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Add v3 support for Groupd fwd conv+bias+clamp & ckProfiler (#2463)

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* Add logging to IsSupported.

* Less casting in AddClamp

* Conv+bias+clamp instances & profiler BF16

* Fix 3D instances & run just 1x for verification.

* :Run just once for verification conv fwd.

* ckProfiler conv fwd clampwq

* Remove exec bit & formatting

* Add support for MultiD for grouped conv fwd v3.

* Enable 2Lds.

* clean

* align instances

* align instances

* profiler fixes

* Fixes

* fix

* fix

---------

Co-authored-by: Adam Osewski <root@quanta-ccs-aus-f01-19.cs-aus.dcgpu>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

[ROCm/composable_kernel commit: c8eb2f995c]
This commit is contained in:
Adam Osewski
2025-07-25 10:34:31 +02:00
committed by GitHub
parent cde0c4d224
commit 5ccd4c679f
6 changed files with 1098 additions and 301 deletions
Download Patch File Download Diff File Expand all files Collapse all files

673
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle_v3.hpp
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File diff suppressed because it is too large Load Diff

8
include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp
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@@ -379,10 +379,10 @@ struct AddClamp
__host__ __device__ constexpr void
operator()<half_t, half_t, half_t>(half_t& y, const half_t& x0, const half_t& x1) const
{
const half_t a = x0 + x1;
y = a > type_convert<half_t>(floor_)
? (a < type_convert<half_t>(ceil_) ? a : type_convert<half_t>(ceil_))
: type_convert<half_t>(floor_);
const half_t floor = type_convert<half_t>(floor_);
const half_t ceil = type_convert<half_t>(ceil_);
const half_t a = x0 + x1;
y = a > floor ? (a < ceil ? a : ceil) : floor;
};
template <>

