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Unverified Commit f84e2020 authored by Rostyslav Geyyer's avatar Rostyslav Geyyer Committed by GitHub
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Merge branch 'develop' into lwpck-1815

parents 408534d4 25935b57
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library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_convscale/CMakeLists.txt
View file @ f84e2020
......@@ -3,6 +3,7 @@ set(GROUPED_CONV3D_FWD_CONVSCALE
xdl/device_grouped_conv3d_fwd_xdl_convscale_ndhwgc_gkzyxc_ndhwgk_f8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_convscale_ndhwgc_gkzyxc_ndhwgk_bf8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_convscale_ndhwgc_gkzyxc_ndhwgk_f8_bf8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_convscale_ndhwgc_gkzyxc_ndhwgk_bf8_f8_instance.cpp)
xdl/device_grouped_conv3d_fwd_xdl_convscale_ndhwgc_gkzyxc_ndhwgk_bf8_f8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_combconvscale_ndhwgc_gkzyxc_ndhwgk_f8_f8_f32_instance.cpp)
add_instance_library(device_grouped_conv3d_fwd_convscale_instance ${GROUPED_CONV3D_FWD_CONVSCALE})
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_convscale/xdl/device_grouped_conv3d_fwd_xdl_combconvscale_ndhwgc_gkzyxc_ndhwgk_f8_f8_f32_instance.cpp 0 → 100644
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_outelementop_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_combconvscale_ndhwgc_gkzyxc_ndhwgk_f8_f8_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
F8,
F8,
ck::Tuple<>,
F32,
PassThrough,
PassThrough,
CombConvScale,
F8,
F8>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_outelementop_f8_f8_f32_instances<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
ConvFwdDefault,
CombConvScale>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_outelementop_f8_f8_f32_instances<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
ConvFwd1x1P0,
CombConvScale>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_outelementop_f8_f8_f32_instances<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
ConvFwd1x1S1P0,
CombConvScale>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_convscale_relu/CMakeLists.txt
View file @ f84e2020
# ONLY XDL_KERNELS
set(GROUPED_CONV3D_FWD_CONVSCALE_RELU
xdl/device_grouped_conv3d_fwd_xdl_convscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_instance.cpp)
xdl/device_grouped_conv3d_fwd_xdl_convscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_combconvscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_f8_f32_instance.cpp)
add_instance_library(device_grouped_conv3d_fwd_convscale_relu_instance ${GROUPED_CONV3D_FWD_CONVSCALE_RELU})
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_convscale_relu/xdl/device_grouped_conv3d_fwd_xdl_combconvscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_f8_f32_instance.cpp 0 → 100644
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_outelementop_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale_relu.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_combconvscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_f8_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
F8,
F8,
ck::Tuple<>,
F32,
PassThrough,
PassThrough,
CombConvScaleRelu,
F8,
F8>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_outelementop_f8_f8_f32_instances<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
ConvFwdDefault,
CombConvScaleRelu>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_outelementop_f8_f8_f32_instances<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
ConvFwd1x1P0,
CombConvScaleRelu>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_outelementop_f8_f8_f32_instances<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
ConvFwd1x1S1P0,
CombConvScaleRelu>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_convscale_relu/xdl/device_grouped_conv3d_fwd_xdl_convscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_instance.cpp
View file @ f84e2020
......@@ -3,15 +3,13 @@
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_outelementop_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale_relu.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using ConvScaleRelu = ck::tensor_operation::element_wise::ConvScaleRelu;
void add_device_grouped_conv3d_fwd_xdl_convscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
......@@ -56,7 +54,6 @@ void add_device_grouped_conv3d_fwd_xdl_convscale_relu_ndhwgc_gkzyxc_ndhwgk_f8_in
ConvFwd1x1S1P0,
ConvScaleRelu>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
......
