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Commit b93575ca authored by Jing Zhang's avatar Jing Zhang
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merge develop

parents 54df59bf c8a8385f
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library/src/tensor_operation_instance/gpu/pool_fwd/CMakeLists.txt deleted 100644 → 0
View file @ 54df59bf
add_instance_library(device_pool_fwd_instance
device_avg_pool2d_fwd_nhwc_f16_instance.cpp
device_avg_pool2d_fwd_nhwc_f32_instance.cpp
device_avg_pool3d_fwd_ndhwc_f16_instance.cpp
device_avg_pool3d_fwd_ndhwc_f32_instance.cpp
device_max_pool2d_fwd_nhwc_f16_instance.cpp
device_max_pool2d_fwd_nhwc_f32_instance.cpp
device_max_pool3d_fwd_ndhwc_f16_instance.cpp
device_max_pool3d_fwd_ndhwc_f32_instance.cpp
)
library/src/tensor_operation_instance/gpu/pool_fwd/device_avg_pool2d_fwd_nhwc_f32_instance.cpp deleted 100644 → 0
View file @ 54df59bf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "pool_fwd_instance_common.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
void add_device_pool2d_fwd_nhwc_f32_instances(
std::vector<std::unique_ptr<DevicePoolFwd<4, 2, F32, F32, I32, ReduceOpId, false>>>& instances)
{
add_device_operation_instances(
instances, device_pool2d_fwd_nhwc_instances<F32, F32, I32, F32, ReduceOpId, false>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/pool_fwd/device_max_pool2d_fwd_nhwc_f16_instance.cpp deleted 100644 → 0
View file @ 54df59bf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "pool_fwd_instance_common.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
void add_device_pool2d_fwd_nhwc_f16_instances(
std::vector<std::unique_ptr<DevicePoolFwd<4, 2, F16, F16, I32, ReduceOpId, false>>>& instances)
{
add_device_operation_instances(
instances, device_pool2d_fwd_nhwc_instances<F16, F16, I32, F16, ReduceOpId, false>{});
}
void add_device_pool2d_fwd_nhwc_index_f16_instances(
std::vector<std::unique_ptr<DevicePoolFwd<4, 2, F16, F16, I32, ReduceOpId, true>>>& instances)
{
add_device_operation_instances(
instances, device_pool2d_fwd_nhwc_instances<F16, F16, I32, F16, ReduceOpId, true>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/pool_fwd/device_max_pool2d_fwd_nhwc_f32_instance.cpp deleted 100644 → 0
View file @ 54df59bf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "pool_fwd_instance_common.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
void add_device_pool2d_fwd_nhwc_f32_instances(
std::vector<std::unique_ptr<DevicePoolFwd<4, 2, F32, F32, I32, ReduceOpId, false>>>& instances)
{
add_device_operation_instances(
instances, device_pool2d_fwd_nhwc_instances<F32, F32, I32, F32, ReduceOpId, false>{});
}
void add_device_pool2d_fwd_nhwc_index_f32_instances(
std::vector<std::unique_ptr<DevicePoolFwd<4, 2, F32, F32, I32, ReduceOpId, true>>>& instances)
{
add_device_operation_instances(
instances, device_pool2d_fwd_nhwc_instances<F32, F32, I32, F32, ReduceOpId, true>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/quantization/CMakeLists.txt
View file @ b93575ca
if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES) if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
set(CONV2D_PERLAYER_QUANT_SRC
conv2d_fwd/device_conv2d_dl_perlayer_quantization_int8_instance.cpp
conv2d_fwd/device_conv2d_xdl_perlayer_quantization_int8_instance.cpp
)
set(CONV2D_PERCHANNEL_QUANT_SRC
conv2d_fwd/device_conv2d_dl_perchannel_quantization_int8_instance.cpp
conv2d_fwd/device_conv2d_xdl_perchannel_quantization_int8_instance.cpp
)
set(CONV2D_BIAS_PERLAYER_QUANT_SRC
conv2d_fwd/device_conv2d_dl_bias_perlayer_quantization_int8_instance.cpp
conv2d_fwd/device_conv2d_xdl_bias_perlayer_quantization_int8_instance.cpp
)
set(CONV2D_BIAS_PERCHANNEL_QUANT_SRC
conv2d_fwd/device_conv2d_dl_bias_perchannel_quantization_int8_instance.cpp
conv2d_fwd/device_conv2d_xdl_bias_perchannel_quantization_int8_instance.cpp
)
set(CONV2D_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_xdl_perlayer_quantization_int8_instance.cpp)
set(CONV2D_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_xdl_perchannel_quantization_int8_instance.cpp)
set(CONV2D_BIAS_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_xdl_bias_perlayer_quantization_int8_instance.cpp)
set(CONV2D_BIAS_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_xdl_bias_perchannel_quantization_int8_instance.cpp)
set(GEMM_QUANT_SRC set(GEMM_QUANT_SRC