327
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3_multi_d.hpp
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@@ -143,7 +143,8 @@ template <typename ALayout,
typename ComputeTypeA = CDataType,
typename ComputeTypeB = ComputeTypeA,
typename LDSTypeA = ADataType,
typename LDSTypeB = BDataType>
typename LDSTypeB = BDataType,
bool DoElementwiseBeforeCShuffle = false>
struct GridwiseGemmMultiD_xdl_cshuffle_v3
{
static constexpr auto I0 = Number<0>{};
@@ -466,6 +467,12 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
}
else
{
static_assert(false,
"The layout configuration is not supported! "
"Only support Row & Col major.");
}
}();
// pad M and N
@@ -538,8 +545,6 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
Number<NumDTensor>{});
}
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N(0, 0, 0, 0, {}))>;
struct Problem
{
__host__ __device__ Problem() = default;
@@ -1245,11 +1250,11 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
template <bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
TailNumber TailNum = TailNumber::Odd>
__device__ static void Run(const ADataType* p_a_grid,
const BDataType* p_b_grid,
__device__ static void Run(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsGridPointer& p_ds_grid,
CDataType* p_c_grid,
void* p_shared,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared,
const Problem& problem,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
@@ -1273,11 +1278,11 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
TailNumber TailNum = TailNumber::Odd>
__device__ static void Run(const ADataType* p_a_grid,
const BDataType* p_b_grid,
__device__ static void Run(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsGridPointer& p_ds_grid,
CDataType* p_c_grid,
void* p_shared,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared,
const Problem& problem,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
@@ -1288,17 +1293,62 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);
const auto b_grid_desc_bk0_n_bk1 = MakeBGridDescriptor_BK0_N_BK1(
problem.K, problem.KPadded, problem.N, problem.NPadded, problem.StrideB, problem.BK0);
const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N<CLayout>(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);
const auto ds_grid_desc_m_n = MakeDsGridDescriptor_M_N(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideDs);
const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
c_grid_desc_m_n, problem.MBlock, problem.NBlock);
Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum, Block2CTileMap>(
p_a_grid,
p_b_grid,
p_ds_grid,
p_c_grid,
p_shared,
problem,
a_element_op,
b_element_op,
c_element_op,
block_2_ctile_map,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
ds_grid_desc_m_n,
c_grid_desc_m_n);
}
template <bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
TailNumber TailNum,
typename Block2CTileMap,
typename AGridDesc_AK0_M_K1,
typename BGridDesc_BK0_N_K1,
typename DsGridDesc_M_N,
typename CGridDesc_M_N>
__device__ static void Run(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsGridPointer& p_ds_grid,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared,
const Problem& problem,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
const Block2CTileMap& block_2_ctile_map,
const AGridDesc_AK0_M_K1& a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_K1& b_grid_desc_bk0_n_bk1,
const DsGridDesc_M_N& ds_grid_desc_m_n,
const CGridDesc_M_N& c_grid_desc_m_n)
{
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
c_grid_desc_m_n, problem.MBlock, problem.NBlock);
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
@@ -1515,43 +1565,63 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
make_multi_index(n_thread_data_on_block));
tensor_operation::element_wise::PassThrough pass_through{};
const auto& vpgr_to_lds_element_op = [&] {
if constexpr(DoElementwiseBeforeCShuffle)
{
return c_element_op;
}
else
{
return pass_through;
}
};
const auto& lds_to_global_element_op = [&] {
if constexpr(!DoElementwiseBeforeCShuffle)
{
return c_element_op;
}
else
{
return pass_through;
}
};
// shuffle: threadwise copy C from VGPR to LDS
auto c_thread_copy_vgpr_to_lds =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
CShuffleDataType,
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
ck::tensor_operation::element_wise::PassThrough,
Sequence<CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
I1,
I1,
M2,
I1,
M4,
I1>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
1,
InMemoryDataOperationEnum::Set,
1,
true>{
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_multi_index(0,
0,
m_thread_data_on_block_idx[I1],
n_thread_data_on_block_idx[I1],
m_thread_data_on_block_idx[I2],
m_thread_data_on_block_idx[I3],
m_thread_data_on_block_idx[I4],
n_thread_data_on_block_idx[I2]),
ck::tensor_operation::element_wise::PassThrough{}};
auto c_thread_copy_vgpr_to_lds = ThreadwiseTensorSliceTransfer_v1r3<
AccDataType,
CShuffleDataType,
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
conditional_t<DoElementwiseBeforeCShuffle,
CElementwiseOperation,
tensor_operation::element_wise::PassThrough>,
Sequence<CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
I1,
I1,
M2,
I1,
M4,
I1>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
1,
InMemoryDataOperationEnum::Set,
1,
true>{c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_multi_index(0,
0,
m_thread_data_on_block_idx[I1],
n_thread_data_on_block_idx[I1],
m_thread_data_on_block_idx[I2],
m_thread_data_on_block_idx[I3],
m_thread_data_on_block_idx[I4],
n_thread_data_on_block_idx[I2]),
vpgr_to_lds_element_op()};
using EDataType = CDataType;
const auto ds_grid_desc_m_n = MakeDsGridDescriptor_M_N(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideDs);
const auto ds_grid_desc_mblock_mperblock_nblock_nperblock =
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
ds_grid_desc_m_n, problem.MBlock, problem.NBlock);
@@ -1601,7 +1671,9 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
Tuple<EDataType>,
decltype(c_ds_desc_refs),
decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
CElementwiseOperation,
conditional_t<!DoElementwiseBeforeCShuffle,
CElementwiseOperation,