library/src/tensor_operation_instance/gpu/permute_scale/CMakeLists.txt
View file @ f84e2020
add_instance_library(device_permute_scale_instance
add_instance_library(device_permute_scale_instance
device_permute_scale_1d_fp16_instances.cpp
device_permute_scale_2d_fp16_instances.cpp
device_permute_scale_3d_fp16_instances.cpp
......@@ -10,4 +10,5 @@ add_instance_library(device_permute_scale_instance
device_permute_scale_3d_fp32_instances.cpp
device_permute_scale_4d_fp32_instances.cpp
device_permute_scale_5d_fp32_instances.cpp
device_permute_scale_6d_fp32_instances.cpp)
device_permute_scale_6d_fp32_instances.cpp
device_permute_scale_6d_fp32_fp8_instances.cpp)
library/src/tensor_operation_instance/gpu/permute_scale/device_permute_scale_6d_fp32_fp8_instances.cpp 0 → 100644
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/permute_scale/device_permute_scale_instances.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using Scale = element_wise::Scale;
void add_device_permute_scale_6d_f32_f8_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>, ck::Tuple<F8>, Scale, 6>>>&
instances)
{
#ifdef CK_ENABLE_FP8
add_device_operation_instances(instances, device_permute_scale_f32_f8_instances<6, Scale>{});
#else
ignore = instances;
#endif
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_f32_f32_f32_amax.cpp
View file @ f84e2020
......@@ -10,15 +10,24 @@ namespace device {
namespace instance {
// clang-format off
// InDataType | AccDataType | OutDataType | Rank | NumReduceDim | ReduceOperation | InElementwiseOp | AccElementwiseOp | PropagateNan | UseIndex
template void add_device_reduce_instance_blockwise<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise<F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
// InDataType | AccDataType | OutDataType | Rank | NumReduceDim | ReduceOperation | InElementwiseOp | AccElementwiseOp | PropagateNan | UseIndex
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>(std::vector<DeviceReducePtr<F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>(std::vector<DeviceReducePtr<F32, F32, F32, 2, 1, ReduceAMax, UnaryAbs, PassThrough, false, true>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 6, 6, ReduceAMax, UnaryAbs, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 6, 6, ReduceAMax, UnaryAbs, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 5, 5, ReduceAMax, UnaryAbs, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 5, 5, ReduceAMax, UnaryAbs, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 4, ReduceAMax, UnaryAbs, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 6, 3, ReduceAMax, UnaryAbs, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 6, 3, ReduceAMax, UnaryAbs, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 5, 3, ReduceAMax, UnaryAbs, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 5, 3, ReduceAMax, UnaryAbs, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 4, 3, ReduceAMax, UnaryAbs, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 3, 3, ReduceAMax, PassThrough, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 3, 3, ReduceAMax, PassThrough, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 2, 2, ReduceAMax, PassThrough, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 2, 2, ReduceAMax, PassThrough, PassThrough, true, false>>&);
template void add_device_reduce_instance_blockwise< F32, F32, F32, 1, 1, ReduceAMax, PassThrough, PassThrough, true, false>(std::vector<DeviceReducePtr<F32, F32, F32, 1, 1, ReduceAMax, PassThrough, PassThrough, true, false>>&);
// clang-format on
} // namespace instance
......
profiler/include/profiler/profile_gemm_multiply_multiply_impl.hpp
View file @ f84e2020
......@@ -48,6 +48,7 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
int StrideD0,
int StrideD1,
int StrideE,
int KBatch,
int n_warmup,
int n_iter,
uint64_t rotating = 0)
......@@ -129,17 +130,17 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
d1_device_buf.ToDevice(d1_m_n.mData.data());
using DeviceOp =
ck::tensor_operation::device::DeviceGemmMultipleD<ALayout,
BLayout,
ck::Tuple<D0Layout, D1Layout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
AElementOp,
BElementOp,
CElementOp>;
ck::tensor_operation::device::DeviceGemmMultipleDSplitK<ALayout,
BLayout,
ck::Tuple<D0Layout, D1Layout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
AElementOp,
BElementOp,
CElementOp>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
