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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>
This commit is contained in:
JH-Leon-KIM-AMD
2025-12-03 21:14:21 +02:00
committed by GitHub
parent 161835533b
commit 4baa4c9fae
21 changed files with 2280 additions and 69 deletions
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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;