tensor_operation::element_wise::PassThrough>,
Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>, // FIXME: make Sequence
// support arbitray type
Sequence<1,
@@ -1625,7 +1697,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
idx_c_ds_block_begin,
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
make_tuple(make_multi_index(block_m_id, 0, block_n_id, 0)),
c_element_op};
lds_to_global_element_op()};
// space filling curve for threadwise C in VGPR
constexpr auto sfc_c_vgpr =
@@ -1698,12 +1770,12 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
template <bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
TailNumber TailNum = TailNumber::Odd>
__device__ static void Run_2Lds(const ADataType* p_a_grid,
const BDataType* p_b_grid,
__device__ static void Run_2Lds(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsGridPointer& p_ds_grid,
CDataType* p_c_grid,
void* p_shared_0,
void* p_shared_1,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared_0,
void* __restrict__ p_shared_1,
const Problem& problem,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
@@ -1729,12 +1801,12 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
TailNumber TailNum = TailNumber::Odd>
__device__ static void Run_2Lds(const ADataType* p_a_grid,
const BDataType* p_b_grid,
__device__ static void Run_2Lds(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsGridPointer& p_ds_grid,
CDataType* p_c_grid,
void* p_shared_0,
void* p_shared_1,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared_0,
void* __restrict__ p_shared_1,
const Problem& problem,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
@@ -1745,8 +1817,53 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);
const auto b_grid_desc_bk0_n_bk1 = MakeBGridDescriptor_BK0_N_BK1(
problem.K, problem.KPadded, problem.N, problem.NPadded, problem.StrideB, problem.BK0);
const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N<CLayout>(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideC);
const auto ds_grid_desc_m_n = MakeDsGridDescriptor_M_N(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideDs);
Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(p_a_grid,
p_b_grid,
p_ds_grid,
p_c_grid,
p_shared_0,
p_shared_1,
problem,
a_element_op,
b_element_op,
c_element_op,
block_2_ctile_map,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
ds_grid_desc_m_n,
c_grid_desc_m_n);
}
template <bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
TailNumber TailNum,
typename Block2CTileMap,
typename AGridDesc_AK0_M_K1,
typename BGridDesc_BK0_N_K1,
typename DsGridDesc_M_N,
typename CGridDesc_M_N>
__device__ static void Run_2Lds(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsGridPointer& p_ds_grid,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared_0,
void* __restrict__ p_shared_1,
const Problem& problem,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
const Block2CTileMap& block_2_ctile_map,
const AGridDesc_AK0_M_K1& a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_K1& b_grid_desc_bk0_n_bk1,
const DsGridDesc_M_N& ds_grid_desc_m_n,
const CGridDesc_M_N& c_grid_desc_m_n)
{
const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
@@ -1982,43 +2099,63 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
make_multi_index(n_thread_data_on_block));
tensor_operation::element_wise::PassThrough pass_through{};
const auto& vpgr_to_lds_element_op = [&] {
if constexpr(DoElementwiseBeforeCShuffle)
{
return c_element_op;
}
else
{
return pass_through;
}
};
const auto& lds_to_global_element_op = [&] {
if constexpr(!DoElementwiseBeforeCShuffle)
{
return c_element_op;
}
else
{
return pass_through;
}
};
// shuffle: threadwise copy C from VGPR to LDS
auto c_thread_copy_vgpr_to_lds =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
CShuffleDataType,
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
ck::tensor_operation::element_wise::PassThrough,
Sequence<CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
I1,
I1,
M2,
I1,
M4,
I1>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
1,
InMemoryDataOperationEnum::Set,
1,
true>{
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_multi_index(0,
0,
m_thread_data_on_block_idx[I1],
n_thread_data_on_block_idx[I1],
m_thread_data_on_block_idx[I2],
m_thread_data_on_block_idx[I3],
m_thread_data_on_block_idx[I4],
n_thread_data_on_block_idx[I2]),
ck::tensor_operation::element_wise::PassThrough{}};
auto c_thread_copy_vgpr_to_lds = ThreadwiseTensorSliceTransfer_v1r3<
AccDataType,
CShuffleDataType,
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
conditional_t<DoElementwiseBeforeCShuffle,
CElementwiseOperation,
tensor_operation::element_wise::PassThrough>,
Sequence<CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
I1,
I1,
M2,
I1,
M4,
I1>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
1,
InMemoryDataOperationEnum::Set,
1,
true>{c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_multi_index(0,
0,
m_thread_data_on_block_idx[I1],
n_thread_data_on_block_idx[I1],
m_thread_data_on_block_idx[I2],
m_thread_data_on_block_idx[I3],
m_thread_data_on_block_idx[I4],
n_thread_data_on_block_idx[I2]),
vpgr_to_lds_element_op()};
using EDataType = CDataType;
const auto ds_grid_desc_m_n = MakeDsGridDescriptor_M_N(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideDs);
const auto ds_grid_desc_mblock_mperblock_nblock_nperblock =
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
ds_grid_desc_m_n, problem.MBlock, problem.NBlock);
@@ -2068,7 +2205,9 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
Tuple<EDataType>,
decltype(c_ds_desc_refs),
decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
CElementwiseOperation,
conditional_t<!DoElementwiseBeforeCShuffle,
CElementwiseOperation,
tensor_operation::element_wise::PassThrough>,
Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>, // FIXME: make Sequence
// support arbitray type
Sequence<1,
@@ -2092,7 +2231,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
idx_c_ds_block_begin,
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
make_tuple(make_multi_index(block_m_id, 0, block_n_id, 0)),
c_element_op};
lds_to_global_element_op()};
// space filling curve for threadwise C in VGPR
constexpr auto sfc_c_vgpr =