......@@ -182,104 +183,128 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
float best_kbatch = 0;
// profile device GEMM instances
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr =
op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
std::array<const void*, 2>{d0_device_buf.GetDeviceBuffer(),
d1_device_buf.GetDeviceBuffer()},
static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
// re-init C to zero before profiling next kernel
c_device_buf.SetZero();
std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38};
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false, 0, n_warmup, n_iter});
if(KBatch > 0)
{
kbatch_list = {KBatch};
}
if(do_verification)
for(std::size_t i = 0; i < kbatch_list.size(); i++)
{
auto kbatch_curr = kbatch_list[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
std::array<const void*, 2>{d0_device_buf.GetDeviceBuffer(),
d1_device_buf.GetDeviceBuffer()},
static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
kbatch_curr,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
c_device_buf.FromDevice(e_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
// re-init C to zero before profiling next kernel
c_device_buf.SetZero();
invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr, false, 0, n_warmup, n_iter});
if(do_log)
if(do_verification)
{
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
<< std::endl;
c_device_buf.FromDevice(e_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(
std::cout << "c_host : ", e_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_device: ", e_m_n_device_result.mData, ",")
<< std::endl;
}
}
}
std::string op_name = op_ptr->GetTypeString();
std::string op_name = op_ptr->GetTypeString();
float ave_time = invoker_ptr->Run(
argument_ptr.get(),
StreamConfig{
nullptr, time_kernel, 0, n_warmup, n_iter, rotating_count > 1, rotating_count});
float ave_time = invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
time_kernel,
0,
n_warmup,
n_iter,
rotating_count > 1,
rotating_count});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(EDataType) * M * N;
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;
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 << 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
// 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>)
{
std::string msg = "Error: Incorrect results!";
double rtol = 1e-1;
double atol = 1e-1;
pass = pass & ck::utils::check_err(
e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
}
else
{
// 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>)
{
std::string msg = "Error: Incorrect results!";
double rtol = 1e-1;
double atol = 1e-1;
pass = pass & ck::utils::check_err(
e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
}
else
{
#endif
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
#if defined CK_ENABLE_FP8
}
}
#endif
if(tflops > best_tflops)
if(tflops > best_tflops && ave_time > 1e-10)
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
best_kbatch = kbatch_curr;
}
}
else
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
std::cout << op_ptr->GetTypeString() << " does not support this problem"
<< std::endl;
}
}
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
}
}
if constexpr(is_same<EDataType, float>::value)
......@@ -318,9 +343,9 @@ bool profile_gemm_multiply_multiply_impl(int do_verification,
}
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
<< " StrideB = " << StrideB << " StrideE = " << StrideE << " : " << best_ave_time
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
<< " StrideB = " << StrideB << " StrideE = " << StrideE << " KBatch = " << best_kbatch
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
<< " GB/s, " << best_op_name << std::endl;
return pass;
}
......
profiler/include/profiler/profile_gemm_universal_impl.hpp
View file @ f84e2020
......@@ -152,7 +152,7 @@ bool profile_gemm_universal_impl(int do_verification,
// profile device GEMM instances
for(auto& op_ptr : op_ptrs)
{
std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 19, 20, 32, 38};
std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38};
if(KBatch > 0)
{
......@@ -249,7 +249,7 @@ bool profile_gemm_universal_impl(int do_verification,
<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
<< kbatch_curr << std::endl;
if(tflops > best_tflops)
if(tflops > best_tflops && ave_time > 1e-10)
{
best_op_name = op_name;
best_tflops = tflops;
......
