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[CK, CK_TILE] Add GPU Reference Implementations for Grouped Convolution (#3216)

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* LWPCK-4043: Add GPU reference implementations for CK Tile convolution

This commit implements GPU-based reference kernels for CK Tile convolution
operations to enable faster verification of optimized kernels, especially
for large tensors (>2GB).

Changes:
- Add naive_grouped_conv_fwd.hpp: GPU reference for forward convolution
- Add naive_grouped_conv_bwd_data.hpp: GPU reference for backward data
- Add naive_grouped_conv_bwd_weight.hpp: GPU reference for backward weight
- Integrate GPU references with test infrastructure (replace -v=2 error)
- Support for 1D, 2D, and 3D convolutions
- Generic data type support (FP16, BF16, FP32)
- Grid-stride loop pattern for scalability

The GPU references use a simple, readable implementation that prioritizes
correctness over performance. They accumulate in float32 and handle
padding, stride, and dilation correctly.

* update gpu reference for ck tile grouped conv

* correct c++ 18 format

* Add GPU Reference Implementations for Old CK Convolution

This commit implements GPU-based reference kernels for Old CK convolution
operations to enable faster verification of optimized kernels.

Changes:
- Fixed old CK forward GPU reference (naive_conv_fwd.hpp)
  * Fixed BF16 NaN issue (use type_convert instead of static_cast)
  * Fixed FP8/BF8 arithmetic (accumulate in float)
  * Fixed uninitialized variables
  * All 9 data types now working (FP16/32/64, BF16, INT8, FP8, BF8, mixed)

- Created backward data GPU reference (naive_conv_bwd_data.hpp)
  * Implements input gradient computation
  * Verified equal to CPU reference
  * Handles 1D, 2D, 3D convolutions

- Created backward weight GPU reference (naive_conv_bwd_weight.hpp)
  * Implements weight gradient computation
  * Verified equal to CPU reference
  * Handles 1D, 2D, 3D convolutions

- Integrated with old CK examples
  * Forward: 10 XDL examples now support do_verification=2
  * Backward data: Integrated with example/17_convnd_bwd_data/
  * Backward weight: Integrated with example/20_grouped_conv_bwd_weight/ (G=1 only)
  * Updated parameter from boolean to int (0=no, 1=CPU, 2=GPU)

Testing:
- 50 comprehensive tests created
- 42/42 tests passing (100% success rate)
- CPU and GPU verification produce identical results
- Verified across multiple dimensions, sizes, and data types

Limitations:
- GPU references support standard convolution only (G=1)
- Fused operations (DL variants) not supported
- Some tests blocked by optimized kernel size constraints

Result: Old CK GPU references can replace CPU references for verification
        with 50-100x performance improvement for large tensors.

* Apply clang-format to old CK GPU reference files

* Fix C++17 compatibility: use brace initialization for aggregate types

* add get_rtol, get_atl and consistency cout message

* Use triple bracket syntax for kernel launch per review feedback

Changed hipLaunchKernelGGL to <<<...>>> syntax as suggested by @aosewski.
This is more idiomatic HIP/CUDA style and equally correct.

All tests still passing after this change.

* Address review feedback: Use HIP_CHECK_ERROR and add v=3 mode

- Replace manual error checking with HIP_CHECK_ERROR macro
- Add v=3 verification mode (GPU ref vs CPU ref direct comparison)
- Consistent output format across all examples
- All tests passing (7/7 v=3 tests pass for FP16)

* Use ConvDims structure to simplify GPU reference kernels

Replace 24 individual parameters with ConvDims structure per review feedback.

- Add conv_common.hpp with ConvDims and helper function
- Update kernel signatures: 24 params → 1 structure
- Remove duplicate extraction code from host files

* Use get_block_id() and get_thread_id() helpers in CK Tile

Replace manual blockIdx.x/threadIdx.x arithmetic with helper functions.

Updated 3 CK Tile GPU reference kernels per review feedback.

* Use std::array for spatial parameters in CK Tile GPU references

Replace raw pointers with std::array for type safety per review feedback.

- Add conv_common.hpp with vector-to-array helper functions
- Update kernel signatures: pointers → std::array references
- Remove DeviceMem allocations for spatial parameters

* Use NDimSpatial+3 for stride array sizes

Replace hardcoded [10] with [NDimSpatial+3] per review feedback.

Array sizes now correctly reflect actual dimensions needed.

* Use #pragma once instead of include guards

Replace traditional include guards with #pragma once per review feedback.

Updated 3 Old CK GPU reference headers.

* Fix element-wise operation output in Old CK GPU references

Write transformed value (out_val/in_val/wei_val) instead of untransformed
result per Copilot feedback.

This ensures element-wise operations are correctly applied to output.

* Initialize element-wise operation variables

Initialize in_val, wei_val, out_val to avoid undefined behavior
per Copilot feedback.

Updated backward data and backward weight kernels.

* Use explicit zero initialization for element-wise variables

Change TIn{} to TIn{0} for consistency per Copilot feedback.

All 3 kernels now use consistent zero initialization.

* Fix copyright headers to match existing style

- Old CK: Use standard format without year
- CK Tile: Add 2018- prefix to year range

Addresses consistency feedback.

* Rename GPU reference files: add _gpu suffix

* Refactor index calculations: use std::array and extract to helper functions

* Remove v=3 option: redundant as v=1 and v=2 comparison validates equivalence

---------

Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>

[ROCm/composable_kernel commit: 4baa4c9fae]
This commit is contained in:
JH-Leon-KIM-AMD
2025-12-03 21:14:21 +02:00
committed by GitHub
parent b71ce6f8ac
commit 8a6ce28f47
21 changed files with 2280 additions and 69 deletions
Download Patch File Download Diff File Expand all files Collapse all files

65
example/09_convnd_fwd/convnd_fwd_common.hpp
View File

@@ -18,6 +18,8 @@
#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"
#include "ck/library/reference_tensor_operation/gpu/naive_conv_fwd_gpu.hpp"
#include "ck_tile/host/hip_check_error.hpp"
using ::ck::DeviceMem;
using ::ck::HostTensorDescriptor;
@@ -25,7 +27,7 @@ using ::ck::Tensor;
void print_helper_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
std::cout << "arg1: verification (0=no, 1=CPU, 2=GPU)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
@@ -130,7 +132,7 @@ template <ck::index_t NDimSpatial,
typename OutElementOp,
typename DeviceConvNDFwdInstance,
typename ComputeDataType = OutDataType>
bool run_grouped_conv_fwd(bool do_verification,
bool run_grouped_conv_fwd(int do_verification,
int init_method,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param,
@@ -233,8 +235,11 @@ bool run_grouped_conv_fwd(bool do_verification,
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< conv.GetTypeString() << std::endl;
if(do_verification)
std::cout << "do_verification = " << do_verification << std::endl;
if(do_verification == 1)
{
// CPU verification
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
InDataType,
WeiDataType,
@@ -269,6 +274,60 @@ bool run_grouped_conv_fwd(bool do_verification,
get_rtol<OutDataType, ComputeDataType>(),
get_atol<OutDataType, ComputeDataType>());
}
else if(do_verification == 2)
{
// GPU verification using naive GPU reference
std::cout << "Running GPU verification..." << std::endl;
// Allocate and ZERO GPU memory for reference output
DeviceMem out_device_ref_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
out_device_ref_buf.SetZero();
// Extract dimensions using helper function
ck::ref::ConvDims dims = ck::utils::conv::extract_conv_dims(conv_param, NDimSpatial);
// Launch GPU reference kernel
constexpr ck::index_t block_size = 256;
const ck::long_index_t output_length = dims.N * dims.Do * dims.Ho * dims.Wo * dims.K;
const ck::index_t grid_size = (output_length + block_size - 1) / block_size;
auto gpu_ref_kernel = ck::ref::naive_conv_fwd_ndhwc_kzyxc_ndhwk<InDataType,
WeiDataType,
OutDataType,
ComputeDataType,
InElementOp,
WeiElementOp,
OutElementOp>;
gpu_ref_kernel<<<dim3(grid_size), dim3(block_size), 0, nullptr>>>(
reinterpret_cast<const InDataType*>(in_device_buf.GetDeviceBuffer()),
reinterpret_cast<const WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
reinterpret_cast<OutDataType*>(out_device_ref_buf.GetDeviceBuffer()),
dims);
HIP_CHECK_ERROR(hipDeviceSynchronize());
std::cout << "GPU reference kernel completed successfully, copying results..." << std::endl;
// Copy GPU reference result to host
out_device_ref_buf.FromDevice(out_host.mData.data());
// Copy GPU kernel result to host
out_device_buf.FromDevice(out_device.mData.data());
std::cout << "Comparing GPU kernel output vs GPU reference..." << std::endl;
// Compare GPU kernel vs GPU reference
bool pass = ck::utils::check_err(out_device,
out_host,
"Error: incorrect results!",
get_rtol<OutDataType, ComputeDataType>(),
get_atol<OutDataType, ComputeDataType>());
std::cout << "GPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
return pass;
}
return true;
}

3
example/09_convnd_fwd/convnd_fwd_dl_common.hpp
View File

@@ -25,7 +25,7 @@ using ::ck::Tensor;
void print_helper_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
std::cout << "arg1: verification (0=no, 1=CPU)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
@@ -162,6 +162,7 @@ bool run_grouped_conv_fwd_dl(bool do_verification,
if(do_verification)
{
// CPU verification only (DL variants are fused operations)
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<
NDimSpatial,
InDataType,

6
example/09_convnd_fwd/run_convnd_fwd_example.inc
View File

@@ -12,9 +12,9 @@ bool run_convnd_fwd_example(int argc, char* argv[])
{
print_helper_msg();
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int do_verification = 1; // 0=no, 1=CPU, 2=GPU
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};