6
profiler/src/CMakeLists.txt
View File

@@ -94,6 +94,8 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx11" OR SUPPORTED_GPU_TARGETS MATCHES "gfx12
list(APPEND PROFILER_OPS profile_batched_gemm.cpp)
list(APPEND PROFILER_OPS profile_gemm_b_scale.cpp)
list(APPEND PROFILER_OPS profile_grouped_conv_fwd.cpp)
list(APPEND PROFILER_OPS profile_grouped_conv_fwd_bias_clamp.cpp)
list(APPEND PROFILER_OPS profile_grouped_conv_fwd_clamp.cpp)
list(APPEND PROFILER_OPS profile_grouped_conv_bwd_data.cpp)
list(APPEND PROFILER_OPS profile_grouped_conv_bwd_weight.cpp)
endif()
@@ -197,6 +199,10 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
list(APPEND DEVICE_INSTANCES device_grouped_convnd_bwd_weight_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_convscale_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_convinvscale_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv2d_fwd_clamp_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_clamp_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv2d_fwd_bias_clamp_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_bias_clamp_instance)
endif()
if((SUPPORTED_GPU_TARGETS MATCHES "gfx9" AND (DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)) OR

191
profiler/src/profile_grouped_conv_fwd_bias_clamp.cpp Normal file
View File

@@ -0,0 +1,191 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "profiler/profile_grouped_conv_fwd_bias_clamp_impl.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/utility/ignore.hpp"
#include "profiler_operation_registry.hpp"
#include <iostream>
enum struct ConvLayout
{
GNHWC_GKYXC_GNHWK, // 0
NHWGC_GKYXC_NHWGK, // 1
NGCHW_GKYXC_NGKHW, // 2
NGCHW_GKCYX_NGKHW, // 3
};
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
F8_F8_F8, // 4
BF8_BF8_F8, // 5
F8_BF8_F8, // 6
BF8_F8_F8, // 7
};
enum struct IndexType
{
INDEX_T, // 0
LONG_INDEX_T, // 1
};
#define OP_NAME "grouped_conv_fwd_bias_clamp"
#define OP_DESC "Grouped Convolution Forward+Bias+Clamp"
static void print_helper_msg()
{
std::cout
// clang-format off
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n"
<< " 3: Input int8, Weight int8, Output int8\n"
<< " 4: Input fp8, Weight fp8, Output fp8\n"
<< " 5: Input bf8, Weight bf8, Output fp8\n"
<< " 6: Input fp8, Weight bf8, Output fp8\n"
<< " 7: Input bf8, Weight fp8, Output fp8)\n"
<< "arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
<< " 1: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]\n"
<< " 2: Input[N, G, C, Hi, Wi], Weight[G, K, Y, X, C], Output[N, "
"G, K, Ho, Wo]\n"
<< " 3: Input[N, G, C, Hi, Wi], Weight[G, K, C, Y, X], Output[N, "
"G, K, Ho, Wo])\n"
<< "arg4: indexing data type (0: 32-bit, 1: 64-bit)\n"
<< "arg5: verification (0: no, 1: yes)\n"
<< "arg6: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
// clang-format on
}
int grouped_conv_fwd_bias_clamp(int argc, char* argv[])
{
// 8 for control, 1 for num_dim_spatial
if(argc < 10)
{
print_helper_msg();
return 1;
}
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ConvLayout>(std::stoi(argv[3]));
const auto index_type = static_cast<IndexType>(std::stoi(argv[4]));
const bool do_verification = std::stoi(argv[5]);
const int init_method = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]);
const int num_dim_spatial = std::stoi(argv[9]);
// 9 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
if(argc != 9 + 1 + 4 + 6 * num_dim_spatial)
{
print_helper_msg();
return 1;
}
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 10, argv);
if(index_type != IndexType::INDEX_T)
{
std::cout << "this indexing data type is not implemented" << std::endl;
return 1;
}
using F32 = float;
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
constexpr auto I2 = ck::Number<2>{};
constexpr auto I3 = ck::Number<3>{};
auto profile = [&](auto num_dim_spatial_tmp,
auto in_layout,
auto wei_layout,
auto out_layout,
auto in_type,
auto wei_type,
auto out_type,
auto a_compute_type,
auto b_compute_type) {
constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
using InLayout = decltype(in_layout);
using WeiLayout = decltype(wei_layout);
using OutLayout = decltype(out_layout);
using InDataType = decltype(in_type);
using WeiDataType = decltype(wei_type);
using OutDataType = decltype(out_type);
using AComputeType = decltype(a_compute_type);
using BComputeType = decltype(b_compute_type);
bool pass = ck::profiler::profile_grouped_conv_fwd_bias_clamp_impl<NDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
AComputeType,
BComputeType>(
do_verification, init_method, do_log, time_kernel, params);
return pass ? 0 : 1;
};
if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
}
}
else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
}
}
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, grouped_conv_fwd_bias_clamp);