profiler/include/profiler/profile_grouped_conv_bwd_weight_impl.hpp
View file @ f84e2020
......@@ -136,9 +136,10 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
ck::index_t best_split_k = 1;
// profile device Conv instances
bool all_pass = true;
......@@ -167,99 +168,111 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
range_copy(conv_param.input_left_pads_, begin(input_left_pads));
range_copy(conv_param.input_right_pads_, begin(input_right_pads));
std::vector<ck::index_t> split_k_list = {1, 2, 4, 8, 16, 32, 64, 128};
if(split_k > 0)
{
split_k_list = {split_k};
}
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr =
op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
input_lengths,
input_strides,
filter_lengths,
weights_strides,
output_lengths,
output_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op,
split_k);
const std::size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
for(std::size_t split_k_id = 0; split_k_id < split_k_list.size(); split_k_id++)
{
// using atomic add, so need to reset input
wei_device_buf.SetZero();
std::string op_name = op_ptr->GetTypeString();
auto invoker_ptr = op_ptr->MakeInvokerPointer();
float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
auto argument_ptr = op_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
input_lengths,
input_strides,
filter_lengths,
weights_strides,
output_lengths,
output_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op,
split_k_list[split_k_id]);
const std::size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
// using atomic add, so need to reset input
wei_device_buf.SetZero();
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
std::string op_name = op_ptr->GetTypeString();
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
if(do_verification)
{
wei_device_buf.FromDevice(weight_device_result.mData.data());
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
bool pass = ck::utils::check_err(weight_device_result, weight_host_result);
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", SplitK "
<< split_k_list[split_k_id] << std::endl;
if(!pass)
if(tflops > best_tflops)
{
std::cout << "Fail info: " << op_ptr->GetTypeString() << std::endl;
best_op_name = op_name;
best_tflops = tflops;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
best_split_k = split_k_list[split_k_id];
}
all_pass &= pass;
if(do_log)
if(do_verification)
{
LogRangeAsType<float>(std::cout << "output : ", output.mData, ",") << std::endl;
;
LogRangeAsType<float>(
std::cout << "weight (device): ", weight_device_result.mData, ",")
<< std::endl;
;
LogRangeAsType<float>(
std::cout << "weight (host): ", weight_host_result.mData, ",")
<< std::endl;
;
LogRangeAsType<float>(std::cout << "input: ", input.mData, ",") << std::endl;
;
wei_device_buf.FromDevice(weight_device_result.mData.data());
bool pass = ck::utils::check_err(weight_device_result, weight_host_result);
if(!pass)
{
std::cout << "Fail info: " << op_ptr->GetTypeString() << std::endl;
}
all_pass &= pass;
if(do_log)
{
LogRangeAsType<float>(std::cout << "output : ", output.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "weight (device): ", weight_device_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "weight (host): ", weight_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "input: ", input.mData, ",")
<< std::endl;
}
}
}
}
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem"
<< std::endl;
}
}
}
std::cout << "Best configuration parameters:"
<< "\nname: " << best_op_name << "\navg_time: " << best_avg_time
<< "\ntflops: " << best_tflops << "\nGB/s: " << best_gb_per_sec << std::endl;
<< "\ntflops: " << best_tflops << "\nGB/s: " << best_gb_per_sec << ", SplitK "
<< best_split_k << std::endl;
return all_pass;
}
......
profiler/src/profile_gemm_multiply_multiply.cpp
View file @ f84e2020
......@@ -34,7 +34,7 @@ enum struct GemmDataType
int profile_gemm_multiply_multiply(int argc, char* argv[])
{
if(argc != 16 && argc != 19)
if(argc != 16 && argc != 20)
{
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: "
......@@ -50,9 +50,10 @@ int profile_gemm_multiply_multiply(int argc, char* argv[])
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 15: M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE\n");
printf("optional:\n");
printf("arg16: number of warm-up cycles (default 1)\n");
printf("arg17: number of iterations (default 10)\n");
printf("arg18: memory for rotating buffer (default 0, size in MB)\n");
printf("arg16: number of kbatch (default 1)\n");
printf("arg17: number of warm-up cycles (default 1)\n");
printf("arg18: number of iterations (default 10)\n");
printf("arg19: memory for rotating buffer (default 0, size in MB)\n");
exit(1);
}
......@@ -76,11 +77,13 @@ int profile_gemm_multiply_multiply(int argc, char* argv[])
int n_warmup = 1;
int n_iter = 10;
uint64_t rotating = 0;
if(argc == 19)
int KBatch = 1;
if(argc == 20)
{
n_warmup = std::stoi(argv[16]);
n_iter = std::stoi(argv[17]);
rotating = std::stoull(argv[18]) * 1024 * 1024;
KBatch = std::stoi(argv[16]);
n_warmup = std::stoi(argv[17]);
n_iter = std::stoi(argv[18]);
rotating = std::stoull(argv[19]) * 1024 * 1024;
}
using F32 = float;
......@@ -146,6 +149,7 @@ int profile_gemm_multiply_multiply(int argc, char* argv[])
(StrideD0 < 0) ? DefaultStrideD0 : StrideD0,
(StrideD1 < 0) ? DefaultStrideD1 : StrideD1,
(StrideE < 0) ? DefaultStrideE : StrideE,
KBatch,
n_warmup,
n_iter,
rotating);
......