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_nk_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp
gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instance.cpp gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instance.cpp
gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instance.cpp gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instance.cpp
gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp
gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp gemm/device_gemm_quantization_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp
) )
if(DL_KERNELS)
list(APPEND CONV2D_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_dl_perlayer_quantization_int8_instance.cpp)
list(APPEND CONV2D_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_dl_perchannel_quantization_int8_instance.cpp)
list(APPEND CONV2D_BIAS_PERLAYER_QUANT_SRC conv2d_fwd/device_conv2d_dl_bias_perlayer_quantization_int8_instance.cpp)
list(APPEND CONV2D_BIAS_PERCHANNEL_QUANT_SRC conv2d_fwd/device_conv2d_dl_bias_perchannel_quantization_int8_instance.cpp)
list(APPEND GEMM_QUANT_SRC
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_km_nk_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_kn_mn_instance.cpp
gemm/device_gemm_quantization_dl_c_shuffle_i8_i8_i8_mk_nk_mn_instance.cpp)
endif()
add_instance_library(device_quantization_instance add_instance_library(device_quantization_instance
${CONV2D_PERLAYER_QUANT_SRC} ${CONV2D_PERLAYER_QUANT_SRC}
......
library/src/tensor_operation_instance/gpu/softmax/CMakeLists.txt
View file @ b93575ca
add_instance_library(device_softmax_instance set(DEVICE_SOFTMAX_INSTANCES)
device_softmax_f16_f16_instance_rank3_reduce1.cpp if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_SOFTMAX_INSTANCES device_softmax_f16_f16_instance_rank3_reduce1.cpp
device_softmax_f16_f16_instance_rank3_reduce2.cpp device_softmax_f16_f16_instance_rank3_reduce2.cpp
device_softmax_f16_f16_instance_rank3_reduce3.cpp device_softmax_f16_f16_instance_rank3_reduce3.cpp
device_softmax_f16_f16_instance_rank4_reduce1.cpp device_softmax_f16_f16_instance_rank4_reduce1.cpp
device_softmax_f16_f16_instance_rank4_reduce2.cpp device_softmax_f16_f16_instance_rank4_reduce2.cpp
device_softmax_f16_f16_instance_rank4_reduce3.cpp device_softmax_f16_f16_instance_rank4_reduce3.cpp
device_softmax_f16_f16_instance_rank4_reduce4.cpp device_softmax_f16_f16_instance_rank4_reduce4.cpp)
device_softmax_f32_f32_instance_rank3_reduce1.cpp endif()
if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
list(APPEND DEVICE_SOFTMAX_INSTANCES device_softmax_f32_f32_instance_rank3_reduce1.cpp
device_softmax_f32_f32_instance_rank3_reduce2.cpp device_softmax_f32_f32_instance_rank3_reduce2.cpp
device_softmax_f32_f32_instance_rank3_reduce3.cpp device_softmax_f32_f32_instance_rank3_reduce3.cpp
device_softmax_f32_f32_instance_rank4_reduce1.cpp device_softmax_f32_f32_instance_rank4_reduce1.cpp
device_softmax_f32_f32_instance_rank4_reduce2.cpp device_softmax_f32_f32_instance_rank4_reduce2.cpp
device_softmax_f32_f32_instance_rank4_reduce3.cpp device_softmax_f32_f32_instance_rank4_reduce3.cpp
device_softmax_f32_f32_instance_rank4_reduce4.cpp device_softmax_f32_f32_instance_rank4_reduce4.cpp)
) endif()
add_instance_library(device_softmax_instance ${DEVICE_SOFTMAX_INSTANCES})
profiler/include/profiler/profile_gemm_splitk_impl.hpp
View file @ b93575ca
...@@ -94,7 +94,6 @@ bool profile_gemm_splitk_impl(int do_verification, ...@@ -94,7 +94,6 @@ bool profile_gemm_splitk_impl(int do_verification,
a_device_buf.ToDevice(a_m_k.mData.data()); a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data()); b_device_buf.ToDevice(b_k_n.mData.data());
c_device_buf.SetZero();
using DeviceOp = ck::tensor_operation::device::DeviceGemmSplitK<ALayout, using DeviceOp = ck::tensor_operation::device::DeviceGemmSplitK<ALayout,
BLayout, BLayout,
...@@ -136,77 +135,114 @@ bool profile_gemm_splitk_impl(int do_verification, ...@@ -136,77 +135,114 @@ bool profile_gemm_splitk_impl(int do_verification,
float best_ave_time = 0; float best_ave_time = 0;
float best_tflops = 0; float best_tflops = 0;
float best_gb_per_sec = 0; float best_gb_per_sec = 0;
float best_kbatch = 0;
// profile device GEMM instances // profile device GEMM instances
for(auto& op_ptr : op_ptrs) for(auto& op_ptr : op_ptrs)
{ {
auto argument_ptr = std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 24, 32, 36, 40, 60,
