: " << host_output.mDesc << std::endl;
std::cout << "bias1 (NDHWGK): " << bias1.mDesc << std::endl;
std::cout << "bias2 (G_K): " << bias2.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
weight.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-1, 1});
bias1.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-1, 1});
bias2.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-1, 1});
break;
default:
input.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0});
weight.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-1.0, 1.0});
bias1.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-0.5, 0.5});
bias2.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-0.5, 0.5});
}
DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * weight.mDesc.GetElementSpaceSize());
DeviceMem out_device_buf(sizeof(OutDataType) * device_output.mDesc.GetElementSpaceSize());
DeviceMem bias1_device_buf(sizeof(BiasDataType) * bias1.mDesc.GetElementSpaceSize());
DeviceMem bias2_device_buf(sizeof(BiasDataType) * bias2.mDesc.GetElementSpaceSize());
in_device_buf.ToDevice(input.mData.data());
wei_device_buf.ToDevice(weight.mData.data());
bias1_device_buf.ToDevice(bias1.mData.data());
bias2_device_buf.ToDevice(bias2.mData.data());
// run reference op
if(do_verification)
{
std::cout << "\nVerifying algorithm against reference convolution..." << std::endl;
std::cout << "\tUsing (rel_tol,abs_tol) = (" << std::setprecision(7)
<< get_rtol_scaleadd<OutDataType>() << ", " << get_atol_scaleadd<OutDataType>()
<< ")" << std::endl;
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
InDataType,
WeiDataType,
CShuffleDataType,
InElementOp,
WeiElementOp,
PassThrough>{};
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(input,
weight,
c,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
in_element_op,
wei_element_op,
PassThrough{});
c.SetZero();
ref_invoker.Run(ref_argument);
host_output.ForEach([&](auto&, auto idx) {
const auto g_idx = idx[0];
const auto k_idx = idx[2];
const auto conv_shuffle = ck::type_convert<CShuffleDataType>(c(idx));
if constexpr(std::is_same_v<OutDataType, int8_t>)
{
const auto conv_val = ck::type_convert<OutDataType>(conv_shuffle);
const auto bias1_val = bias1(idx);
const auto bias2_val = bias2(g_idx, k_idx);
OutDataType out_val{};
out_element_op(out_val, conv_val, bias1_val, bias2_val);
host_output(idx) = ck::type_convert<OutDataType>(out_val);
}
else
{
const auto conv_val = conv_shuffle;
const auto bias1_val = ck::type_convert<CShuffleDataType>(bias1(idx));
const auto bias2_val = ck::type_convert<CShuffleDataType>(bias2(g_idx, k_idx));
CShuffleDataType out_val{};
out_element_op(out_val, conv_val, bias1_val, bias2_val);
host_output(idx) = ck::type_convert<OutDataType>(out_val);
}
});
}
std::string best_op_name;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
auto run_impl = [&](auto& op_ptr, auto& argument_ptr) {
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
out_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});
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
out_device_buf.FromDevice(device_output.mData.data());
pass = pass & ck::utils::check_err(device_output,
host_output,
"Error: Device and Host results do not match!",
get_rtol_scaleadd<OutDataType>(),
get_atol_scaleadd<OutDataType>());
if(do_log)
{
LogRangeAsType<float>(std::cout << "input : ", input.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "weight: ", weight.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "bias1: ", bias1.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "bias2: ", bias2.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "host_output : ", host_output.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "device_output: ", device_output.mData, ",")
<< std::endl;
}
}
}
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
}
};
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<
NDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<OutLayout, ck::tensor_layout::convolution::G_K>,
OutLayout,
InDataType,
WeiDataType,
ck::Tuple<BiasDataType, BiasDataType>,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
AComputeType,
BComputeType>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "ckProfiler found " << op_ptrs.size() << " instances" << std::endl;
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr = op_ptr->MakeArgumentPointer(
in_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
{bias1_device_buf.GetDeviceBuffer(), bias2_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,
{bias1_ndhwgk_lengths, bias2_g_n_k_wos_lengths},
{bias1_ndhwgk_strides, bias2_g_n_k_wos_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_ptr, argument_ptr);
}
std::cout << "Best configuration parameters:" << "\nname: " << best_op_name
<< "\navg_time: " << best_avg_time << "\ntflops: " << best_tflops
<< "\nGB/s: " << best_gb_per_sec << std::endl;
return pass;
}
} // namespace profiler
} // namespace ck