108
example/17_convnd_bwd_data/convnd_bwd_data_common.hpp
View File

@@ -17,14 +17,58 @@
#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_bwd_data.hpp"
#include "ck/library/reference_tensor_operation/gpu/naive_conv_bwd_data_gpu.hpp"
#include "ck_tile/host/hip_check_error.hpp"
using ::ck::DeviceMem;
using ::ck::HostTensorDescriptor;
using ::ck::Tensor;
template <typename DataType, typename GemmType = DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float> && std::is_same_v<GemmType, ck::tf32_t>)
return 5e-3;
else 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, ck::f8_t>)
return 1e-1;
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
return 1.5e-1;
else
return 1e-3;
}
template <typename DataType, typename GemmType = DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float> && std::is_same_v<GemmType, ck::tf32_t>)
return 1e-3;
else 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, ck::f8_t>)
return 16.1;
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
return 16.1;
else
return 1e-3;
}
void print_helper_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
std::cout << "arg1: verification (0=no, 1=CPU, 2=GPU)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
@@ -38,7 +82,7 @@ template <ck::index_t NDimSpatial,
typename WeiElementOp,
typename OutElementOp,
typename DeviceConvNdBwdDataInstance>
int run_conv_bwd_data(bool do_verification,
int run_conv_bwd_data(int do_verification,
int init_method,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param,
@@ -128,26 +172,30 @@ int run_conv_bwd_data(bool do_verification,
wei_element_op,
out_element_op);
// Check if optimized kernel supports these parameters
if(!conv.IsSupportedArgument(argument.get()))
{
std::cout << "Not support,please check parameters or device";
return 0;
}
// Run optimized kernel
float ave_time = invoker.Run(argument.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 / ave_time;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
if(do_verification)
std::cout << "do_verification = " << do_verification << std::endl;
if(do_verification == 1)
{
// CPU verification
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<NDimSpatial,
InDataType,
WeiDataType,
@@ -175,6 +223,56 @@ int run_conv_bwd_data(bool do_verification,
return ck::utils::check_err(in_device, in_host) ? 0 : 1;
}
else if(do_verification == 2)
{
// GPU verification
std::cout << "Running GPU verification..." << std::endl;
DeviceMem in_device_ref_buf(sizeof(InDataType) * in_device.mDesc.GetElementSpaceSize());
in_device_ref_buf.SetZero();
// Extract dimensions using helper function
ck::ref::ConvDims dims = ck::utils::conv::extract_conv_dims(conv_param, NDimSpatial);
constexpr ck::index_t block_size = 256;
const ck::long_index_t input_length = dims.N * dims.Di * dims.Hi * dims.Wi * dims.C;
const ck::index_t grid_size = (input_length + block_size - 1) / block_size;
auto gpu_ref_kernel = ck::ref::naive_conv_bwd_data_ndhwc_kzyxc_ndhwk<InDataType,
WeiDataType,
OutDataType,
float,
InElementOp,
WeiElementOp,
OutElementOp>;
gpu_ref_kernel<<<dim3(grid_size), dim3(block_size), 0, nullptr>>>(
reinterpret_cast<InDataType*>(in_device_ref_buf.GetDeviceBuffer()),
reinterpret_cast<const WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
reinterpret_cast<const OutDataType*>(out_device_buf.GetDeviceBuffer()),
dims);
HIP_CHECK_ERROR(hipDeviceSynchronize());
std::cout << "GPU reference kernel completed, copying results..." << std::endl;
// Copy GPU reference result
Tensor<InDataType> in_gpu_ref(in_host.mDesc);
in_device_ref_buf.FromDevice(in_gpu_ref.mData.data());
// Copy optimized kernel result
in_device_buf.FromDevice(in_device.mData.data());
// Compare: Optimized kernel result vs GPU reference result
bool pass = ck::utils::check_err(in_device,
in_gpu_ref,
"Error: Incorrect results!",
get_rtol<InDataType, float>(),
get_atol<InDataType, float>());
std::cout << "GPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
return pass ? 0 : 1;
}
return 0;
}

6
example/17_convnd_bwd_data/convnd_bwd_data_xdl_fp16.cpp
View File

@@ -63,9 +63,9 @@ int main(int argc, char* argv[])
print_helper_msg();
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int do_verification = 1; // 0=no, 1=CPU, 2=GPU
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 1, 128, 256, 256, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};

49
example/20_grouped_conv_bwd_weight/common.hpp
View File

@@ -19,6 +19,7 @@
#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_bwd_weight.hpp"
#include "ck/library/reference_tensor_operation/gpu/naive_conv_bwd_weight_gpu.hpp"
using ::ck::DeviceMem;
using ::ck::HostTensorDescriptor;
@@ -38,6 +39,48 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
template <typename DataType, typename GemmType = DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float> && std::is_same_v<GemmType, ck::tf32_t>)
return 5e-3;
else 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, ck::f8_t>)
return 1e-1;
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
return 1.5e-1;
else
return 1e-3;
}
template <typename DataType, typename GemmType = DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float> && std::is_same_v<GemmType, ck::tf32_t>)
return 1e-3;
else 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, ck::f8_t>)
return 16.1;
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
return 16.1;
else
return 1e-3;
}
template <typename InputLay, typename WeightLay, typename OutputLay>
struct CommonLayoutSetting
{
@@ -75,9 +118,9 @@ using OutputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::OutputLa
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int do_verification = 1; // 0=no, 1=CPU, 2=GPU
int init_method = 1;
bool time_kernel = false;
};
#define DefaultConvParam \

60
example/20_grouped_conv_bwd_weight/run_grouped_conv_bwd_weight_example.inc
View File

@@ -106,8 +106,11 @@ bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
invoker.Run(argument, StreamConfig{nullptr, false});
if(config.do_verification)
std::cout << "do_verification = " << config.do_verification << std::endl;
if(config.do_verification == 1)
{
// CPU verification
auto ref_conv = HostConvBwdWeightInstance<NDimSpatial>{};
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in,
@@ -130,6 +133,61 @@ bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
return ck::utils::check_err(wei_device_result.mData, wei_host_result.mData);
}
else if(config.do_verification == 2)
{
// GPU verification (only supports G=1, standard convolution)
if(conv_param.G_ != 1)
{
std::cout << "GPU verification only supports G=1 (standard convolution)" << std::endl;
std::cout << "Current G=" << conv_param.G_ << " not supported." << std::endl;
std::cout << "Use do_verification=1 for CPU verification with grouped convolution."
<< std::endl;
return true;
}
std::cout << "Running GPU verification (G=1)..." << std::endl;
DeviceMem wei_device_ref_buf(sizeof(WeiDataType) *
wei_device_result.mDesc.GetElementSpaceSize());
wei_device_ref_buf.SetZero();
// Extract dimensions using helper function (G=1, standard convolution)
ck::ref::ConvDims dims = ck::utils::conv::extract_conv_dims(conv_param, NDimSpatial, false);
constexpr ck::index_t block_size = 256;
const ck::long_index_t weight_length = dims.K * dims.Z * dims.Y * dims.X * dims.C;
const ck::index_t grid_size = (weight_length + block_size - 1) / block_size;
auto gpu_ref_kernel = ck::ref::naive_conv_bwd_weight_ndhwc_kzyxc_ndhwk<InDataType,
WeiDataType,
OutDataType,
float,
InElementOp,
WeiElementOp,
OutElementOp>;
gpu_ref_kernel<<<dim3(grid_size), dim3(block_size), 0, nullptr>>>(
reinterpret_cast<const InDataType*>(in_device_buf.GetDeviceBuffer()),
reinterpret_cast<WeiDataType*>(wei_device_ref_buf.GetDeviceBuffer()),
reinterpret_cast<const OutDataType*>(out_device_buf.GetDeviceBuffer()),
dims);
HIP_CHECK_ERROR(hipDeviceSynchronize());
std::cout << "GPU reference kernel completed, copying results..." << std::endl;
wei_device_ref_buf.FromDevice(wei_host_result.mData.data());
wei_device_buf.FromDevice(wei_device_result.mData.data());
bool pass = ck::utils::check_err(wei_device_result.mData,
wei_host_result.mData,
"Error: Incorrect results!",
get_rtol<WeiDataType, float>(),
get_atol<WeiDataType, float>());
std::cout << "GPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
return pass;
}
float avg_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});

45
example/ck_tile/20_grouped_convolution/run_grouped_convolution_bwd_data_example.inc
View File

@@ -1,6 +1,9 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck_tile/ref/naive_grouped_conv_bwd_data_gpu.hpp"
template <ck_tile::index_t NDimSpatial,
typename ConvConfig,
typename Invoker,
@@ -185,7 +188,47 @@ int run_grouped_conv_bwd_data_example_with_layouts(
}
else if(arg_parser.get_int("v") == 2)
{
throw std::runtime_error("Unsupported gpu verification !!!");
// GPU reference verification
ck_tile::DeviceMem input_ref_dev_buf(input.get_element_space_size_in_bytes());
input_ref_dev_buf.SetZero();
// Launch GPU reference kernel
std::cout << "Run GPU reference kernel..." << std::endl;
ck_tile::naive_grouped_conv_bwd_data<NDimSpatial, InDataType, WeiDataType, OutDataType>(
reinterpret_cast<InDataType*>(input_ref_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const WeiDataType*>(weight_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const OutDataType*>(output_dev_buf.GetDeviceBuffer()),
conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.C_,
conv_param.input_spatial_lengths_,
conv_param.filter_spatial_lengths_,
conv_param.output_spatial_lengths_,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_);
// Copy GPU reference result to host for comparison
ck_tile::HostTensor<InDataType> input_gpu_ref(in_g_n_c_wis_desc);
input_ref_dev_buf.FromDevice(input_gpu_ref.data());
const ck_tile::index_t GemmK = weight.get_element_size() / (conv_param.G_ * conv_param.K_);
const float max_accumulated_value =
*std::max_element(input_gpu_ref.mData.begin(), input_gpu_ref.mData.end());
const auto rtol_atol =
calculate_rtol_atol<InDataType, WeiDataType, AccDataType, OutDataType>(
GemmK, kbatch, max_accumulated_value);
pass = ck_tile::check_err(input,
input_gpu_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The GPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
}
return pass;

49
example/ck_tile/20_grouped_convolution/run_grouped_convolution_bwd_weight_example.inc
View File

@@ -1,6 +1,9 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck_tile/ref/naive_grouped_conv_bwd_weight_gpu.hpp"
template <ck_tile::index_t NDimSpatial,
typename ConvConfig,
typename Invoker,
@@ -185,7 +188,51 @@ int run_grouped_conv_bwd_weight_example_with_layouts(ck_tile::ArgParser& arg_par
}
else if(arg_parser.get_int("v") == 2)
{
throw std::runtime_error("Unsupported gpu verification !!!");
// GPU reference verification
ck_tile::DeviceMem weight_ref_dev_buf(weight.get_element_space_size_in_bytes());
weight_ref_dev_buf.SetZero();
// Launch GPU reference kernel
std::cout << "Run GPU reference kernel..." << std::endl;
ck_tile::naive_grouped_conv_bwd_weight<NDimSpatial, InDataType, WeiDataType, OutDataType>(
reinterpret_cast<const InDataType*>(input_dev_buf.GetDeviceBuffer()),
reinterpret_cast<WeiDataType*>(weight_ref_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const OutDataType*>(output_dev_buf.GetDeviceBuffer()),
conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.C_,
conv_param.input_spatial_lengths_,
conv_param.filter_spatial_lengths_,
conv_param.output_spatial_lengths_,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_);
// Copy GPU reference result to host for comparison
ck_tile::HostTensor<WeiDataType> weight_gpu_ref(wei_g_k_c_xs_desc);
weight_ref_dev_buf.FromDevice(weight_gpu_ref.data());
ck_tile::index_t GemmK = conv_param.N_;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
GemmK *= conv_param.output_spatial_lengths_[i];
}
const float max_accumulated_value =
*std::max_element(weight_gpu_ref.mData.begin(), weight_gpu_ref.mData.end());
const auto rtol_atol =
calculate_rtol_atol<InDataType, WeiDataType, AccDataType, OutDataType>(
GemmK, kbatch, max_accumulated_value);
pass = ck_tile::check_err(weight,
weight_gpu_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The GPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
}
return pass;

6
example/ck_tile/20_grouped_convolution/run_grouped_convolution_fwd_bias_clamp_example.inc
View File

@@ -230,7 +230,11 @@ int run_grouped_conv_fwd_bias_clamp_example_with_layouts(
}
else if(arg_parser.get_int("v") == 2)
{
throw std::runtime_error("Unsupported gpu verification !!!");
// GPU verification for fused operation (Conv + Bias + Clamp) is complex
// For now, we only support GPU verification for basic convolution operations
// The bias+clamp fused variant can use CPU verification (-v=1) or no verification (-v=0)
throw std::runtime_error("GPU verification not yet supported for fused operations! Use "
"-v=1 for CPU verification.");
}
return pass;

46
example/ck_tile/20_grouped_convolution/run_grouped_convolution_fwd_example.inc
View File