profiler/src/profile_gemm_universal.cpp
View file @ f84e2020
......@@ -171,6 +171,10 @@ int profile_gemm_universal(int argc, char* argv[])
{
return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
{
return profile(F8{}, F8{}, F8{}, F32{}, BF16{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(F8{}, F8{}, F8{}, F32{}, BF16{}, Row{}, Col{}, Row{});
......
profiler/src/profile_grouped_conv_bwd_weight.cpp
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <initializer_list>
......@@ -81,7 +81,6 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 9, argv);
ck::index_t split_k = std::stoi(argv[8 + 1 + 4 + 6 * num_dim_spatial]);
split_k = std::max(1, split_k);
using F32 = float;
using F16 = ck::half_t;
......
profiler/src/profile_grouped_gemm_fixed_nk.cpp
View file @ f84e2020
......@@ -85,9 +85,11 @@ int profile_grouped_gemm_fixed_nk(int argc, char* argv[])
const auto StrideCs = argToIntArray(argv[13]);
const int kbatch = argc == 15 ? std::stoi(argv[14]) : 1;
using F32 = float;
using F16 = ck::half_t;
using F8 = ck::f8_t;
using F32 = float;
using F16 = ck::half_t;
#if defined(CK_ENABLE_FP8)
using F8 = ck::f8_t;
#endif
using BF16 = ck::bhalf_t;
using I8 = int8_t;
......
script/convert_miopen_driver_to_profiler.py 0 → 100644
View file @ f84e2020
# SPDX-License-Identifier: MIT
# Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
# Convert miopen driver command to ck Profiler
# Example: python3 ../script/convert_miopen_driver_to_profiler.py
# /opt/rocm/bin/MIOpenDriver conv -n 32 -c 64 -H 28 -W 28 -k 64 -y 3 -x 3
# -p 1 -q 1 -u 2 -v 2 -l 1 -j 1 -m conv -g 32 -F 1 -t 1
import argparse
import subprocess
def init_const_args(args):
args.ck_profiler_cmd = '../build/bin/ckProfiler'
# use decimal values
args.init_method = 2
# don't print tensor values
args.log_value = 0
def run_ck_profiler_cmd(cmd):
print("ckProfiler command:")
print(cmd)
subprocess.run(cmd)
def parse_data_type(args):
if args.data_type == "fp32":
if args.ck_profier_op == "grouped_conv_bwd_weight" or \
args.ck_profier_op == "grouped_conv_bwd_data" or \
args.ck_profier_op == "grouped_conv_fwd":
args.data_type = 0
if args.data_type == "fp16":
if args.ck_profier_op == "grouped_conv_bwd_weight" or \
args.ck_profier_op == "grouped_conv_bwd_data" or \
args.ck_profier_op == "grouped_conv_fwd":
args.data_type = 1
if args.data_type == "int8":
if args.ck_profier_op == "grouped_conv_bwd_weight":
args.data_type = 4
if args.ck_profier_op == "grouped_conv_bwd_data":
print('Not supported data type for grouped_conv_bwd_data')
exit(1)
if args.ck_profier_op == "grouped_conv_fwd":
args.data_type = 3
if args.data_type == "bfp16":
if args.ck_profier_op == "grouped_conv_bwd_weight" or \
args.ck_profier_op == "grouped_conv_bwd_data" or \
args.ck_profier_op == "grouped_conv_fwd":
args.data_type = 2
def add_conv_params_to_cmd(args, cmd):
if args.spatial_dim == 1:
cmd += [str(args.fil_w), str(args.in_w)]
cmd += [str(args.conv_stride_w), str(args.dilation_w)]
cmd += [str(args.pad_w), str(args.pad_w)]
elif args.spatial_dim == 2:
cmd += [str(args.fil_h), str(args.fil_w)]