op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()), 64, 72, 80, 88, 96, 128, 144, 160, 176, 192, 256};
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()), if(KBatch > 0)
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op,
KBatch);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{ {
// re-init C to zero before profiling next kernel kbatch_list = {KBatch};
c_device_buf.SetZero(); }
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()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op,
kbatch_curr);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
std::string op_name = op_ptr->GetTypeString(); // re-init C to zero before profiling next kernel
c_device_buf.SetZero();
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K; if(do_verification)
{
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(c_m_n_device_result, c_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 : ", c_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_device: ", c_m_n_device_result.mData, ",")
<< std::endl;
}
}
std::size_t num_btype = std::string op_name = op_ptr->GetTypeString();
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
float gb_per_sec = num_btype / 1.E6 / ave_time; std::size_t flop = std::size_t(2) * M * N * K;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, " std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
<< gb_per_sec << " GB/s, " << op_name << std::endl; sizeof(CDataType) * M * N;
if(tflops > best_tflops) float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification) float gb_per_sec = num_btype / 1.E6 / ave_time;
{
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result); std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
<< kbatch_curr << std::endl;
if(do_log) // set softer tolerances for fp8
if constexpr(is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
is_same_v<CDataType, f8_t>)
{ {
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl; std::string msg = "Error: Incorrect results!";
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl; double rtol = 1e-1;
LogRangeAsType<float>(std::cout << "c_host : ", c_m_n_host_result.mData, ",") double atol = 1e-1;
<< std::endl; pass = pass & ck::utils::check_err(
LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",") c_m_n_device_result, c_m_n_host_result, msg, rtol, atol);
<< std::endl; }
else
{
pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
if(tflops > best_tflops)
{
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
else {
{ std::cout << op_ptr->GetTypeString() << " does not support this problem"
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl; << std::endl;
}
} }
} }
...@@ -246,7 +282,7 @@ bool profile_gemm_splitk_impl(int do_verification, ...@@ -246,7 +282,7 @@ bool profile_gemm_splitk_impl(int do_verification,
} }
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " KBatch = " << KBatch << " StrideB = " << StrideB << " StrideC = " << StrideC << " KBatch = " << best_kbatch
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
<< " GB/s, " << best_op_name << std::endl; << " GB/s, " << best_op_name << std::endl;
......
profiler/include/profiler/profile_gemm_streamk_impl.hpp
View file @ b93575ca
...@@ -170,6 +170,25 @@ bool profile_gemm_streamk_impl(int do_verification, ...@@ -170,6 +170,25 @@ bool profile_gemm_streamk_impl(int do_verification,
// re-init C to zero before profiling next kernel // re-init C to zero before profiling next kernel
c_device_buf.SetZero(); c_device_buf.SetZero();
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
if(do_verification)
{
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(c_m_n_device_result, c_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 : ", c_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
<< std::endl;
}
}
std::string op_name = op_ptr->GetTypeString(); std::string op_name = op_ptr->GetTypeString();
float ave_time = float ave_time =
...@@ -194,23 +213,6 @@ bool profile_gemm_streamk_impl(int do_verification, ...@@ -194,23 +213,6 @@ bool profile_gemm_streamk_impl(int do_verification,
best_ave_time = ave_time; best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec; best_gb_per_sec = gb_per_sec;
} }
if(do_verification)
{
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(c_m_n_device_result, c_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 : ", c_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
<< std::endl;
}
}
} }
else else
{ {
......
