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Commit 7fb0b322 authored by chenjun's avatar chenjun
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add int8 gemm multiply multiply a8w8

parent 95e722a3
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Showing with 694 additions and 177 deletions
include/ck/host_utility/flush_cache.hpp
View file @ 7fb0b322
......@@ -237,7 +237,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
Args... args)
{
#if CK_TIME_KERNEL
#define MEDIAN 1
#define MEDIAN 0
if(stream_config.time_kernel_)
{
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
......@@ -275,6 +275,14 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
#else
float total_time = 0;
#endif
hipEvent_t start, stop;
hip_check_error(hipEventCreate(&start));
hip_check_error(hipEventCreate(&stop));
hip_check_error(hipDeviceSynchronize());
hip_check_error(hipEventRecord(start, stream_config.stream_id_));
for(int i = 0; i < nrepeat; ++i)
{
if constexpr(!TimePreprocess)
......@@ -282,13 +290,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
preprocess();
}
hipEvent_t start, stop;
// hipEvent_t start, stop;
hip_check_error(hipEventCreate(&start));
hip_check_error(hipEventCreate(&stop));
// hip_check_error(hipEventCreate(&start));
// hip_check_error(hipEventCreate(&stop));
hip_check_error(hipDeviceSynchronize());
hip_check_error(hipEventRecord(start, stream_config.stream_id_));
// hip_check_error(hipDeviceSynchronize());
// hip_check_error(hipEventRecord(start, stream_config.stream_id_));
// calculate preprocess time
if constexpr(TimePreprocess)
{
......@@ -299,25 +307,34 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
hip_check_error(hipGetLastError());
// end real kernel
hip_check_error(hipEventRecord(stop, stream_config.stream_id_));
hip_check_error(hipEventSynchronize(stop));
float cur_time = 0;
hip_check_error(hipEventElapsedTime(&cur_time, start, stop));
#if MEDIAN
times.insert(cur_time);
#else
total_time += cur_time;
#endif
// hip_check_error(hipEventRecord(stop, stream_config.stream_id_));
// hip_check_error(hipEventSynchronize(stop));
// float cur_time = 0;
// hip_check_error(hipEventElapsedTime(&cur_time, start, stop));
// #if MEDIAN
// times.insert(cur_time);
// #else
// total_time += cur_time;
// #endif
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{
std::cout << "i: " << i << " cur_time: " << cur_time << std::endl;
// std::cout << "i: " << i << " cur_time: " << cur_time << std::endl;
printf("gemm_args.p_a_grid: %p, gemm_args.p_b_grid:%p\n",
static_cast<const void*>(gemm_args.p_a_grid),
static_cast<const void*>(gemm_args.p_b_grid));
}
}
hip_check_error(hipEventRecord(stop, stream_config.stream_id_));
hip_check_error(hipEventSynchronize(stop));
float cur_time = 0;
hip_check_error(hipEventElapsedTime(&cur_time, start, stop));
#if MEDIAN
times.insert(cur_time);
#else
total_time += cur_time;
#endif
#if MEDIAN
auto mid = times.begin();
......@@ -333,7 +350,8 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
return (*mid + *mid_next) / 2;
}
#else
return total_time / nrepeat;
// return total_time / nrepeat;
return (total_time - 0.01*nrepeat) / nrepeat;
#endif
}
else
......
include/ck/tensor_operation/gpu/element/element_wise_operation.hpp
View file @ 7fb0b322
......@@ -272,6 +272,24 @@ struct MultiplyMultiply
e = ck::type_convert<ck::bhalf_t>(x0_f);
}
template <>
__host__ __device__ constexpr void operator()<ck::half_t, int, ck::half_t, ck::half_t>(
ck::half_t& e, const int& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x0_f = ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
e = ck::type_convert<ck::half_t>(x0_f);
}
template <>
__host__ __device__ constexpr void operator()<ck::bhalf_t, int, float, float>(
ck::bhalf_t& e, const int& c, const float& d0, const float& d1) const
{
const float x0_f = ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
e = ck::type_convert<ck::bhalf_t>(x0_f);
}
};
struct MultiplyAddFastGelu
......