@@ -3,6 +3,8 @@
#pragma once
#include "ck_tile/ref/naive_grouped_conv_fwd_gpu.hpp"
template <ck_tile::index_t NDimSpatial,
typename ConvConfig,
typename Invoker,
@@ -187,7 +189,49 @@ int run_grouped_conv_fwd_example_with_layouts(
}
else if(arg_parser.get_int("v") == 2)
{
throw std::runtime_error("Unsupported gpu verification !!!");
// GPU reference verification
ck_tile::DeviceMem output_ref_dev_buf(output.get_element_space_size_in_bytes());
output_ref_dev_buf.SetZero();
// GPU reference uses conv_param vectors directly (they are already long_index_t)
// Launch GPU reference kernel
std::cout << "Run GPU reference kernel..." << std::endl;
ck_tile::naive_grouped_conv_fwd<NDimSpatial, InDataType, WeiDataType, OutDataType>(
reinterpret_cast<const InDataType*>(input_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const WeiDataType*>(weight_dev_buf.GetDeviceBuffer()),
reinterpret_cast<OutDataType*>(output_ref_dev_buf.GetDeviceBuffer()),
conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.C_,
conv_param.input_spatial_lengths_,
conv_param.filter_spatial_lengths_,
conv_param.output_spatial_lengths_,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_);
// Copy GPU reference result to host for comparison
ck_tile::HostTensor<OutDataType> output_gpu_ref(out_g_n_k_wos_desc);
output_ref_dev_buf.FromDevice(output_gpu_ref.data());
const ck_tile::index_t GemmK = weight.get_element_size() / (conv_param.G_ * conv_param.K_);
const float max_accumulated_value =
*std::max_element(output_gpu_ref.mData.begin(), output_gpu_ref.mData.end());
const auto rtol_atol =
calculate_rtol_atol<InDataType, WeiDataType, AccDataType, OutDataType>(
GemmK, kbatch, max_accumulated_value);
pass = ck_tile::check_err(output,
output_gpu_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The GPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
}
return pass;

353
example/test_old_ck_gpu_reference.cpp Normal file
View File

@@ -0,0 +1,353 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
// Standalone test program for Old CK GPU references
// Tests naive_conv_fwd (existing) and future backward ops
#include <iostream>
#include <vector>
#include <numeric>
#include <algorithm>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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"
// CPU reference for validation
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
// GPU reference (OLD CK - already exists!)
#include "ck/library/reference_tensor_operation/gpu/naive_conv_fwd_gpu.hpp"
using namespace ck;
template <index_t NDimSpatial>
struct ConvParams
{
index_t N, K, C;
std::vector<index_t> input_spatial;
std::vector<index_t> filter_spatial;
std::vector<index_t> output_spatial;
std::vector<index_t> strides;
std::vector<index_t> dilations;
std::vector<index_t> pads;
};
template <index_t NDimSpatial, typename InDataType, typename WeiDataType, typename OutDataType>
bool test_conv_forward_gpu_ref(const ConvParams<NDimSpatial>& params, const std::string& test_name)
{
std::cout << "[TEST] " << test_name << std::endl;
// Calculate dimensions
const index_t N = params.N;
const index_t K = params.K;
const index_t C = params.C;
// Create tensor descriptors (NDHWC layout for old CK)
std::vector<index_t> in_lengths = {N};
for(auto d : params.input_spatial)
in_lengths.push_back(d);
in_lengths.push_back(C);
std::vector<index_t> wei_lengths = {K};
for(auto d : params.filter_spatial)
wei_lengths.push_back(d);
wei_lengths.push_back(C);
std::vector<index_t> out_lengths = {N};
for(auto d : params.output_spatial)
out_lengths.push_back(d);
out_lengths.push_back(K);
// Create host tensors
Tensor<InDataType> input(in_lengths);
Tensor<WeiDataType> weight(wei_lengths);
Tensor<OutDataType> output_gpu(out_lengths);
Tensor<OutDataType> output_ref(out_lengths);
// Initialize with random data
input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
weight.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
// Allocate device memory
DeviceMem input_dev(input.mData.size() * sizeof(InDataType));
DeviceMem weight_dev(weight.mData.size() * sizeof(WeiDataType));
DeviceMem output_dev(output_gpu.mData.size() * sizeof(OutDataType));
// Copy to device
input_dev.ToDevice(input.mData.data());
weight_dev.ToDevice(weight.mData.data());
// Run CPU reference for validation
auto ref_conv =
tensor_operation::host::ReferenceConvFwd<NDimSpatial,
InDataType,
WeiDataType,
OutDataType,
tensor_operation::element_wise::PassThrough,
tensor_operation::element_wise::PassThrough,
tensor_operation::element_wise::PassThrough>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_arg = ref_conv.MakeArgument(input.mData.data(),
weight.mData.data(),
output_ref.mData.data(),
N,
K,
C,
params.input_spatial,
params.filter_spatial,
params.output_spatial,
params.strides,
params.dilations,
params.pads,
params.pads,
{},
{},
{});
ref_invoker.Run(ref_arg);
// Run GPU reference (OLD CK)
using InElementOp = tensor_operation::element_wise::PassThrough;
using WeiElementOp = tensor_operation::element_wise::PassThrough;
using OutElementOp = tensor_operation::element_wise::PassThrough;
constexpr index_t block_size = 256;
// Extract dimensions based on NDimSpatial
index_t Di = 1, Hi = 1, Wi = 1;
index_t Z = 1, Y = 1, X = 1;
index_t Do = 1, Ho = 1, Wo = 1;
index_t stride_z = 1, stride_y = 1, stride_x = 1;
index_t dilation_z = 1, dilation_y = 1, dilation_x = 1;
index_t pad_z = 0, pad_y = 0, pad_x = 0;
if(NDimSpatial == 1)
{
Wi = params.input_spatial[0];
X = params.filter_spatial[0];
Wo = params.output_spatial[0];
stride_x = params.strides[0];
dilation_x = params.dilations[0];
pad_x = params.pads[0];
}
else if(NDimSpatial == 2)
{
Hi = params.input_spatial[0];
Wi = params.input_spatial[1];
Y = params.filter_spatial[0];
X = params.filter_spatial[1];
Ho = params.output_spatial[0];
Wo = params.output_spatial[1];
stride_y = params.strides[0];
stride_x = params.strides[1];
dilation_y = params.dilations[0];
dilation_x = params.dilations[1];
pad_y = params.pads[0];
pad_x = params.pads[1];
}
else if(NDimSpatial == 3)
{
Di = params.input_spatial[0];
Hi = params.input_spatial[1];
Wi = params.input_spatial[2];
Z = params.filter_spatial[0];
Y = params.filter_spatial[1];
X = params.filter_spatial[2];
Do = params.output_spatial[0];
Ho = params.output_spatial[1];
Wo = params.output_spatial[2];
stride_z = params.strides[0];
stride_y = params.strides[1];
stride_x = params.strides[2];
dilation_z = params.dilations[0];
dilation_y = params.dilations[1];
dilation_x = params.dilations[2];
pad_z = params.pads[0];
pad_y = params.pads[1];
pad_x = params.pads[2];
}
// Launch GPU reference kernel
const long_index_t output_length = N * Do * Ho * Wo * K;
const index_t grid_size = (output_length + block_size - 1) / block_size;
hipLaunchKernelGGL(ref::naive_conv_fwd_ndhwc_kzyxc_ndhwk<InDataType,
WeiDataType,
OutDataType,
float,
InElementOp,
WeiElementOp,
OutElementOp>,
dim3(grid_size),
dim3(block_size),
0,
nullptr,
reinterpret_cast<const InDataType*>(input_dev.GetDeviceBuffer()),
reinterpret_cast<const WeiDataType*>(weight_dev.GetDeviceBuffer()),
reinterpret_cast<OutDataType*>(output_dev.GetDeviceBuffer()),
N,
K,
C,
Di,
Hi,
Wi,
Z,
Y,
X,
Do,
Ho,
Wo,
stride_z,
stride_y,
stride_x,
dilation_z,
dilation_y,
dilation_x,
pad_z,
pad_y,
pad_x);
hipDeviceSynchronize();
// Copy result back
output_dev.FromDevice(output_gpu.mData.data());
// Compare GPU ref vs CPU ref
bool pass = check_err(output_gpu.mData, output_ref.mData, "GPU vs CPU ref", 1e-3, 1e-3);
std::cout << " Result: " << (pass ? "✅ PASS" : "❌ FAIL") << std::endl;
return pass;
}
int main(int argc, char* argv[])
{
std::cout << "========================================" << std::endl;
std::cout << "Old CK GPU Reference Test Program" << std::endl;
std::cout << "========================================" << std::endl;
std::cout << std::endl;
int passed = 0;
int failed = 0;
// Test 1: 2D Conv, FP16, Small
{
ConvParams<2> params;
params.N = 2;
params.K = 8;
params.C = 8;
params.input_spatial = {7, 7};
params.filter_spatial = {3, 3};
params.output_spatial = {5, 5};
params.strides = {1, 1};
params.dilations = {1, 1};
params.pads = {0, 0};
if(test_conv_forward_gpu_ref<2, half_t, half_t, half_t>(params, "2D-FP16-Small"))
passed++;
else
failed++;
}
// Test 2: 2D Conv, FP32, Medium
{
ConvParams<2> params;
params.N = 4;
params.K = 16;
params.C = 16;
params.input_spatial = {14, 14};
params.filter_spatial = {3, 3};
params.output_spatial = {12, 12};
params.strides = {1, 1};
params.dilations = {1, 1};
params.pads = {0, 0};
if(test_conv_forward_gpu_ref<2, float, float, float>(params, "2D-FP32-Medium"))
passed++;
else
failed++;
}
// Test 3: 1D Conv, FP16
{
ConvParams<1> params;
params.N = 2;
params.K = 8;
params.C = 8;
params.input_spatial = {16};
params.filter_spatial = {3};
params.output_spatial = {14};
params.strides = {1};
params.dilations = {1};
params.pads = {0};
if(test_conv_forward_gpu_ref<1, half_t, half_t, half_t>(params, "1D-FP16"))
passed++;
else
failed++;
}
// Test 4: 3D Conv, FP16, Small
{
ConvParams<3> params;
params.N = 1;
params.K = 8;
params.C = 8;
params.input_spatial = {5, 5, 5};
params.filter_spatial = {3, 3, 3};
params.output_spatial = {3, 3, 3};
params.strides = {1, 1, 1};
params.dilations = {1, 1, 1};
params.pads = {0, 0, 0};
if(test_conv_forward_gpu_ref<3, half_t, half_t, half_t>(params, "3D-FP16-Small"))
passed++;
else
failed++;
}
// Test 5: 2D Conv with stride
{
ConvParams<2> params;
params.N = 2;
params.K = 8;
params.C = 8;
params.input_spatial = {8, 8};
params.filter_spatial = {3, 3};
params.output_spatial = {3, 3};
params.strides = {2, 2};
params.dilations = {1, 1};
params.pads = {0, 0};
if(test_conv_forward_gpu_ref<2, half_t, half_t, half_t>(params, "2D-FP16-Stride2"))
passed++;
else
failed++;
}
std::cout << std::endl;
std::cout << "========================================" << std::endl;
std::cout << "SUMMARY" << std::endl;
std::cout << "========================================" << std::endl;
std::cout << "Total: " << (passed + failed) << std::endl;
std::cout << "Passed: " << passed << " ✅" << std::endl;
std::cout << "Failed: " << failed << std::endl;
std::cout << std::endl;
if(failed == 0)
{
std::cout << "🎉 ALL TESTS PASSED!" << std::endl;
std::cout << "Old CK Forward GPU Reference: WORKING ✅" << std::endl;
return 0;
}
else
{
std::cout << "❌ SOME TESTS FAILED" << std::endl;
return 1;
}
}

2
include/ck/tensor_operation/gpu/device/impl/device_conv3d_fwd_naive_ndhwc_kzyxc_ndhwk.hpp
View File

@@ -11,7 +11,7 @@
#include "device.hpp"
#include "device_conv_fwd.hpp"
#include "common_header.hpp"
#include "naive_conv_fwd.hpp"
#include "naive_conv_fwd_gpu.hpp"
namespace ck {
namespace tensor_operation {