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profiler/src/profile_grouped_conv_fwd_clamp.cpp Normal file
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@@ -0,0 +1,194 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "profiler/profile_grouped_conv_fwd_impl.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/utility/ignore.hpp"
#include "profiler_operation_registry.hpp"
#include <iostream>
enum struct ConvLayout
{
GNHWC_GKYXC_GNHWK, // 0
NHWGC_GKYXC_NHWGK, // 1
NGCHW_GKYXC_NGKHW, // 2
NGCHW_GKCYX_NGKHW, // 3
};
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
F8_F8_F8, // 4
BF8_BF8_F8, // 5
F8_BF8_F8, // 6
BF8_F8_F8, // 7
};
enum struct IndexType
{
INDEX_T, // 0
LONG_INDEX_T, // 1
};
#define OP_NAME "grouped_conv_fwd_clamp"
#define OP_DESC "Grouped Convolution Forward+Clamp"
static void print_helper_msg()
{
std::cout
// clang-format off
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n"
<< " 3: Input int8, Weight int8, Output int8\n"
<< " 4: Input fp8, Weight fp8, Output fp8\n"
<< " 5: Input bf8, Weight bf8, Output fp8\n"
<< " 6: Input fp8, Weight bf8, Output fp8\n"
<< " 7: Input bf8, Weight fp8, Output fp8)\n"
<< "arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
<< " 1: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]\n"
<< " 2: Input[N, G, C, Hi, Wi], Weight[G, K, Y, X, C], Output[N, "
"G, K, Ho, Wo]\n"
<< " 3: Input[N, G, C, Hi, Wi], Weight[G, K, C, Y, X], Output[N, "
"G, K, Ho, Wo])\n"
<< "arg4: indexing data type (0: 32-bit, 1: 64-bit)\n"
<< "arg5: verification (0: no, 1: yes)\n"
<< "arg6: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
// clang-format on
}
int grouped_conv_fwd_clamp(int argc, char* argv[])
{
// 8 for control, 1 for num_dim_spatial
if(argc < 10)
{
print_helper_msg();
return 1;
}
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ConvLayout>(std::stoi(argv[3]));
const auto index_type = static_cast<IndexType>(std::stoi(argv[4]));
const bool do_verification = std::stoi(argv[5]);
const int init_method = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]);
const int num_dim_spatial = std::stoi(argv[9]);
// 9 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
if(argc != 9 + 1 + 4 + 6 * num_dim_spatial)
{
print_helper_msg();
return 1;
}
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 10, argv);
if(index_type != IndexType::INDEX_T)
{
std::cout << "this indexing data type is not implemented" << std::endl;
return 1;
}
using F32 = float;
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
constexpr auto I2 = ck::Number<2>{};
constexpr auto I3 = ck::Number<3>{};
auto profile = [&](auto num_dim_spatial_tmp,
auto in_layout,
auto wei_layout,
auto out_layout,
auto in_type,
auto wei_type,
auto out_type,
auto a_compute_type,
auto b_compute_type) {
constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
using InLayout = decltype(in_layout);
using WeiLayout = decltype(wei_layout);
using OutLayout = decltype(out_layout);
using InDataType = decltype(in_type);
using WeiDataType = decltype(wei_type);
using OutDataType = decltype(out_type);
using AComputeType = decltype(a_compute_type);
using BComputeType = decltype(b_compute_type);
bool pass =
ck::profiler::profile_grouped_conv_fwd_impl<NDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
AComputeType,
BComputeType,
ck::index_t,
ck::tensor_operation::element_wise::Clamp>(
do_verification, init_method, do_log, time_kernel, params);
return pass ? 0 : 1;
};
if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
}
}
else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
}
}
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, grouped_conv_fwd_clamp);