profiler/include/profiler/profile_grouped_conv_bwd_weight_impl.hpp
View file @ b93575ca
...@@ -136,10 +136,11 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -136,10 +136,11 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
// profile device Conv instances // profile device Conv instances
bool all_pass = true; bool all_pass = true;
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{}; std::array<ck::index_t, NDimSpatial + 3> input_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{}; std::array<ck::index_t, NDimSpatial + 3> filter_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{}; std::array<ck::index_t, NDimSpatial + 3> output_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_strides{}; std::array<ck::index_t, NDimSpatial + 3> input_strides{};
std::array<ck::index_t, NDimSpatial + 3> weights_strides{};
std::array<ck::index_t, NDimSpatial + 3> output_strides{}; std::array<ck::index_t, NDimSpatial + 3> output_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{}; std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{}; std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
...@@ -148,10 +149,11 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -148,10 +149,11 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
auto range_copy = [](const auto& from, auto to) { std::copy(begin(from), end(from), to); }; auto range_copy = [](const auto& from, auto to) { std::copy(begin(from), end(from), to); };
range_copy(conv_param.input_spatial_lengths_, begin(input_spatial_lengths)); range_copy(in_g_n_c_wis_desc.GetLengths(), begin(input_lengths));
range_copy(conv_param.filter_spatial_lengths_, begin(filter_spatial_lengths));
range_copy(conv_param.output_spatial_lengths_, begin(output_spatial_lengths));
range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides)); range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides));
range_copy(wei_g_k_c_xs_desc.GetLengths(), begin(filter_lengths));
range_copy(wei_g_k_c_xs_desc.GetStrides(), begin(weights_strides));
range_copy(out_g_n_k_wos_desc.GetLengths(), begin(output_lengths));
range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides)); range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides));
range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides)); range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations)); range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
...@@ -164,14 +166,11 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -164,14 +166,11 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()), op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()), static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()), static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
conv_param.G_, input_lengths,
conv_param.N_,
conv_param.K_,
conv_param.C_,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
input_strides, input_strides,
filter_lengths,
weights_strides,
output_lengths,
output_strides, output_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
......
profiler/include/profiler/profile_grouped_gemm_impl.hpp
View file @ b93575ca
...@@ -70,6 +70,7 @@ bool profile_grouped_gemm_impl(int do_verification, ...@@ -70,6 +70,7 @@ bool profile_grouped_gemm_impl(int do_verification,
std::vector<Tensor<ADataType>> a_m_k; std::vector<Tensor<ADataType>> a_m_k;
std::vector<Tensor<BDataType>> b_k_n; std::vector<Tensor<BDataType>> b_k_n;
std::vector<Tensor<CDataType>> c_m_n_host_results;
std::vector<Tensor<CDataType>> c_m_n_device_results; std::vector<Tensor<CDataType>> c_m_n_device_results;
for(std::size_t i = 0; i < group_count; i++) for(std::size_t i = 0; i < group_count; i++)
...@@ -81,6 +82,9 @@ bool profile_grouped_gemm_impl(int do_verification, ...@@ -81,6 +82,9 @@ bool profile_grouped_gemm_impl(int do_verification,
c_m_n_device_results.push_back( c_m_n_device_results.push_back(
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{}))); Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
c_m_n_host_results.push_back(
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
#if DEBUG_LOG #if DEBUG_LOG
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
<< "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i << "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
...@@ -137,7 +141,6 @@ bool profile_grouped_gemm_impl(int do_verification, ...@@ -137,7 +141,6 @@ bool profile_grouped_gemm_impl(int do_verification,
a_device_buf[i]->ToDevice(a_m_k[i].mData.data()); a_device_buf[i]->ToDevice(a_m_k[i].mData.data());
b_device_buf[i]->ToDevice(b_k_n[i].mData.data()); b_device_buf[i]->ToDevice(b_k_n[i].mData.data());
c_device_buf[i]->SetZero();
gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}}); gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
...@@ -170,9 +173,36 @@ bool profile_grouped_gemm_impl(int do_verification, ...@@ -170,9 +173,36 @@ bool profile_grouped_gemm_impl(int do_verification,
float best_ave_time = 0; float best_ave_time = 0;
float best_tflops = 0; float best_tflops = 0;
float best_gb_per_sec = 0; float best_gb_per_sec = 0;
float best_kbatch = 0;
auto p_ds = std::vector<std::array<const void*, 0>>{}; auto p_ds = std::vector<std::array<const void*, 0>>{};
if(do_verification)
{
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_m_k[i],
b_k_n[i],
c_m_n_host_results[i],
a_element_op,
b_element_op,
c_element_op);
ref_invoker.Run(ref_argument);
}
}
// profile device GEMM instances // profile device GEMM instances
for(auto& gemm_ptr : op_ptrs) for(auto& gemm_ptr : op_ptrs)
{ {