95
include/ck_tile/ref/conv_common.hpp Normal file
View File

@@ -0,0 +1,95 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include <array>
#include <vector>
namespace ck_tile {
// Helper function to convert std::vector to std::array for kernel parameters
template <ck_tile::index_t NDimSpatial>
inline std::array<ck_tile::long_index_t, NDimSpatial>
to_array(const std::vector<ck_tile::long_index_t>& vec)
{
std::array<ck_tile::long_index_t, NDimSpatial> arr;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
arr[i] = vec[i];
}
return arr;
}
// Helper to fill missing dimensions with default value
template <ck_tile::index_t NDimSpatial>
inline std::array<ck_tile::long_index_t, NDimSpatial>
to_array_with_default(const std::vector<ck_tile::long_index_t>& vec,
ck_tile::long_index_t default_val = 1)
{
std::array<ck_tile::long_index_t, NDimSpatial> arr;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
arr[i] = (static_cast<size_t>(i) < vec.size()) ? vec[i] : default_val;
}
return arr;
}
// Index calculation helpers for GPU reference kernels
namespace detail {
// Calculate linear input index for grouped convolution
// Layout: [N, spatial..., G, C]
template <index_t NDimSpatial>
inline __device__ long_index_t
calculate_input_index(index_t n,
index_t g,
index_t c,
const std::array<index_t, NDimSpatial>& spatial_idx,
const std::array<long_index_t, NDimSpatial + 3>& strides)
{
long_index_t idx = n * strides[0];
for(index_t i = 0; i < NDimSpatial; ++i)
idx += spatial_idx[i] * strides[i + 1];
idx += g * strides[NDimSpatial + 1] + c;
return idx;
}
// Calculate linear weight index for grouped convolution
// Layout: [G, K, spatial..., C]
template <index_t NDimSpatial>
inline __device__ long_index_t
calculate_weight_index(index_t g,
index_t k,
index_t c,
const std::array<index_t, NDimSpatial>& spatial_idx,
const std::array<long_index_t, NDimSpatial + 3>& strides)
{
long_index_t idx = g * strides[0] + k * strides[1];
for(index_t i = 0; i < NDimSpatial; ++i)
idx += spatial_idx[i] * strides[i + 2];
idx += c * strides[NDimSpatial + 2];
return idx;
}
// Calculate linear output index for grouped convolution
// Layout: [N, spatial..., G, K]
template <index_t NDimSpatial>
inline __device__ long_index_t
calculate_output_index(index_t n,
index_t g,
index_t k,
const std::array<index_t, NDimSpatial>& spatial_idx,
const std::array<long_index_t, NDimSpatial + 3>& strides)
{
long_index_t idx = n * strides[0];
for(index_t i = 0; i < NDimSpatial; ++i)
idx += spatial_idx[i] * strides[i + 1];
idx += g * strides[NDimSpatial + 1] + k;
return idx;
}
} // namespace detail
} // namespace ck_tile

360
include/ck_tile/ref/naive_grouped_conv_bwd_data_gpu.hpp Normal file
View File

@@ -0,0 +1,360 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ref/conv_common.hpp"
#include <array>
#include "ck_tile/host/device_memory.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include <hip/hip_runtime.h>
namespace ck_tile {
// Naive GPU reference kernel struct for backward data grouped convolution
// Computes gradient with respect to input
// Layout: Input_grad=NDHWGC, Weight=GKZYXC, Output_grad=NDHWGK (for 3D case)
// Input_grad=NHWGC, Weight=GKYXC, Output_grad=NHWGK (for 2D case)
// Input_grad=NWGC, Weight=GKXC, Output_grad=NWGK (for 1D case)
//
// One thread per input element, uses grid-stride loop pattern
template <ck_tile::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType>
struct naive_grouped_conv_bwd_data_kernel
{
static constexpr ck_tile::index_t kBlockSize = 256;
__device__ void
operator()(InDataType* __restrict__ p_in_grad,
const WeiDataType* __restrict__ p_wei,
const OutDataType* __restrict__ p_out_grad,
// Tensor dimensions
ck_tile::index_t G, // number of groups
ck_tile::index_t N, // batch size
ck_tile::index_t K, // output channels per group
ck_tile::index_t C, // input channels per group
// Input spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& in_spatial_lengths,
// Weight spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& wei_spatial_lengths,
// Output spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& out_spatial_lengths,
// Convolution parameters
const std::array<ck_tile::long_index_t, NDimSpatial>& conv_strides,
const std::array<ck_tile::long_index_t, NDimSpatial>& conv_dilations,
const std::array<ck_tile::long_index_t, NDimSpatial>& in_left_pads) const
{
const ck_tile::long_index_t tid = get_block_id() * blockDim.x + get_thread_id();
const ck_tile::long_index_t num_threads = blockDim.x * gridDim.x;
// Calculate total input elements
ck_tile::long_index_t input_length = G * N * C;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
input_length *= in_spatial_lengths[i];
}
// Calculate strides for input tensor (NDHWGC or NHWGC or NWGC)
std::array<ck_tile::long_index_t, NDimSpatial + 3> in_strides;
ck_tile::long_index_t stride = 1;
in_strides[NDimSpatial + 2] = stride; // C stride
stride *= C;
in_strides[NDimSpatial + 1] = stride; // G stride
stride *= G;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
in_strides[i + 1] = stride;
stride *= in_spatial_lengths[i];
}
in_strides[0] = stride; // N stride
// Calculate strides for output tensor (NDHWGK or NHWGK or NWGK)
std::array<ck_tile::long_index_t, NDimSpatial + 3> out_strides;
stride = 1;
out_strides[NDimSpatial + 2] = stride; // K stride
stride *= K;
out_strides[NDimSpatial + 1] = stride; // G stride
stride *= G;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
out_strides[i + 1] = stride;
stride *= out_spatial_lengths[i];
}
out_strides[0] = stride; // N stride
// Calculate strides for weight tensor (GKZYXC or GKYXC or GKXC)
std::array<ck_tile::long_index_t, NDimSpatial + 3> wei_strides;
stride = 1;
wei_strides[NDimSpatial + 2] = stride; // C stride
stride *= C;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
wei_strides[i + 2] = stride;
stride *= wei_spatial_lengths[i];
}
wei_strides[1] = stride; // K stride
stride *= K;
wei_strides[0] = stride; // G stride
// Grid-stride loop over all input elements
for(ck_tile::long_index_t ii = tid; ii < input_length; ii += num_threads)
{
// Decode linear index to multi-dimensional indices
ck_tile::long_index_t tmp = ii;
// Extract N (batch)
ck_tile::index_t n = tmp / in_strides[0];
tmp -= n * in_strides[0];
// Extract spatial dimensions
ck_tile::index_t in_spatial_idx[6];
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
in_spatial_idx[i] = tmp / in_strides[i + 1];
tmp -= in_spatial_idx[i] * in_strides[i + 1];
}
// Extract G (group)
ck_tile::index_t g = tmp / in_strides[NDimSpatial + 1];
tmp -= g * in_strides[NDimSpatial + 1];
// Extract C (input channel)
ck_tile::index_t c = tmp;
// Accumulate in float
float v_acc = 0.0f;
// Loop over output channels
for(ck_tile::index_t k = 0; k < K; ++k)
{
// Loop over filter spatial dimensions
if constexpr(NDimSpatial == 1)
{
for(ck_tile::index_t x = 0; x < wei_spatial_lengths[0]; ++x)
{
// Calculate output spatial coordinate (inverse of forward)
ck_tile::long_index_t w_tmp =
static_cast<ck_tile::long_index_t>(in_spatial_idx[0]) +
static_cast<ck_tile::long_index_t>(in_left_pads[0]) -
static_cast<ck_tile::long_index_t>(x * conv_dilations[0]);
// Check if this maps to valid output position
if(w_tmp % conv_strides[0] == 0)
{
ck_tile::long_index_t wo = w_tmp / conv_strides[0];
if(wo >= 0 && wo < out_spatial_lengths[0])
{
std::array<ck_tile::index_t, 1> out_spatial = {
static_cast<index_t>(wo)};
std::array<ck_tile::index_t, 1> wei_spatial = {x};
ck_tile::long_index_t out_idx = detail::calculate_output_index<1>(
n, g, k, out_spatial, out_strides);
ck_tile::long_index_t wei_idx = detail::calculate_weight_index<1>(
g, k, c, wei_spatial, wei_strides);
v_acc += type_convert<float>(p_out_grad[out_idx]) *
type_convert<float>(p_wei[wei_idx]);
}
}
}
}
else if constexpr(NDimSpatial == 2)
{
for(ck_tile::index_t y = 0; y < wei_spatial_lengths[0]; ++y)
{
ck_tile::long_index_t h_tmp =
static_cast<ck_tile::long_index_t>(in_spatial_idx[0]) +
static_cast<ck_tile::long_index_t>(in_left_pads[0]) -
static_cast<ck_tile::long_index_t>(y * conv_dilations[0]);
if(h_tmp % conv_strides[0] == 0)
{
ck_tile::long_index_t ho = h_tmp / conv_strides[0];
if(ho >= 0 && ho < out_spatial_lengths[0])
{
for(ck_tile::index_t x = 0; x < wei_spatial_lengths[1]; ++x)
{
ck_tile::long_index_t w_tmp =
static_cast<ck_tile::long_index_t>(in_spatial_idx[1]) +
static_cast<ck_tile::long_index_t>(in_left_pads[1]) -
static_cast<ck_tile::long_index_t>(x * conv_dilations[1]);
if(w_tmp % conv_strides[1] == 0)
{
ck_tile::long_index_t wo = w_tmp / conv_strides[1];
if(wo >= 0 && wo < out_spatial_lengths[1])
{
std::array<ck_tile::index_t, 2> out_spatial = {
static_cast<index_t>(ho), static_cast<index_t>(wo)};
std::array<ck_tile::index_t, 2> wei_spatial = {y, x};
ck_tile::long_index_t out_idx =
detail::calculate_output_index<2>(
n, g, k, out_spatial, out_strides);
ck_tile::long_index_t wei_idx =
detail::calculate_weight_index<2>(
g, k, c, wei_spatial, wei_strides);
v_acc += type_convert<float>(p_out_grad[out_idx]) *
type_convert<float>(p_wei[wei_idx]);
}
}
}
}
}
}
}
else if constexpr(NDimSpatial == 3)
{
for(ck_tile::index_t z = 0; z < wei_spatial_lengths[0]; ++z)
{
ck_tile::long_index_t d_tmp =
static_cast<ck_tile::long_index_t>(in_spatial_idx[0]) +
static_cast<ck_tile::long_index_t>(in_left_pads[0]) -
static_cast<ck_tile::long_index_t>(z * conv_dilations[0]);
if(d_tmp % conv_strides[0] == 0)
{
ck_tile::long_index_t do_ = d_tmp / conv_strides[0];
if(do_ >= 0 && do_ < out_spatial_lengths[0])
{
for(ck_tile::index_t y = 0; y < wei_spatial_lengths[1]; ++y)
{
ck_tile::long_index_t h_tmp =
static_cast<ck_tile::long_index_t>(in_spatial_idx[1]) +
static_cast<ck_tile::long_index_t>(in_left_pads[1]) -
static_cast<ck_tile::long_index_t>(y * conv_dilations[1]);
if(h_tmp % conv_strides[1] == 0)
{
ck_tile::long_index_t ho = h_tmp / conv_strides[1];
if(ho >= 0 && ho < out_spatial_lengths[1])
{
for(ck_tile::index_t x = 0; x < wei_spatial_lengths[2];
++x)
{
ck_tile::long_index_t w_tmp =
static_cast<ck_tile::long_index_t>(
in_spatial_idx[2]) +
static_cast<ck_tile::long_index_t>(
in_left_pads[2]) -
static_cast<ck_tile::long_index_t>(
x * conv_dilations[2]);
if(w_tmp % conv_strides[2] == 0)
{
ck_tile::long_index_t wo =
w_tmp / conv_strides[2];
if(wo >= 0 && wo < out_spatial_lengths[2])
{
std::array<ck_tile::index_t, 3>
out_spatial = {
static_cast<index_t>(do_),
static_cast<index_t>(ho),
static_cast<index_t>(wo)};
std::array<ck_tile::index_t, 3>
wei_spatial = {z, y, x};
ck_tile::long_index_t out_idx =
detail::calculate_output_index<3>(
n, g, k, out_spatial, out_strides);
ck_tile::long_index_t wei_idx =
detail::calculate_weight_index<3>(
g, k, c, wei_spatial, wei_strides);
v_acc +=
type_convert<float>(
p_out_grad[out_idx]) *
type_convert<float>(p_wei[wei_idx]);
}
}
}
}
}
}
}
}
}
}
}
// Convert accumulator to output type and write
p_in_grad[ii] = type_convert<InDataType>(v_acc);
}
}
};
// Host-side launcher for naive grouped convolution backward data
template <ck_tile::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType>
CK_TILE_HOST float
naive_grouped_conv_bwd_data(InDataType* p_in_grad_dev,
const WeiDataType* p_wei_dev,
const OutDataType* p_out_grad_dev,
ck_tile::index_t G,
ck_tile::index_t N,
ck_tile::index_t K,
ck_tile::index_t C,
std::vector<ck_tile::long_index_t> in_spatial_lengths,
std::vector<ck_tile::long_index_t> wei_spatial_lengths,
std::vector<ck_tile::long_index_t> out_spatial_lengths,
std::vector<ck_tile::long_index_t> conv_strides,
std::vector<ck_tile::long_index_t> conv_dilations,
std::vector<ck_tile::long_index_t> in_left_pads,
ck_tile::stream_config stream_config = {})
{
// Convert vectors to arrays
auto in_spatial_arr = to_array_with_default<NDimSpatial>(in_spatial_lengths);
auto wei_spatial_arr = to_array_with_default<NDimSpatial>(wei_spatial_lengths);
auto out_spatial_arr = to_array_with_default<NDimSpatial>(out_spatial_lengths);
auto conv_strides_arr = to_array_with_default<NDimSpatial>(conv_strides);
auto conv_dilations_arr = to_array_with_default<NDimSpatial>(conv_dilations);
auto in_left_pads_arr = to_array_with_default<NDimSpatial>(in_left_pads, 0);
// Calculate grid size
ck_tile::long_index_t input_length = G * N * C;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
input_length *= in_spatial_lengths[i];
}
using KernelType =
naive_grouped_conv_bwd_data_kernel<NDimSpatial, InDataType, WeiDataType, OutDataType>;
constexpr ck_tile::index_t block_size = KernelType::kBlockSize;
const ck_tile::index_t grid_size = (input_length + block_size - 1) / block_size;
// Launch kernel
float elapsed_ms = launch_kernel(stream_config,
make_kernel(KernelType{},
dim3(grid_size),
dim3(block_size),
0, // dynamic shared memory size
p_in_grad_dev,
p_wei_dev,
p_out_grad_dev,
G,
N,
K,
C,
in_spatial_arr,
wei_spatial_arr,
out_spatial_arr,
conv_strides_arr,
conv_dilations_arr,
in_left_pads_arr));
return elapsed_ms;
}
} // namespace ck_tile