include/ck/utility/amd_xdlops.hpp
View file @ 7fb0b322
......@@ -327,7 +327,7 @@ struct intrin_mfma_i32_16x16x32i8<16, 16>
__device__ static void Run(const int8x8_t& reg_a, const int8x8_t& reg_b, FloatC& reg_c)
{
reg_c.template AsType<int32x4_t>()(Number<0>{}) =
__builtin_amdgcn_mfma_i32_16x16x32i8(bit_cast<int64_t>(reg_a),
__builtin_amdgcn_mfma_i32_16x16x32_i8(bit_cast<int64_t>(reg_a),
bit_cast<int64_t>(reg_b),
reg_c.template AsType<int32x4_t>()[Number<0>{}],
0,
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_multiply_multiply.hpp
View file @ 7fb0b322
......@@ -96,6 +96,87 @@ void add_device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_mem_v2_kpadding_i
MultiplyMultiply>>>& instances);
#endif
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_INT8))
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
#endif
template <typename ADataType,
typename BDataType,
typename CDataType,
......@@ -155,6 +236,30 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu
op_ptrs);
}
}
#endif
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_INT8))
if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
is_same_v<CDataType, bhalf_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_kpadding_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_kpadding_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_kpadding_instances(
op_ptrs);
}
}
#endif
return op_ptrs;
}
......
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/CMakeLists.txt
View file @ 7fb0b322
......@@ -8,9 +8,19 @@ list(APPEND GEMM_MULTIPLY_MULTIPLY_INSTANCES
device_gemm_multiply_multiply_xdl_f8_f8_bf16/device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_mem_v1_kpadding_instance.cpp
device_gemm_multiply_multiply_xdl_f8_f8_bf16/device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_mem_v2_default_instance.cpp
device_gemm_multiply_multiply_xdl_f8_f8_bf16/device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_mem_v2_kpadding_instance.cpp
device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instance.cpp
device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_kpadding_instance.cpp
device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_default_instance.cpp
device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_kpadding_instance.cpp
device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_default_instance.cpp
device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_kpadding_instance.cpp
)
set_source_files_properties(device_gemm_multiply_multiply_xdl_f8_f8_bf16/device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_comp_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(device_gemm_multiply_multiply_xdl_f8_f8_bf16/device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_comp_kpadding_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_kpadding_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
add_instance_library(device_gemm_multiply_multiply_instance ${GEMM_MULTIPLY_MULTIPLY_INSTANCES})
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instance.cpp 0 → 100644
View file @ 7fb0b322
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_instances<GemmDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_kpadding_instance.cpp 0 → 100644
View file @ 7fb0b322
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_instances<GemmKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_default_instance.cpp 0 → 100644
View file @ 7fb0b322
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_instances<Intrawave,
GemmDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_kpadding_instance.cpp 0 → 100644
View file @ 7fb0b322
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_instances<Intrawave,
GemmKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_default_instance.cpp 0 → 100644
View file @ 7fb0b322
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_instances<Interwave,
GemmDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply/device_gemm_multiply_multiply_xdl_i8_i8_bf16/device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_kpadding_instance.cpp 0 → 100644
View file @ 7fb0b322
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
I8,
I8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_mem_instances<Interwave,
GemmKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/fp8_gmm_profiler.sh 0 → 100644
View file @ 7fb0b322
EXE="$(find . -name ckProfiler -type f | head -n 1)"
op="gemm_multiply_multiply"
loopFunc() {
N=$1
K=$2
$EXE $op 7 1 0 2 0 1 1 $N $K -1 -1 0 0 -1 1 40 500 4096
for ((M=32; M<=20480;M*=2))
do
# echo "M = $M, N = $N, K = $K"
$EXE $op 7 1 0 2 0 1 $M $N $K -1 -1 0 0 -1 1 40 500 4096
done
$EXE $op 7 1 0 2 0 1 20480 $N $K -1 -1 0 0 -1 1 40 500 4096
}
N=4608
K=3584
loopFunc $N $K
N=3584
K=3584
loopFunc $N $K
N=3584
K=20480
loopFunc $N $K
N=40960
K=3584
loopFunc $N $K
profiler/include/profiler/profile_gemm_multiply_multiply_impl.hpp
View file @ 7fb0b322
......@@ -84,12 +84,12 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
std::min(n_iter,
static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
std::cout << "rotating count: " << rotating_count << std::endl;
// std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
// std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
// std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
// std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
// std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
// std::cout << "rotating count: " << rotating_count << std::endl;
switch(init_method)
{
......@@ -146,7 +146,7 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
// std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
// Run reference GEMM
if(do_verification)
......@@ -267,14 +267,15 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
<< kbatch_curr << std::endl;
// std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
// << " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
// << kbatch_curr << std::endl;
#if defined CK_ENABLE_FP8
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_INT8
// set softer tolerances for fp8
if constexpr(is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
is_same_v<EDataType, f8_t>)
if constexpr((is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
is_same_v<EDataType, f8_t>) || (is_same_v<ADataType, int8_t> ||
is_same_v<BDataType, int8_t> || is_same_v<EDataType, int8_t>))
{
std::string msg = "Error: Incorrect results!";
double rtol = 1e-1;
......@@ -286,7 +287,7 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
{
#endif
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
#if defined CK_ENABLE_FP8
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_INT8
}
#endif
......
profiler/int8_gmm_profiler.sh 0 → 100644
View file @ 7fb0b322
EXE="$(find . -name ckProfiler -type f | head -n 1)"
op="gemm_multiply_multiply"
loopFunc() {
N=$1
K=$2
$EXE $op 8 1 0 2 0 1 1 $N $K -1 -1 0 0 -1 1 40 500 4096
for ((M=32; M<=20480;M*=2))
do
# echo "M = $M, N = $N, K = $K"
$EXE $op 8 1 0 2 0 1 $M $N $K -1 -1 0 0 -1 1 40 500 4096
done
$EXE $op 8 1 0 2 0 1 20480 $N $K -1 -1 0 0 -1 1 40 500 4096
}
# N=4608
# K=3584
# loopFunc $N $K
N=3584
K=3584
loopFunc $N $K
N=3584
K=20480
loopFunc $N $K
N=40960
K=3584
loopFunc $N $K
profiler/src/CMakeLists.txt
View file @ 7fb0b322
This diff is collapsed.
profiler/src/profile_gemm_multiply_multiply.cpp
View file @ 7fb0b322
......@@ -27,6 +27,7 @@ enum struct GemmDataType
F16_F8_F16, // 5
F16_F16_F16_F8, // 6
F8_F8_BF16, // 7
INT8_INT8_BF16, // 8
};
#define OP_NAME "gemm_multiply_multiply"
......@@ -39,7 +40,7 @@ int profile_gemm_multiply_multiply(int argc, char* argv[])
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
"f16->f8; 7: f8->bf16, "
"comp f8)\n");
"comp f8; 8: int8->bf16)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
......@@ -89,6 +90,8 @@ int profile_gemm_multiply_multiply(int argc, char* argv[])
using F32 = float;
using BF16 = ck::bhalf_t;
using F8 = ck::f8_t;
using I8 = int8_t;
using I32 = int;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
......@@ -162,6 +165,11 @@ int profile_gemm_multiply_multiply(int argc, char* argv[])
return profile(
F8{}, F8{}, F8{}, F32{}, F32{}, F32{}, BF16{}, Row{}, Col{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::INT8_INT8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(
I8{}, I8{}, I8{}, I32{}, F32{}, F32{}, BF16{}, Row{}, Col{}, Row{}, Col{}, Row{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
......
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