...@@ -193,139 +223,135 @@ bool profile_grouped_gemm_impl(int do_verification, ...@@ -193,139 +223,135 @@ bool profile_grouped_gemm_impl(int do_verification,
gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer()); gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
std::string gemm_name = gemm_ptr->GetTypeString(); std::string gemm_name = gemm_ptr->GetTypeString();
if(kbatch > 1) using DeviceOpSplitK = ck::tensor_operation::device::DeviceGroupedGemmSplitK<ALayout,
BLayout,
ck::Tuple<>,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
CDataType,
AElementOp,
BElementOp,
CElementOp>;
// skip non-splitk grouped_gemm
if(dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get()) == nullptr)
{ {
using DeviceOpSplitK = continue;
ck::tensor_operation::device::DeviceGroupedGemmSplitK<ALayout,
BLayout,
ck::Tuple<>,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
CDataType,
AElementOp,
BElementOp,
CElementOp>;
if(dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get()) != nullptr)
{
dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->SetKBatchSize(argument_ptr.get(), kbatch);
}
} }
if(gemm_ptr->IsSupportedArgument(argument_ptr.get())) std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 24, 32, 48, 64};
if(kbatch > 0)
{ {
kbatch_list = {kbatch};
}
float ave_time = for(std::size_t j = 0; j < kbatch_list.size(); j++)
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel}); {
auto kbatch_curr = kbatch_list[j];
dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->SetKBatchSize(argument_ptr.get(), kbatch_curr);
if(time_kernel) if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{ {
std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_descs.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ c_device_buf[i]->SetZero();
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] + invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
sizeof(BDataType) * Ks[i] * Ns[i] +
sizeof(CDataType) * Ms[i] * Ns[i];
}
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; if(do_verification)
{
bool instance_pass = true;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
float gb_per_sec = num_btype / 1.E6 / ave_time; c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << gemm_name << std::endl; if(std::is_same_v<CDataType, ck::half_t> && kbatch_curr > 1)
{
instance_pass =
instance_pass && ck::utils::check_err(c_m_n_device_results[i],
c_m_n_host_results[i],
"Error: Incorrect results!",
0.06);
}
else
{
instance_pass =
instance_pass && ck::utils::check_err(c_m_n_device_results[i],
c_m_n_host_results[i]);
}
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a_m_k[i].mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_k_n[i].mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_device: ", c_m_n_device_results[i].mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_host : ", c_m_n_host_results[i].mData, ",")
<< std::endl;
}
}
if(tflops > best_tflops) std::cout << "Instance: " << gemm_name << " verification "
{ << (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
best_gemm_name = gemm_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
if(do_verification) pass = pass && instance_pass;
{ }
bool instance_pass = true;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data()); float ave_time =
c_device_buf[i]->SetZero(); invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
Tensor<CDataType> c_m_n_host_result( if(time_kernel)
f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})); {
std::size_t flop = 0, num_btype = 0;
using ReferenceGemmInstance = for(std::size_t i = 0; i < gemm_descs.size(); i++)
ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_m_k[i],
b_k_n[i],
c_m_n_host_result,
a_element_op,
b_element_op,
c_element_op);
ref_invoker.Run(ref_argument);
if(std::is_same_v<CDataType, ck::half_t> && kbatch > 1)
{
instance_pass =
instance_pass && ck::utils::check_err(c_m_n_device_results[i],
c_m_n_host_result,
"Error: Incorrect results!",
0.06);
}
else
{ {
instance_pass = flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
instance_pass &&
ck::utils::check_err(c_m_n_device_results[i], c_m_n_host_result); num_btype += sizeof(ADataType) * Ms[i] * Ks[i] +
sizeof(BDataType) * Ks[i] * Ns[i] +
sizeof(CDataType) * Ms[i] * Ns[i];
} }
if(do_log) float tflops = static_cast<float>(flop) / 1.E9 / 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, " << gemm_name << ", KBatch "
<< kbatch_curr << std::endl;
if(tflops > best_tflops)
{ {
LogRangeAsType<float>(std::cout << "a : ", a_m_k[i].mData, ",") best_gemm_name = gemm_name;
<< std::endl; best_tflops = tflops;
LogRangeAsType<float>(std::cout << "b: ", b_k_n[i].mData, ",") << std::endl; best_ave_time = ave_time;
LogRangeAsType<float>( best_gb_per_sec = gb_per_sec;
std::cout << "c_device: ", c_m_n_device_results[i].mData, ",") best_kbatch = kbatch_curr;
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_host : ", c_m_n_host_result.mData, ",")
<< std::endl;
} }
} }
std::cout << "Instance: " << gemm_name << " verification "
<< (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
pass = pass && instance_pass;
} }
} else
else {
{ std::cout << "Instance: " << gemm_name << ", does not support this GEMM problem"
std::cout << "Instance: " << gemm_name << ", does not support this GEMM problem" << std::endl;
<< std::endl; }
} }
} }
if(time_kernel) if(time_kernel)
{ {
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, " std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl; << best_gb_per_sec << " GB/s, " << best_gemm_name << ", KBatch = " << best_kbatch
<< std::endl;
} }
return pass; return pass;
......