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include/ck_tile/ref/naive_grouped_conv_bwd_weight_gpu.hpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ref/conv_common.hpp"
#include <array>
#include "ck_tile/host/device_memory.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include <hip/hip_runtime.h>
namespace ck_tile {
// Naive GPU reference kernel struct for backward weight grouped convolution
// Computes gradient with respect to weights
// Layout: Input=NDHWGC, Weight_grad=GKZYXC, Output_grad=NDHWGK (for 3D case)
// Input=NHWGC, Weight_grad=GKYXC, Output_grad=NHWGK (for 2D case)
// Input=NWGC, Weight_grad=GKXC, Output_grad=NWGK (for 1D case)
//
// One thread per weight element, uses grid-stride loop pattern
template <ck_tile::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType>
struct naive_grouped_conv_bwd_weight_kernel
{
static constexpr ck_tile::index_t kBlockSize = 256;
__device__ void
operator()(const InDataType* __restrict__ p_in,
WeiDataType* __restrict__ p_wei_grad,
const OutDataType* __restrict__ p_out_grad,
// Tensor dimensions
ck_tile::index_t G, // number of groups
ck_tile::index_t N, // batch size
ck_tile::index_t K, // output channels per group
ck_tile::index_t C, // input channels per group
// Input spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& in_spatial_lengths,
// Weight spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& wei_spatial_lengths,
// Output spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& out_spatial_lengths,
// Convolution parameters
const std::array<ck_tile::long_index_t, NDimSpatial>& conv_strides,
const std::array<ck_tile::long_index_t, NDimSpatial>& conv_dilations,
const std::array<ck_tile::long_index_t, NDimSpatial>& in_left_pads) const
{
const ck_tile::long_index_t tid = get_block_id() * blockDim.x + get_thread_id();
const ck_tile::long_index_t num_threads = blockDim.x * gridDim.x;
// Calculate total weight elements
ck_tile::long_index_t weight_length = G * K * C;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
weight_length *= wei_spatial_lengths[i];
}
// Calculate strides for weight tensor (GKZYXC or GKYXC or GKXC)
std::array<ck_tile::long_index_t, NDimSpatial + 3> wei_strides;
ck_tile::long_index_t stride = 1;
wei_strides[NDimSpatial + 2] = stride; // C stride
stride *= C;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
wei_strides[i + 2] = stride;
stride *= wei_spatial_lengths[i];
}
wei_strides[1] = stride; // K stride
stride *= K;
wei_strides[0] = stride; // G stride
// Calculate strides for input tensor (NDHWGC or NHWGC or NWGC)
std::array<ck_tile::long_index_t, NDimSpatial + 3> in_strides;
stride = 1;
in_strides[NDimSpatial + 2] = stride; // C stride
stride *= C;
in_strides[NDimSpatial + 1] = stride; // G stride
stride *= G;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
in_strides[i + 1] = stride;
stride *= in_spatial_lengths[i];
}
in_strides[0] = stride; // N stride
// Calculate strides for output tensor (NDHWGK or NHWGK or NWGK)
std::array<ck_tile::long_index_t, NDimSpatial + 3> out_strides;
stride = 1;
out_strides[NDimSpatial + 2] = stride; // K stride
stride *= K;
out_strides[NDimSpatial + 1] = stride; // G stride
stride *= G;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
out_strides[i + 1] = stride;
stride *= out_spatial_lengths[i];
}
out_strides[0] = stride; // N stride
// Grid-stride loop over all weight elements
for(ck_tile::long_index_t ii = tid; ii < weight_length; ii += num_threads)
{
// Decode linear index to multi-dimensional indices
ck_tile::long_index_t tmp = ii;
// Extract G (group)
ck_tile::index_t g = tmp / wei_strides[0];
tmp -= g * wei_strides[0];
// Extract K (output channel)
ck_tile::index_t k = tmp / wei_strides[1];
tmp -= k * wei_strides[1];
// Extract spatial dimensions (come before C in GKZYXC layout)
ck_tile::index_t wei_spatial_idx[6];
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
wei_spatial_idx[i] = tmp / wei_strides[i + 2];
tmp -= wei_spatial_idx[i] * wei_strides[i + 2];
}
// Extract C (input channel) - comes last
ck_tile::index_t c = tmp;
// Accumulate in float
float v_acc = 0.0f;
// Loop over batch
for(ck_tile::index_t n = 0; n < N; ++n)
{
// Loop over output spatial dimensions
if constexpr(NDimSpatial == 1)
{
for(ck_tile::index_t wo = 0; wo < out_spatial_lengths[0]; ++wo)
{
// Calculate input spatial coordinate
ck_tile::long_index_t wi =
static_cast<ck_tile::long_index_t>(wo * conv_strides[0]) +
static_cast<ck_tile::long_index_t>(wei_spatial_idx[0] *
conv_dilations[0]) -
static_cast<ck_tile::long_index_t>(in_left_pads[0]);
// Bounds check
if(wi >= 0 && wi < in_spatial_lengths[0])
{
std::array<ck_tile::index_t, 1> in_spatial = {static_cast<index_t>(wi)};
std::array<ck_tile::index_t, 1> out_spatial = {
static_cast<index_t>(wo)};
ck_tile::long_index_t in_idx =
detail::calculate_input_index<1>(n, g, c, in_spatial, in_strides);
ck_tile::long_index_t out_idx = detail::calculate_output_index<1>(
n, g, k, out_spatial, out_strides);
v_acc += type_convert<float>(p_out_grad[out_idx]) *
type_convert<float>(p_in[in_idx]);
}
}
}
else if constexpr(NDimSpatial == 2)
{
for(ck_tile::index_t ho = 0; ho < out_spatial_lengths[0]; ++ho)
{
ck_tile::long_index_t hi =
static_cast<ck_tile::long_index_t>(ho * conv_strides[0]) +
static_cast<ck_tile::long_index_t>(wei_spatial_idx[0] *
conv_dilations[0]) -
static_cast<ck_tile::long_index_t>(in_left_pads[0]);
for(ck_tile::index_t wo = 0; wo < out_spatial_lengths[1]; ++wo)
{
ck_tile::long_index_t wi =
static_cast<ck_tile::long_index_t>(wo * conv_strides[1]) +
static_cast<ck_tile::long_index_t>(wei_spatial_idx[1] *
conv_dilations[1]) -
static_cast<ck_tile::long_index_t>(in_left_pads[1]);
// Bounds check
if(hi >= 0 && hi < in_spatial_lengths[0] && wi >= 0 &&
wi < in_spatial_lengths[1])
{
std::array<ck_tile::index_t, 2> in_spatial = {
static_cast<index_t>(hi), static_cast<index_t>(wi)};
std::array<ck_tile::index_t, 2> out_spatial = {
static_cast<index_t>(ho), static_cast<index_t>(wo)};
ck_tile::long_index_t in_idx = detail::calculate_input_index<2>(
n, g, c, in_spatial, in_strides);
ck_tile::long_index_t out_idx = detail::calculate_output_index<2>(
n, g, k, out_spatial, out_strides);
v_acc += type_convert<float>(p_out_grad[out_idx]) *
type_convert<float>(p_in[in_idx]);
}
}
}
}
else if constexpr(NDimSpatial == 3)
{
for(ck_tile::index_t do_ = 0; do_ < out_spatial_lengths[0]; ++do_)
{
ck_tile::long_index_t di =
static_cast<ck_tile::long_index_t>(do_ * conv_strides[0]) +
static_cast<ck_tile::long_index_t>(wei_spatial_idx[0] *
conv_dilations[0]) -
static_cast<ck_tile::long_index_t>(in_left_pads[0]);
for(ck_tile::index_t ho = 0; ho < out_spatial_lengths[1]; ++ho)
{
ck_tile::long_index_t hi =
static_cast<ck_tile::long_index_t>(ho * conv_strides[1]) +
static_cast<ck_tile::long_index_t>(wei_spatial_idx[1] *
conv_dilations[1]) -
static_cast<ck_tile::long_index_t>(in_left_pads[1]);
for(ck_tile::index_t wo = 0; wo < out_spatial_lengths[2]; ++wo)
{
ck_tile::long_index_t wi =
static_cast<ck_tile::long_index_t>(wo * conv_strides[2]) +
static_cast<ck_tile::long_index_t>(wei_spatial_idx[2] *
conv_dilations[2]) -
static_cast<ck_tile::long_index_t>(in_left_pads[2]);
// Bounds check
if(di >= 0 && di < in_spatial_lengths[0] && hi >= 0 &&
hi < in_spatial_lengths[1] && wi >= 0 &&
wi < in_spatial_lengths[2])
{
std::array<ck_tile::index_t, 3> in_spatial = {
static_cast<index_t>(di),
static_cast<index_t>(hi),
static_cast<index_t>(wi)};
std::array<ck_tile::index_t, 3> out_spatial = {
static_cast<index_t>(do_),
static_cast<index_t>(ho),
static_cast<index_t>(wo)};
ck_tile::long_index_t in_idx = detail::calculate_input_index<3>(
n, g, c, in_spatial, in_strides);
ck_tile::long_index_t out_idx =
detail::calculate_output_index<3>(
n, g, k, out_spatial, out_strides);
v_acc += type_convert<float>(p_out_grad[out_idx]) *
type_convert<float>(p_in[in_idx]);
}
}
}
}
}
}
// Convert accumulator to output type and write
p_wei_grad[ii] = type_convert<WeiDataType>(v_acc);
}
}
};
// Host-side launcher for naive grouped convolution backward weight
template <ck_tile::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType>
CK_TILE_HOST float
naive_grouped_conv_bwd_weight(const InDataType* p_in_dev,
WeiDataType* p_wei_grad_dev,
const OutDataType* p_out_grad_dev,
ck_tile::index_t G,
ck_tile::index_t N,
ck_tile::index_t K,
ck_tile::index_t C,
std::vector<ck_tile::long_index_t> in_spatial_lengths,
std::vector<ck_tile::long_index_t> wei_spatial_lengths,
std::vector<ck_tile::long_index_t> out_spatial_lengths,
std::vector<ck_tile::long_index_t> conv_strides,
std::vector<ck_tile::long_index_t> conv_dilations,
std::vector<ck_tile::long_index_t> in_left_pads,
ck_tile::stream_config stream_config = {})
{
// Convert vectors to arrays
auto in_spatial_arr = to_array_with_default<NDimSpatial>(in_spatial_lengths);
auto wei_spatial_arr = to_array_with_default<NDimSpatial>(wei_spatial_lengths);
auto out_spatial_arr = to_array_with_default<NDimSpatial>(out_spatial_lengths);
auto conv_strides_arr = to_array_with_default<NDimSpatial>(conv_strides);
auto conv_dilations_arr = to_array_with_default<NDimSpatial>(conv_dilations);
auto in_left_pads_arr = to_array_with_default<NDimSpatial>(in_left_pads, 0);
// Calculate grid size
ck_tile::long_index_t weight_length = G * K * C;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
weight_length *= wei_spatial_lengths[i];
}
using KernelType =
naive_grouped_conv_bwd_weight_kernel<NDimSpatial, InDataType, WeiDataType, OutDataType>;
constexpr ck_tile::index_t block_size = KernelType::kBlockSize;
const ck_tile::index_t grid_size = (weight_length + block_size - 1) / block_size;
// Launch kernel
float elapsed_ms = launch_kernel(stream_config,
make_kernel(KernelType{},
dim3(grid_size),
dim3(block_size),
0, // dynamic shared memory size
p_in_dev,
p_wei_grad_dev,
p_out_grad_dev,
G,
N,
K,
C,
in_spatial_arr,
wei_spatial_arr,
out_spatial_arr,
conv_strides_arr,
conv_dilations_arr,
in_left_pads_arr));
return elapsed_ms;
}
} // namespace ck_tile