cmd += [str(args.in_h), str(args.in_w)]
cmd += [str(args.conv_stride_h), str(args.conv_stride_w)]
cmd += [str(args.dilation_h), str(args.dilation_w)]
cmd += [str(args.pad_h), str(args.pad_w)]
cmd += [str(args.pad_h), str(args.pad_w)]
elif args.spatial_dim == 3:
cmd += [str(args.fil_d), str(args.fil_h), str(args.fil_w)]
cmd += [str(args.in_d), str(args.in_h), str(args.in_w)]
cmd += [str(args.conv_stride_d), str(args.conv_stride_h)]
cmd += [str(args.conv_stride_w)]
cmd += [str(args.dilation_d),
str(args.dilation_h),
str(args.dilation_w)]
cmd += [str(args.pad_d), str(args.pad_h), str(args.pad_w)]
cmd += [str(args.pad_d), str(args.pad_h), str(args.pad_w)]
else:
print('Not supported spatial dim (supported: 1, 2, 3)')
exit(1)
def run_ck_grouped_conv_fwd(args):
args.ck_profier_op = "grouped_conv_fwd"
parse_data_type(args)
# default for MIOpen NHWGC
args.layout = 1
# use int32 by default
args.index_type = 0
cmd = [str(args.ck_profiler_cmd), str(args.ck_profier_op)]
cmd += [str(args.data_type), str(args.layout), str(args.index_type)]
cmd += [str(args.verify), str(args.init_method)]
cmd += [str(args.log_value), str(args.time)]
cmd += [str(args.spatial_dim), str(args.group_count)]
cmd += [str(args.batchsize), str(args.out_channels)]
cmd += [str(args.in_channels)]
add_conv_params_to_cmd(args, cmd)
run_ck_profiler_cmd(cmd)
def run_ck_grouped_conv_bwd_data(args):
args.ck_profier_op = "grouped_conv_bwd_data"
parse_data_type(args)
# default for MIOpen NHWGC
args.layout = 1
cmd = [str(args.ck_profiler_cmd), str(args.ck_profier_op)]
cmd += [str(args.data_type), str(args.layout)]
cmd += [str(args.verify), str(args.init_method)]
cmd += [str(args.log_value), str(args.time)]
cmd += [str(args.spatial_dim), str(args.group_count)]
cmd += [str(args.batchsize), str(args.out_channels)]
cmd += [str(args.in_channels)]
add_conv_params_to_cmd(args, cmd)
run_ck_profiler_cmd(cmd)
def run_ck_grouped_conv_bwd_weight(args):
args.ck_profier_op = "grouped_conv_bwd_weight"
parse_data_type(args)
# default for MIOpen NHWGC
args.layout = 2
# Test all split K value from the list {1, 2, 4, 8, 32, 64, 128}
args.split_k_value = -1
cmd = [str(args.ck_profiler_cmd), str(args.ck_profier_op)]
cmd += [str(args.data_type), str(args.layout)]
cmd += [str(args.verify), str(args.init_method)]
cmd += [str(args.log_value), str(args.time)]
cmd += [str(args.spatial_dim), str(args.group_count)]
cmd += [str(args.batchsize), str(args.out_channels)]
cmd += [str(args.in_channels)]
add_conv_params_to_cmd(args, cmd)
cmd += [str(args.split_k_value)]
run_ck_profiler_cmd(cmd)
# Get name of miopen driver, remove it from unknown
def process_miopen_driver_name(args, unknown):
if "convint8" in unknown:
args.data_type = 'int8'
unknown.remove("convint8")
elif "convbfp16" in unknown:
args.data_type = 'bfp16'
unknown.remove("convbfp16")
elif "convfp16" in unknown:
args.data_type = 'fp16'
unknown.remove("convfp16")
elif "conv" in unknown:
args.data_type = 'fp32'
unknown.remove("conv")
else:
print('Not supported driver (supported: conv, convfp16, convint8,'
' convbfp16).')