profiler/include/profiler/profile_groupnorm_impl.hpp
View file @ b93575ca
...@@ -139,6 +139,10 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -139,6 +139,10 @@ bool profile_groupnorm_impl(int do_verification,
continue; continue;
} }
size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
auto invoker_ptr = inst_ptr->MakeInvokerPointer(); auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel}); float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
......
profiler/include/profiler/profile_layernorm_impl.hpp
View file @ b93575ca
...@@ -155,6 +155,10 @@ bool profile_layernorm_impl(int do_verification, ...@@ -155,6 +155,10 @@ bool profile_layernorm_impl(int do_verification,
continue; continue;
} }
size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
auto invoker_ptr = inst_ptr->MakeInvokerPointer(); auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel}); float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
......
profiler/include/profiler/profile_pool2d_fwd_impl.hpp deleted 100644 → 0
View file @ 54df59bf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/pool2d_fwd.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_pool_fwd.hpp"
namespace ck {
namespace profiler {
template <typename InDataType,
typename OutDataType,
typename ComputeDataType,
typename IndexDataType,
ck::ReduceTensorOp ReduceOpId,
bool PropagateNan,
bool OutputIndex>
bool profile_pool2d_fwd_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
std::vector<index_t> in_length, // NCHW
std::vector<index_t> window_spatial_lengths,
std::vector<index_t> window_strides,
std::vector<index_t> input_left_pads,
std::vector<index_t> input_right_pads)
{
constexpr index_t InOutRank = 4;
constexpr index_t WindowRank = 2;
if(in_length.size() != InOutRank || window_spatial_lengths.size() != WindowRank ||
window_strides.size() != WindowRank || input_left_pads.size() != WindowRank ||
input_right_pads.size() != WindowRank)
return false;
std::vector<index_t> out_length(InOutRank);
int N = in_length[0];
int C = in_length[1];
out_length[0] = N;
out_length[1] = C;
// Calculate Ho, Wo
for(int i = 2; i < InOutRank; ++i)
{
auto pad1 = input_left_pads[i - 2];
auto pad2 = input_right_pads[i - 2];
auto windows_size = window_spatial_lengths[i - 2];
auto windows_stride = window_strides[i - 2];
out_length[i] = (in_length[i] + pad1 + pad2 - windows_size) / windows_stride + 1;
}
int Hi = in_length[2];
int Wi = in_length[3];
int Ho = out_length[2];
int Wo = out_length[3];
auto f_host_tensor_descriptor =
[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W) {
using namespace ck::literals;
return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, 1_uz, W * C_, C_});
};
Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi));
Tensor<OutDataType> out_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
Tensor<IndexDataType> out_indices_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
Tensor<OutDataType> out_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
Tensor<IndexDataType> out_indices_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
switch(init_method)
{
case 0: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{}); break;
case 1: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); break;
default: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{-0.5, 0.5});
}
DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpaceSize());
DeviceMem out_device_buf(sizeof(OutDataType) *
out_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
DeviceMem out_indices_device_buf(sizeof(IndexDataType) *
out_indices_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
// add device normalization instances
using DeviceOp = ck::tensor_operation::device::DevicePoolFwd<InOutRank,
WindowRank,
InDataType,
OutDataType,
IndexDataType,
ReduceOpId,
OutputIndex>;
// get device op instances
const auto instance_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