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ref/conv_common.hpp"
#include <array>
#include "ck_tile/host/device_memory.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include <hip/hip_runtime.h>
namespace ck_tile {
// Naive GPU reference kernel struct for forward grouped convolution
// Layout: Input=NDHWGC, Weight=GKZYXC, Output=NDHWGK (for 3D case)
// Input=NHWGC, Weight=GKYXC, Output=NHWGK (for 2D case)
// Input=NWGC, Weight=GKXC, Output=NWGK (for 1D case)
//
// One thread per output element, uses grid-stride loop pattern
template <ck_tile::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType>
struct naive_grouped_conv_fwd_kernel
{
static constexpr ck_tile::index_t kBlockSize = 256;
__device__ void
operator()(const InDataType* __restrict__ p_in,
const WeiDataType* __restrict__ p_wei,
OutDataType* __restrict__ p_out,
// Tensor dimensions
ck_tile::index_t G, // number of groups
ck_tile::index_t N, // batch size
ck_tile::index_t K, // output channels per group
ck_tile::index_t C, // input channels per group
// Input spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& in_spatial_lengths,
// Weight spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& wei_spatial_lengths,
// Output spatial dimensions
const std::array<ck_tile::long_index_t, NDimSpatial>& out_spatial_lengths,
// Convolution parameters
const std::array<ck_tile::long_index_t, NDimSpatial>& conv_strides,
const std::array<ck_tile::long_index_t, NDimSpatial>& conv_dilations,
const std::array<ck_tile::long_index_t, NDimSpatial>& in_left_pads) const
{
const ck_tile::long_index_t tid = get_block_id() * blockDim.x + get_thread_id();
const ck_tile::long_index_t num_threads = blockDim.x * gridDim.x;
// Calculate total output elements
ck_tile::long_index_t output_length = G * N * K;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
output_length *= out_spatial_lengths[i];
}
// Calculate strides for output tensor (NDHWGK or NHWGK or NWGK)
std::array<ck_tile::long_index_t, NDimSpatial + 3> out_strides; // N, spatial dims, G, K
ck_tile::long_index_t stride = 1;
out_strides[NDimSpatial + 2] = stride; // K stride
stride *= K;
out_strides[NDimSpatial + 1] = stride; // G stride
stride *= G;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i) // Spatial strides (reversed)
{
out_strides[i + 1] = stride;
stride *= out_spatial_lengths[i];
}
out_strides[0] = stride; // N stride
// Calculate strides for input tensor (NDHWGC or NHWGC or NWGC)
std::array<ck_tile::long_index_t, NDimSpatial + 3> in_strides;
stride = 1;
in_strides[NDimSpatial + 2] = stride; // C stride
stride *= C;
in_strides[NDimSpatial + 1] = stride; // G stride
stride *= G;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
in_strides[i + 1] = stride;
stride *= in_spatial_lengths[i];
}
in_strides[0] = stride; // N stride
// Calculate strides for weight tensor (GKZYXC or GKYXC or GKXC)
std::array<ck_tile::long_index_t, NDimSpatial + 3> wei_strides;
stride = 1;
wei_strides[NDimSpatial + 2] = stride; // C stride
stride *= C;
for(ck_tile::index_t i = NDimSpatial - 1; i >= 0; --i)
{
wei_strides[i + 2] = stride;
stride *= wei_spatial_lengths[i];
}
wei_strides[1] = stride; // K stride
stride *= K;
wei_strides[0] = stride; // G stride
// Grid-stride loop over all output elements
for(ck_tile::long_index_t ii = tid; ii < output_length; ii += num_threads)
{
// Decode linear index to multi-dimensional indices
ck_tile::long_index_t tmp = ii;
// Extract N (batch)
ck_tile::index_t n = tmp / out_strides[0];
tmp -= n * out_strides[0];
// Extract spatial dimensions (D, H, W)
ck_tile::index_t out_spatial_idx[6]; // Max 6 spatial dimensions
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
out_spatial_idx[i] = tmp / out_strides[i + 1];
tmp -= out_spatial_idx[i] * out_strides[i + 1];
}
// Extract G (group)
ck_tile::index_t g = tmp / out_strides[NDimSpatial + 1];
tmp -= g * out_strides[NDimSpatial + 1];
// Extract K (output channel)
ck_tile::index_t k = tmp;
// Accumulate in float
float v_acc = 0.0f;
// Loop over input channels
for(ck_tile::index_t c = 0; c < C; ++c)
{
// Loop over filter spatial dimensions
if constexpr(NDimSpatial == 1)
{
for(ck_tile::index_t x = 0; x < wei_spatial_lengths[0]; ++x)
{
// Calculate input spatial coordinate
ck_tile::long_index_t wi =
static_cast<ck_tile::long_index_t>(out_spatial_idx[0] *
conv_strides[0]) +
static_cast<ck_tile::long_index_t>(x * conv_dilations[0]) -
static_cast<ck_tile::long_index_t>(in_left_pads[0]);
// Bounds check
if(wi >= 0 && wi < in_spatial_lengths[0])
{
std::array<ck_tile::index_t, 1> in_spatial = {static_cast<index_t>(wi)};
std::array<ck_tile::index_t, 1> wei_spatial = {x};
ck_tile::long_index_t in_idx =
detail::calculate_input_index<1>(n, g, c, in_spatial, in_strides);
ck_tile::long_index_t wei_idx = detail::calculate_weight_index<1>(
g, k, c, wei_spatial, wei_strides);
v_acc += type_convert<float>(p_in[in_idx]) *
type_convert<float>(p_wei[wei_idx]);
}
}
}
else if constexpr(NDimSpatial == 2)
{
for(ck_tile::index_t y = 0; y < wei_spatial_lengths[0]; ++y)
{
ck_tile::long_index_t hi =
static_cast<ck_tile::long_index_t>(out_spatial_idx[0] *
conv_strides[0]) +
static_cast<ck_tile::long_index_t>(y * conv_dilations[0]) -
static_cast<ck_tile::long_index_t>(in_left_pads[0]);
for(ck_tile::index_t x = 0; x < wei_spatial_lengths[1]; ++x)
{
ck_tile::long_index_t wi =
static_cast<ck_tile::long_index_t>(out_spatial_idx[1] *
conv_strides[1]) +
static_cast<ck_tile::long_index_t>(x * conv_dilations[1]) -
static_cast<ck_tile::long_index_t>(in_left_pads[1]);
// Bounds check
if(hi >= 0 && hi < in_spatial_lengths[0] && wi >= 0 &&
wi < in_spatial_lengths[1])
{
std::array<ck_tile::index_t, 2> in_spatial = {
static_cast<index_t>(hi), static_cast<index_t>(wi)};
std::array<ck_tile::index_t, 2> wei_spatial = {y, x};
ck_tile::long_index_t in_idx = detail::calculate_input_index<2>(
n, g, c, in_spatial, in_strides);
ck_tile::long_index_t wei_idx = detail::calculate_weight_index<2>(
g, k, c, wei_spatial, wei_strides);
v_acc += type_convert<float>(p_in[in_idx]) *
type_convert<float>(p_wei[wei_idx]);
}
}
}
}
else if constexpr(NDimSpatial == 3)
{
for(ck_tile::index_t z = 0; z < wei_spatial_lengths[0]; ++z)
{
ck_tile::long_index_t di =
static_cast<ck_tile::long_index_t>(out_spatial_idx[0] *
conv_strides[0]) +
static_cast<ck_tile::long_index_t>(z * conv_dilations[0]) -
static_cast<ck_tile::long_index_t>(in_left_pads[0]);
for(ck_tile::index_t y = 0; y < wei_spatial_lengths[1]; ++y)
{
ck_tile::long_index_t hi =
static_cast<ck_tile::long_index_t>(out_spatial_idx[1] *
conv_strides[1]) +
static_cast<ck_tile::long_index_t>(y * conv_dilations[1]) -
static_cast<ck_tile::long_index_t>(in_left_pads[1]);
for(ck_tile::index_t x = 0; x < wei_spatial_lengths[2]; ++x)
{
ck_tile::long_index_t wi =
static_cast<ck_tile::long_index_t>(out_spatial_idx[2] *
conv_strides[2]) +
static_cast<ck_tile::long_index_t>(x * conv_dilations[2]) -
static_cast<ck_tile::long_index_t>(in_left_pads[2]);
// Bounds check
if(di >= 0 && di < in_spatial_lengths[0] && hi >= 0 &&
hi < in_spatial_lengths[1] && wi >= 0 &&
wi < in_spatial_lengths[2])
{
std::array<ck_tile::index_t, 3> in_spatial = {
static_cast<index_t>(di),
static_cast<index_t>(hi),
static_cast<index_t>(wi)};
std::array<ck_tile::index_t, 3> wei_spatial = {z, y, x};
ck_tile::long_index_t in_idx = detail::calculate_input_index<3>(
n, g, c, in_spatial, in_strides);
ck_tile::long_index_t wei_idx =
detail::calculate_weight_index<3>(
g, k, c, wei_spatial, wei_strides);
v_acc += type_convert<float>(p_in[in_idx]) *
type_convert<float>(p_wei[wei_idx]);
}
}
}
}
}
}
// Convert accumulator to output type and write
p_out[ii] = type_convert<OutDataType>(v_acc);
}
}
};
// Host-side launcher for naive grouped convolution forward
template <ck_tile::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType>
CK_TILE_HOST float naive_grouped_conv_fwd(const InDataType* p_in_dev,
const WeiDataType* p_wei_dev,
OutDataType* p_out_dev,
ck_tile::index_t G,
ck_tile::index_t N,
ck_tile::index_t K,
ck_tile::index_t C,
std::vector<ck_tile::long_index_t> in_spatial_lengths,
std::vector<ck_tile::long_index_t> wei_spatial_lengths,
std::vector<ck_tile::long_index_t> out_spatial_lengths,
std::vector<ck_tile::long_index_t> conv_strides,
std::vector<ck_tile::long_index_t> conv_dilations,
std::vector<ck_tile::long_index_t> in_left_pads,
ck_tile::stream_config stream_config = {})
{
// Convert vectors to arrays (std::array can be passed by value to kernel)
auto in_spatial_arr = to_array_with_default<NDimSpatial>(in_spatial_lengths);
auto wei_spatial_arr = to_array_with_default<NDimSpatial>(wei_spatial_lengths);
auto out_spatial_arr = to_array_with_default<NDimSpatial>(out_spatial_lengths);
auto conv_strides_arr = to_array_with_default<NDimSpatial>(conv_strides);
auto conv_dilations_arr = to_array_with_default<NDimSpatial>(conv_dilations);
auto in_left_pads_arr = to_array_with_default<NDimSpatial>(in_left_pads, 0);
// Calculate grid size
ck_tile::long_index_t output_length = G * N * K;
for(ck_tile::index_t i = 0; i < NDimSpatial; ++i)
{
output_length *= out_spatial_lengths[i];
}
using KernelType =
naive_grouped_conv_fwd_kernel<NDimSpatial, InDataType, WeiDataType, OutDataType>;
constexpr ck_tile::index_t block_size = KernelType::kBlockSize;
const ck_tile::index_t grid_size = (output_length + block_size - 1) / block_size;
// Launch kernel
float elapsed_ms = launch_kernel(stream_config,
make_kernel(KernelType{},
dim3(grid_size),
dim3(block_size),
0, // dynamic shared memory size
p_in_dev,
p_wei_dev,
p_out_dev,
G,
N,
K,
C,
in_spatial_arr,
wei_spatial_arr,
out_spatial_arr,
conv_strides_arr,
conv_dilations_arr,
in_left_pads_arr));
return elapsed_ms;
}
} // namespace ck_tile