exit(1)
def run_ck_profiler(args):
# MIOpen get number of channel per all groups, CK profiler get number of
# channel per group
args.in_channels = int(args.in_channels / args.group_count)
args.out_channels = int(args.out_channels / args.group_count)
if args.forw == 0 or args.forw == 1 or args.forw == 3 or args.forw == 5:
run_ck_grouped_conv_fwd(args)
if args.forw == 0 or args.forw == 2 or args.forw == 3 or args.forw == 6:
run_ck_grouped_conv_bwd_data(args)
if args.forw == 0 or args.forw == 4 or args.forw == 5 or args.forw == 6:
run_ck_grouped_conv_bwd_weight(args)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog="converter",
description="Convert miopen driver command to ck Profiler"
"\nExample: python3 "
"../script/convert_miopen_driver_to_profiler.py "
"/opt/rocm/bin/MIOpenDriver conv -n 32 -c 64 -H 28 -W 28 "
"-k 64 -y 3 -x 3 -p 1 -q 1 -u 1 -v 1 -l 1 -j 1 -m conv -g "
"32 -F 1 -t 1",
)
parser.add_argument(
"-in_layout",
"-I",
default=-1,
type=int,
required=False,
help="Input Layout (Default=NCHW for 2d conv, NCDHW for 3d conv)"
)
parser.add_argument(
"-forw",
"-F",
default=0,
type=int,
required=False,
help="Flag enables fwd, bwd, wrw convolutions"
"\n0 fwd+bwd+wrw (default)"
"\n1 fwd only"
"\n2 bwd only"
"\n4 wrw only"
"\n3 fwd+bwd"
"\n5 fwd+wrw"
"\n6 bwd+wrw"
)
parser.add_argument(
"-spatial_dim",
"-_",
default=2,
type=int,
required=False,
help="convolution spatial dimension (Default-2)"
)
parser.add_argument(
"-batchsize",
"-n",
default=100,
type=int,
required=False,
help="Mini-batch size (Default=100)"
)
parser.add_argument(
"-in_channels",
"-c",
default=3,
type=int,
required=False,
help="Number of Input Channels (Default=3)"
)
parser.add_argument(
"-in_d",
"-!",
default=32,
type=int,
required=False,
help="Input Depth (Default=32)"
)
parser.add_argument(
"-in_h",
"-H",
default=32,
type=int,
required=False,
help="Input Height (Default=32)"
)
parser.add_argument(
"-in_w",
"-W",
default=32,
type=int,
required=False,
help="Input Width (Default=32)"
)
parser.add_argument(
"-out_channels",
"-k",
default=32,
type=int,
required=False,
help="Number of Output Channels (Default=32)"
)
parser.add_argument(
"-fil_d",
"-@",
default=3,
type=int,
required=False,
help="Filter Depth (Default=3)"
)
parser.add_argument(
"-fil_h",
"-y",
default=3,
type=int,
required=False,
help="Filter Height (Default=3)"
)
parser.add_argument(
"-fil_w",
"-x",
default=3,
type=int,
required=False,
help="Filter Width (Default=3)"
)
parser.add_argument(
"-conv_stride_d",
"-#",
default=1,
type=int,
required=False,
help="Convolution Stride for Depth (Default=1)"
)
parser.add_argument(
"-conv_stride_h",
"-u",
default=1,
type=int,
required=False,
help="Convolution Stride for Height (Default=1)"
)
parser.add_argument(
"-conv_stride_w",
"-v",
default=1,
type=int,
required=False,
help="Convolution Stride for Width (Default=1)"
)
parser.add_argument(
"-pad_d",
"-$",
default=1,
type=int,
required=False,
help="Zero Padding for Depth (Default=0)"
)
parser.add_argument(
"-pad_h",
"-p",
default=1,
type=int,
required=False,
help="Zero Padding for Height (Default=0)"
)
parser.add_argument(
"-pad_w",
"-q",
default=1,
type=int,
required=False,
help="Zero Padding for Width (Default=0)"
)
parser.add_argument(
"-verify",
"-V",
default=1,
type=int,
required=False,
help="Verify Each Layer (Default=1)"
)
parser.add_argument(
"-time",
"-t",
default=0,
type=int,
required=False,
help="Time Each Layer (Default=0)"
)
parser.add_argument(
"-dilation_d",
"-^",
default=1,
type=int,
required=False,
help="Dilation of Filter Depth (Default=1)"
)
parser.add_argument(
"-dilation_h",
"-l",
default=1,
type=int,
required=False,
help="Dilation of Filter Height (Default=1)"
)
parser.add_argument(
"-dilation_w",
"-j",
default=1,
type=int,
required=False,
help="Dilation of Filter Width (Default=1)"
)
parser.add_argument(
"-group_count",
"-g",
type=int,
default=1,
required=False,
help="Number of Groups (Default=1)"
)
args, unknown = parser.parse_known_args()
init_const_args(args)
process_miopen_driver_name(args, unknown)
print("Ignored args:")
print(unknown)
run_ck_profiler(args)
script/process_perf_data.py
View file @ f84e2020
......@@ -122,7 +122,7 @@ def parse_logfile(logfile):
#sorted_kernels = [x for _,x in sorted(zip(tests,kernels))]
test_list=list(range(1,len(tests)+1))
#parse conv_fwd and conv_bwd performance tests:
elif 'conv_fwd' in logfile or 'conv_bwd_data' in logfile:
elif 'conv_fwd' in logfile or 'conv_bwd' in logfile:
for line in open(logfile):
if 'tflops:' in line:
lst=line.split()
......@@ -274,14 +274,26 @@ def main():
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_grouped_gemm_tflops"