std::string best_instance_name;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
if(do_verification)
{
using ReferenceInstance = ck::tensor_operation::host::ReferencePoolingFwd<InOutRank,
WindowRank,
InDataType,
OutDataType,
ComputeDataType,
IndexDataType,
ReduceOpId,
PropagateNan,
OutputIndex>;
ReferenceInstance ref;
auto ref_argument = ref.MakeArgument(in_n_c_hi_wi,
out_n_c_ho_wo_host,
out_indices_n_c_ho_wo_host,
window_spatial_lengths,
window_strides,
input_left_pads,
input_right_pads);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument);
}
int num_kernel = 0;
for(auto& inst_ptr : instance_ptrs)
{
auto argument_ptr = inst_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
static_cast<IndexDataType*>(out_indices_device_buf.GetDeviceBuffer()),
in_length,
window_spatial_lengths,
out_length,
{C * Hi * Wi, 1, Wi * C, C},
{C * Ho * Wo, 1, Wo * C, C},
{C * Ho * Wo, 1, Wo * C, C},
window_strides,
input_left_pads,
input_right_pads,
{2, 3});
if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{
++num_kernel;
}
else
{
if(time_kernel)
{
std::cout << inst_ptr->GetTypeString() << " skipped due to unsupported argument: ";
LogRange(std::cout << "input lengths = ", in_length, ", ") << std::endl;
}
continue;
}
auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t num_bytes = in_n_c_hi_wi.mDesc.GetElementSize() * sizeof(InDataType) +
out_n_c_ho_wo_host.mDesc.GetElementSize() * sizeof(OutDataType);
if constexpr(OutputIndex)
num_bytes += out_indices_n_c_ho_wo_host.mDesc.GetElementSize() * sizeof(IndexDataType);
float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel)
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< inst_ptr->GetTypeString() << std::endl;
if(avg_time < best_avg_time)
{
best_instance_name = inst_ptr->GetTypeString();
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
bool pass = ck::utils::check_err(out_n_c_ho_wo_device.mData,
out_n_c_ho_wo_host.mData,
"Error: Incorrect results",
1e-3,
1e-3);
if constexpr(OutputIndex)
{
out_indices_device_buf.FromDevice(out_indices_n_c_ho_wo_device.mData.data());
pass = pass && ck::utils::check_err(out_indices_n_c_ho_wo_device,
out_indices_n_c_ho_wo_host);
}
if(do_log)
{
LogRangeAsType<float>(std::cout << "in_n_c_hi_wi : ", in_n_c_hi_wi.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "out_n_c_ho_wo_host : ", out_n_c_ho_wo_host.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "out_n_c_ho_wo_device : ", out_n_c_ho_wo_device.mData, ",")
<< std::endl;
if constexpr(OutputIndex)
LogRangeAsType<float>(std::cout << "out_indices_n_c_ho_wo_device : ",
out_indices_n_c_ho_wo_device.mData,
",")
<< std::endl;
}
if(!pass)
{
std::cout << inst_ptr->GetTypeString() << " failed verification: ";
LogRange(std::cout << "lengths = [", in_length, ", ") << "]." << std::endl;
return false;
}
else
{
if(time_kernel)
std::cout << "pass" << std::endl;
}
}
}
if(time_kernel)
{
LogRange(std::cout << "length = ", in_length, ",") << std::endl;
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_instance_name << std::endl;
}
if(num_kernel == 0)
{
std::cout << "Error: No kernel is applicable" << std::endl;
return false;
}
return true;
}
} // namespace profiler
} // namespace ck
profiler/include/profiler/profile_pool3d_fwd_impl.hpp
View file @ b93575ca
...@@ -21,6 +21,8 @@ template <typename InDataType, ...@@ -21,6 +21,8 @@ template <typename InDataType,
typename OutDataType, typename OutDataType,
typename ComputeDataType, typename ComputeDataType,
typename IndexDataType, typename IndexDataType,
typename InLayout,
typename OutLayout,
ck::ReduceTensorOp ReduceOpId, ck::ReduceTensorOp ReduceOpId,
bool PropagateNan, bool PropagateNan,
bool OutputIndex> bool OutputIndex>
...@@ -31,6 +33,7 @@ bool profile_pool3d_fwd_impl(int do_verification, ...@@ -31,6 +33,7 @@ bool profile_pool3d_fwd_impl(int do_verification,