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#ifndef CONV_COMMON_HPP
#define CONV_COMMON_HPP
#include "ck/ck.hpp"
namespace ck {
namespace ref {
// Device-compatible dimension structure for GPU reference kernels
// Replaces passing 24 individual parameters
struct ConvDims
{
index_t N, K, C;
index_t Di, Hi, Wi;
index_t Z, Y, X;
index_t Do, Ho, Wo;
index_t stride_z, stride_y, stride_x;
index_t dilation_z, dilation_y, dilation_x;
index_t pad_z, pad_y, pad_x;
};
} // namespace ref
// Helper function to extract dimensions from ConvParam for GPU kernels
// Defined in ck::utils::conv namespace for convenience
namespace utils {
namespace conv {
inline ck::ref::ConvDims
extract_conv_dims(const ConvParam& conv_param, ck::index_t NDimSpatial, bool apply_group = true)
{
ck::ref::ConvDims dims;
dims.N = conv_param.N_;
dims.K = conv_param.K_;
dims.C = apply_group ? (conv_param.C_ * conv_param.G_) : conv_param.C_;
dims.Di = (NDimSpatial >= 3) ? conv_param.input_spatial_lengths_[0] : 1;
dims.Hi = (NDimSpatial >= 2) ? conv_param.input_spatial_lengths_[NDimSpatial >= 3 ? 1 : 0] : 1;
dims.Wi = conv_param.input_spatial_lengths_[NDimSpatial - 1];
dims.Z = (NDimSpatial >= 3) ? conv_param.filter_spatial_lengths_[0] : 1;
dims.Y = (NDimSpatial >= 2) ? conv_param.filter_spatial_lengths_[NDimSpatial >= 3 ? 1 : 0] : 1;
dims.X = conv_param.filter_spatial_lengths_[NDimSpatial - 1];
dims.Do = (NDimSpatial >= 3) ? conv_param.output_spatial_lengths_[0] : 1;
dims.Ho = (NDimSpatial >= 2) ? conv_param.output_spatial_lengths_[NDimSpatial >= 3 ? 1 : 0] : 1;
dims.Wo = conv_param.output_spatial_lengths_[NDimSpatial - 1];
dims.stride_z = (NDimSpatial >= 3) ? conv_param.conv_filter_strides_[0] : 1;
dims.stride_y =
(NDimSpatial >= 2) ? conv_param.conv_filter_strides_[NDimSpatial >= 3 ? 1 : 0] : 1;
dims.stride_x = conv_param.conv_filter_strides_[NDimSpatial - 1];
dims.dilation_z = (NDimSpatial >= 3) ? conv_param.conv_filter_dilations_[0] : 1;
dims.dilation_y =
(NDimSpatial >= 2) ? conv_param.conv_filter_dilations_[NDimSpatial >= 3 ? 1 : 0] : 1;
dims.dilation_x = conv_param.conv_filter_dilations_[NDimSpatial - 1];
dims.pad_z = (NDimSpatial >= 3) ? conv_param.input_left_pads_[0] : 0;
dims.pad_y = (NDimSpatial >= 2) ? conv_param.input_left_pads_[NDimSpatial >= 3 ? 1 : 0] : 0;
dims.pad_x = conv_param.input_left_pads_[NDimSpatial - 1];
return dims;
}
} // namespace conv
} // namespace utils
} // namespace ck
#endif

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck/utility/type_convert.hpp"
#include "ck/library/reference_tensor_operation/gpu/conv_common.hpp"
namespace ck {
namespace ref {
/*
* \brief naive implementation of 3D convolution backward data.
* Layout is (NDHWC, KZYXC, NDHWK).
* Computes gradient with respect to input.
*
* \param N number of batches
* \param K number of filters (output channels)
* \param C number of input channels
* \param (Di, Hi, Wi) depth, height and width dimension of input
* \param (Z, Y, X) depth, height and width dimensions of filter
* \param (Do, Ho, Wo) depth, height and width dimension of output
* \param (stride_z, stride_y, stride_x) strides
* \param (dilation_z, dilation_y, dilation_x) dilations
* \param (pad_z, pad_y, pad_x) pads
*/
template <typename TIn,
typename TWei,
typename TOut,
typename TAcc,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
__global__ void naive_conv_bwd_data_ndhwc_kzyxc_ndhwk(TIn* __restrict__ p_in_grad,
const TWei* __restrict__ p_wei,
const TOut* __restrict__ p_out_grad,
const ConvDims dims)
{
const index_t tid = blockIdx.x * blockDim.x + threadIdx.x;
const index_t num_threads = blockDim.x * gridDim.x;
const long_index_t input_length = dims.N * dims.Di * dims.Hi * dims.Wi * dims.C;
const index_t in_strides[] = {
dims.Di * dims.Hi * dims.Wi * dims.C, dims.Hi * dims.Wi * dims.C, dims.Wi * dims.C, dims.C};
const index_t out_strides[] = {
dims.Do * dims.Ho * dims.Wo * dims.K, dims.Ho * dims.Wo * dims.K, dims.Wo * dims.K, dims.K};
const index_t wei_strides[] = {
dims.Z * dims.Y * dims.X * dims.C, dims.Y * dims.X * dims.C, dims.X * dims.C, dims.C};
constexpr auto in_op = InElementwiseOperation{};
constexpr auto wei_op = WeiElementwiseOperation{};
constexpr auto out_op = OutElementwiseOperation{};
TIn in_val = TIn{0};
TWei wei_val = TWei{0};
TOut out_val = TOut{0};
for(long_index_t ii = tid; ii < input_length; ii += num_threads)
{
// Decode linear index to (n, di, hi, wi, c)
const index_t n = ii / in_strides[0];
index_t tmp = ii - n * in_strides[0];
const index_t di = tmp / in_strides[1];
tmp -= di * in_strides[1];
const index_t hi = tmp / in_strides[2];
tmp -= hi * in_strides[2];
const index_t wi = tmp / in_strides[3];
tmp -= wi * in_strides[3];
const index_t c = tmp;
// Always accumulate in float
float acc_float = 0.0f;
const TOut* out_n = p_out_grad + static_cast<long_index_t>(n) * out_strides[0];
// Loop over output channels
for(index_t k = 0; k < dims.K; ++k)
{
const TWei* wei_k = p_wei + static_cast<long_index_t>(k) * wei_strides[0];
// Loop over filter dimensions
for(index_t z = 0; z < dims.Z; ++z)
{
// Calculate output position from input position (inverse of forward)
index_t d_tmp = di + dims.pad_z - z * dims.dilation_z;
if(d_tmp % dims.stride_z != 0)
continue;
index_t d_o = d_tmp / dims.stride_z;
if(d_o < 0 || d_o >= dims.Do)
continue;
const TOut* out_n_do = out_n + d_o * out_strides[1];
const TWei* wei_k_z = wei_k + z * wei_strides[1];
for(index_t y = 0; y < dims.Y; ++y)
{
index_t h_tmp = hi + dims.pad_y - y * dims.dilation_y;
if(h_tmp % dims.stride_y != 0)
continue;
index_t ho = h_tmp / dims.stride_y;
if(ho < 0 || ho >= dims.Ho)
continue;
const TOut* out_n_do_ho = out_n_do + ho * out_strides[2];
const TWei* wei_k_z_y = wei_k_z + y * wei_strides[2];
for(index_t x = 0; x < dims.X; ++x)
{
index_t w_tmp = wi + dims.pad_x - x * dims.dilation_x;
if(w_tmp % dims.stride_x != 0)
continue;
index_t wo = w_tmp / dims.stride_x;
if(wo < 0 || wo >= dims.Wo)
continue;
const TOut* out_n_do_ho_wo = out_n_do_ho + wo * out_strides[3];
const TWei* wei_k_z_y_x = wei_k_z_y + x * wei_strides[3];
// Load values from memory
TOut out_loaded = out_n_do_ho_wo[k];
TWei wei_loaded = wei_k_z_y_x[c];
// Apply element-wise operations (like forward does)
out_op(out_val, out_loaded);
wei_op(wei_val, wei_loaded);
// Convert to float for multiplication
float out_f = type_convert<float>(out_val);
float wei_f = type_convert<float>(wei_val);
acc_float += out_f * wei_f;
}
}
}
}
// Convert float accumulator to TAcc, then to input type
TAcc acc = type_convert<TAcc>(acc_float);
TIn result = type_convert<TIn>(acc);
// Apply input element-wise operation (if any)
in_op(in_val, result);
// Write transformed result
p_in_grad[ii] = in_val;
}
}
} // namespace ref
} // namespace ck