if 'conv_fwd' in filename:
if 'perf_conv_fwd' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_conv_fwd_tflops"
if 'conv_bwd_data' in filename:
if 'perf_conv_bwd_data' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_conv_bwd_data_tflops"
if 'grouped_conv_fwd' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_grouped_conv_fwd_tflops"
if 'grouped_conv_bwd_data' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_grouped_conv_bwd_data_tflops"
if 'grouped_conv_bwd_weight' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
table_name="ck_grouped_conv_bwd_weight_tflops"
if 'gemm_bilinear' in filename:
for i in range(1,len(results)+1):
testlist.append("Test%i"%i)
......
script/process_qa_data.sh
View file @ f84e2020
......@@ -15,8 +15,9 @@ python3 process_perf_data.py perf_resnet50_N256.log
python3 process_perf_data.py perf_resnet50_N4.log
python3 process_perf_data.py perf_batched_gemm.log
python3 process_perf_data.py perf_grouped_gemm.log
python3 process_perf_data.py perf_conv_fwd.log
python3 process_perf_data.py perf_conv_bwd_data.log
python3 process_perf_data.py perf_grouped_conv_fwd.log
python3 process_perf_data.py perf_grouped_conv_bwd_data.log
python3 process_perf_data.py perf_grouped_conv_bwd_weight.log
python3 process_perf_data.py perf_gemm_bilinear.log
python3 process_perf_data.py perf_reduction.log
python3 process_perf_data.py perf_splitK_gemm.log
......
script/profile_conv_fwd.sh → script/profile_grouped_conv_bwd_weight.sh
View file @ f84e2020
......@@ -12,27 +12,28 @@ INIT=$5
LOG=$6
TIME=$7
N=$8
N=$8
SplitK=$9
# Resnet50
######## op datatype layout verify init log time conv_dim G__ N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 1024 1 1 14 14 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 1024 1 1 14 14 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 128 3 3 28 28 1 1 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 128 1 1 28 28 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 128 3 3 56 56 2 2 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 2048 1 1 7 7 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 1024 256 1 1 14 14 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 256 3 3 14 14 1 1 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 256 3 3 28 28 2 2 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 256 1 1 56 56 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 256 1 1 56 56 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 512 3 3 14 14 2 2 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 512 1 1 28 28 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 512 1 1 28 28 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 2048 512 1 1 7 7 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 512 3 3 7 7 1 1 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 64 1 1 56 56 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 64 1 1 56 56 1 1 1 1 0 0 0 0
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 64 3 3 56 56 1 1 1 1 1 1 1 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 3 7 7 224 224 2 2 1 1 3 3 3 3
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 1024 1 1 14 14 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 1024 1 1 14 14 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 128 3 3 28 28 1 1 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 128 1 1 28 28 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 128 3 3 56 56 2 2 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 2048 1 1 7 7 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 1024 256 1 1 14 14 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 256 3 3 14 14 1 1 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 256 3 3 28 28 2 2 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 256 1 1 56 56 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 256 1 1 56 56 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 512 3 3 14 14 2 2 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 128 512 1 1 28 28 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 512 1 1 28 28 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 2048 512 1 1 7 7 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 512 512 3 3 7 7 1 1 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 256 64 1 1 56 56 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 64 1 1 56 56 1 1 1 1 0 0 0 0 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 64 3 3 56 56 1 1 1 1 1 1 1 1 $SplitK
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME 2 1 $N 64 3 7 7 224 224 2 2 1 1 3 3 3 3 $SplitK
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