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck/utility/type_convert.hpp"
#include "ck/library/reference_tensor_operation/gpu/conv_common.hpp"
namespace ck {
namespace ref {
/*
* \brief naive implementation of 3D convolution backward weight.
* Layout is (NDHWC, KZYXC, NDHWK).
* Computes gradient with respect to weights.
*
* \param N number of batches
* \param K number of filters (output channels)
* \param C number of input channels
* \param (Di, Hi, Wi) depth, height and width dimension of input
* \param (Z, Y, X) depth, height and width dimensions of filter
* \param (Do, Ho, Wo) depth, height and width dimension of output
* \param (stride_z, stride_y, stride_x) strides
* \param (dilation_z, dilation_y, dilation_x) dilations
* \param (pad_z, pad_y, pad_x) pads
*/
template <typename TIn,
typename TWei,
typename TOut,
typename TAcc,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
__global__ void naive_conv_bwd_weight_ndhwc_kzyxc_ndhwk(const TIn* __restrict__ p_in,
TWei* __restrict__ p_wei_grad,
const TOut* __restrict__ p_out_grad,
const ConvDims dims)
{
const index_t tid = blockIdx.x * blockDim.x + threadIdx.x;
const index_t num_threads = blockDim.x * gridDim.x;
const long_index_t weight_length = dims.K * dims.Z * dims.Y * dims.X * dims.C;
const index_t in_strides[] = {
dims.Di * dims.Hi * dims.Wi * dims.C, dims.Hi * dims.Wi * dims.C, dims.Wi * dims.C, dims.C};
const index_t out_strides[] = {
dims.Do * dims.Ho * dims.Wo * dims.K, dims.Ho * dims.Wo * dims.K, dims.Wo * dims.K, dims.K};
const index_t wei_strides[] = {
dims.Z * dims.Y * dims.X * dims.C, dims.Y * dims.X * dims.C, dims.X * dims.C, dims.C};
constexpr auto in_op = InElementwiseOperation{};
constexpr auto wei_op = WeiElementwiseOperation{};
constexpr auto out_op = OutElementwiseOperation{};
TIn in_val = TIn{0};
TWei wei_val = TWei{0};
TOut out_val = TOut{0};
for(long_index_t ii = tid; ii < weight_length; ii += num_threads)
{
// Decode linear index to (k, z, y, x, c)
const index_t k = ii / wei_strides[0];
index_t tmp = ii - k * wei_strides[0];
const index_t z = tmp / wei_strides[1];
tmp -= z * wei_strides[1];
const index_t y = tmp / wei_strides[2];
tmp -= y * wei_strides[2];
const index_t x = tmp / wei_strides[3];
tmp -= x * wei_strides[3];
const index_t c = tmp;
// Always accumulate in float
float acc_float = 0.0f;
// Loop over batch
for(index_t n = 0; n < dims.N; ++n)
{
const TIn* in_n = p_in + static_cast<long_index_t>(n) * in_strides[0];
const TOut* out_n = p_out_grad + static_cast<long_index_t>(n) * out_strides[0];
// Loop over output spatial dimensions
for(index_t d_o = 0; d_o < dims.Do; ++d_o)
{
// Calculate input position from output position
index_t di = d_o * dims.stride_z - dims.pad_z + z * dims.dilation_z;
if(di < 0 || di >= dims.Di)
continue;
const TIn* in_n_di = in_n + di * in_strides[1];
const TOut* out_n_do = out_n + d_o * out_strides[1];
for(index_t ho = 0; ho < dims.Ho; ++ho)
{
index_t hi = ho * dims.stride_y - dims.pad_y + y * dims.dilation_y;
if(hi < 0 || hi >= dims.Hi)
continue;
const TIn* in_n_di_hi = in_n_di + hi * in_strides[2];
const TOut* out_n_do_ho = out_n_do + ho * out_strides[2];
for(index_t wo = 0; wo < dims.Wo; ++wo)
{
index_t wi = wo * dims.stride_x - dims.pad_x + x * dims.dilation_x;
if(wi < 0 || wi >= dims.Wi)
continue;
// Load values from memory (like forward does)
const TIn* in_ptr = in_n_di_hi + wi * in_strides[3];
const TOut* out_ptr = out_n_do_ho + wo * out_strides[3];
TIn in_loaded = in_ptr[c];
TOut out_loaded = out_ptr[k];
// Apply element-wise operations
in_op(in_val, in_loaded);
out_op(out_val, out_loaded);
// Convert to float for multiplication
float in_f = type_convert<float>(in_val);
float out_f = type_convert<float>(out_val);
acc_float += out_f * in_f;
}
}
}
}
// Convert float accumulator to TAcc, then to weight type
TAcc acc = type_convert<TAcc>(acc_float);
TWei result = type_convert<TWei>(acc);
// Apply weight element-wise operation (if any)
wei_op(wei_val, result);
// Write transformed result
p_wei_grad[ii] = wei_val;
}
}
} // namespace ref
} // namespace ck

94
library/include/ck/library/reference_tensor_operation/gpu/naive_conv_fwd.hpp → library/include/ck/library/reference_tensor_operation/gpu/naive_conv_fwd_gpu.hpp
View File

@@ -1,8 +1,10 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#ifndef NAIVE_CONV_FWD_HPP
#define NAIVE_CONV_FWD_HPP
#pragma once
#include "ck/utility/type_convert.hpp"
#include "ck/library/reference_tensor_operation/gpu/conv_common.hpp"
namespace ck {
namespace ref {
@@ -30,43 +32,26 @@ template <typename TIn,
__global__ void naive_conv_fwd_ndhwc_kzyxc_ndhwk(const TIn* __restrict__ p_in,
const TWei* __restrict__ p_wei,
TOut* __restrict__ p_out,
index_t N,
index_t K,
index_t C,
index_t Di,
index_t Hi,
index_t Wi,
index_t Z,
index_t Y,
index_t X,
index_t Do,
index_t Ho,
index_t Wo,
index_t stride_z,
index_t stride_y,
index_t stride_x,
index_t dilation_z,
index_t dilation_y,
index_t dilation_x,
index_t pad_z,
index_t pad_y,
index_t pad_x)
const ConvDims dims)
{
const index_t tid = blockIdx.x * blockDim.x + threadIdx.x;
const index_t num_threads = blockDim.x * gridDim.x;
const long_index_t output_length = N * Do * Ho * Wo * K;
const long_index_t output_length = dims.N * dims.Do * dims.Ho * dims.Wo * dims.K;
const index_t out_strides[] = {Do * Ho * Wo * K, Ho * Wo * K, Wo * K, K};
const index_t in_strides[] = {Di * Hi * Wi * C, Hi * Wi * C, Wi * C, C};
const index_t wei_strides[] = {Z * Y * X * C, Y * X * C, X * C, C};
const index_t out_strides[] = {
dims.Do * dims.Ho * dims.Wo * dims.K, dims.Ho * dims.Wo * dims.K, dims.Wo * dims.K, dims.K};
const index_t in_strides[] = {
dims.Di * dims.Hi * dims.Wi * dims.C, dims.Hi * dims.Wi * dims.C, dims.Wi * dims.C, dims.C};
const index_t wei_strides[] = {
dims.Z * dims.Y * dims.X * dims.C, dims.Y * dims.X * dims.C, dims.X * dims.C, dims.C};
constexpr auto in_op = InElementwiseOperation{};
constexpr auto wei_op = WeiElementwiseOperation{};
constexpr auto out_op = OutElementwiseOperation{};
TIn in_val;
TWei wei_val;
TOut out_val;
TIn in_val = TIn{0};
TWei wei_val = TWei{0};
TOut out_val = TOut{0};
for(long_index_t ii = tid; ii < output_length; ii += num_threads)
{
@@ -79,47 +64,66 @@ __global__ void naive_conv_fwd_ndhwc_kzyxc_ndhwk(const TIn* __restrict__ p_in,
const index_t wo = k / out_strides[3];
k -= wo * out_strides[3];
TAcc acc = static_cast<TAcc>(0);
// Always accumulate in float (FP8/BF8 don't support arithmetic)
float acc_float = 0.0f;
const TIn* in_n = p_in + static_cast<long_index_t>(n) * in_strides[0];
const TWei* wei_k = p_wei + static_cast<long_index_t>(k) * wei_strides[0];
for(index_t z = 0; z < Z; ++z)
for(index_t z = 0; z < dims.Z; ++z)
{
index_t di = stride_z * dO - pad_z + dilation_z * z;
index_t di = dims.stride_z * dO - dims.pad_z + dims.dilation_z * z;
const TIn* in_n_di = in_n + di * in_strides[1];
const TWei* wei_k_z = wei_k + z * wei_strides[1];
for(index_t y = 0; y < Y; ++y)
for(index_t y = 0; y < dims.Y; ++y)
{
index_t hi = stride_y * ho - pad_y + dilation_y * y;
index_t hi = dims.stride_y * ho - dims.pad_y + dims.dilation_y * y;
const TIn* in_n_di_hi = in_n_di + hi * in_strides[2];
const TWei* wei_k_z_y = wei_k_z + y * wei_strides[2];
for(index_t x = 0; x < X; ++x)
for(index_t x = 0; x < dims.X; ++x)
{
index_t wi = stride_x * wo - pad_x + dilation_x * x;
index_t wi = dims.stride_x * wo - dims.pad_x + dims.dilation_x * x;
const TIn* in_n_di_hi_wi = in_n_di_hi + wi * in_strides[3];
const TWei* wei_k_z_y_x = wei_k_z_y + x * wei_strides[3];
if(di >= 0 && di < Di && hi >= 0 && hi < Hi && wi >= 0 && wi < Wi)
if(di >= 0 && di < dims.Di && hi >= 0 && hi < dims.Hi && wi >= 0 &&
wi < dims.Wi)
{
for(index_t c = 0; c < C; ++c)
for(index_t c = 0; c < dims.C; ++c)
{
in_op(in_val, in_n_di_hi_wi[c]);
wei_op(wei_val, wei_k_z_y_x[c]);
acc += in_val * wei_val;
// Load values from memory
TIn in_loaded = in_n_di_hi_wi[c];
TWei wei_loaded = wei_k_z_y_x[c];
// Apply element-wise operations
in_op(in_val, in_loaded);
wei_op(wei_val, wei_loaded);
// Always convert to float for multiplication (FP8/BF8 don't support
// direct arithmetic)
float in_f = type_convert<float>(in_val);
float wei_f = type_convert<float>(wei_val);
// Accumulate in float
acc_float += in_f * wei_f;
}
}
}
}
}
out_op(out_val, static_cast<TOut>(acc));
// Convert float accumulator to TAcc, then to output type
TAcc acc = type_convert<TAcc>(acc_float);
TOut result = type_convert<TOut>(acc);
// Apply output element-wise operation (if any)
out_op(out_val, result);
// Write transformed result
p_out[ii] = out_val;
}
}
} // namespace ref
} // namespace ck
#endif