std::vector<index_t> in_length, // NCDHW std::vector<index_t> in_length, // NCDHW
std::vector<index_t> window_spatial_lengths, std::vector<index_t> window_spatial_lengths,
std::vector<index_t> window_strides, std::vector<index_t> window_strides,
std::vector<index_t> window_dilations,
std::vector<index_t> input_left_pads, std::vector<index_t> input_left_pads,
std::vector<index_t> input_right_pads) std::vector<index_t> input_right_pads)
{ {
...@@ -38,8 +41,8 @@ bool profile_pool3d_fwd_impl(int do_verification, ...@@ -38,8 +41,8 @@ bool profile_pool3d_fwd_impl(int do_verification,
constexpr index_t WindowRank = 3; constexpr index_t WindowRank = 3;
if(in_length.size() != InOutRank || window_spatial_lengths.size() != WindowRank || if(in_length.size() != InOutRank || window_spatial_lengths.size() != WindowRank ||
window_strides.size() != WindowRank || input_left_pads.size() != WindowRank || window_strides.size() != WindowRank || window_dilations.size() != WindowRank ||
input_right_pads.size() != WindowRank) input_left_pads.size() != WindowRank || input_right_pads.size() != WindowRank)
return false; return false;
std::vector<index_t> out_length(InOutRank); std::vector<index_t> out_length(InOutRank);
...@@ -53,11 +56,13 @@ bool profile_pool3d_fwd_impl(int do_verification, ...@@ -53,11 +56,13 @@ bool profile_pool3d_fwd_impl(int do_verification,
// Calculate Do, Ho, Wo // Calculate Do, Ho, Wo
for(int i = 2; i < InOutRank; ++i) for(int i = 2; i < InOutRank; ++i)
{ {
auto pad1 = input_left_pads[i - 2]; auto pad1 = input_left_pads[i - 2];
auto pad2 = input_right_pads[i - 2]; auto pad2 = input_right_pads[i - 2];
auto windows_size = window_spatial_lengths[i - 2]; auto windows_size = window_spatial_lengths[i - 2];
auto windows_stride = window_strides[i - 2]; auto windows_stride = window_strides[i - 2];
out_length[i] = (in_length[i] + pad1 + pad2 - windows_size) / windows_stride + 1; auto windows_dilation = window_dilations[i - 2];
auto eff = (windows_size - 1) * windows_dilation + 1;
out_length[i] = (in_length[i] + pad1 + pad2 - eff) / windows_stride + 1;
} }
int Di = in_length[2]; int Di = in_length[2];
...@@ -104,6 +109,8 @@ bool profile_pool3d_fwd_impl(int do_verification, ...@@ -104,6 +109,8 @@ bool profile_pool3d_fwd_impl(int do_verification,
InDataType, InDataType,
OutDataType, OutDataType,
IndexDataType, IndexDataType,
InLayout,
OutLayout,
ReduceOpId, ReduceOpId,
OutputIndex>; OutputIndex>;
...@@ -136,6 +143,7 @@ bool profile_pool3d_fwd_impl(int do_verification, ...@@ -136,6 +143,7 @@ bool profile_pool3d_fwd_impl(int do_verification,
out_indices_n_c_do_ho_wo_host, out_indices_n_c_do_ho_wo_host,
window_spatial_lengths, window_spatial_lengths,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads); input_right_pads);
auto ref_invoker = ref.MakeInvoker(); auto ref_invoker = ref.MakeInvoker();
...@@ -157,6 +165,7 @@ bool profile_pool3d_fwd_impl(int do_verification, ...@@ -157,6 +165,7 @@ bool profile_pool3d_fwd_impl(int do_verification,
{Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C}, {Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C},
{Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C}, {Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C},
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3, 4}); {2, 3, 4});
......
profiler/src/CMakeLists.txt
View file @ b93575ca
...@@ -3,20 +3,11 @@ set(PROFILER_SOURCES ...@@ -3,20 +3,11 @@ set(PROFILER_SOURCES
profiler.cpp profiler.cpp
profile_gemm.cpp profile_gemm.cpp
profile_gemm_splitk.cpp profile_gemm_splitk.cpp
profile_gemm_streamk.cpp
profile_gemm_bilinear.cpp
profile_gemm_bias_add_reduce.cpp profile_gemm_bias_add_reduce.cpp
profile_gemm_add_add_fastgelu.cpp
profile_gemm_add_multiply.cpp profile_gemm_add_multiply.cpp
profile_gemm_add_fastgelu.cpp
profile_gemm_add_relu_add_layernorm.cpp
profile_gemm_fastgelu.cpp
profile_gemm_reduce.cpp profile_gemm_reduce.cpp
profile_batched_gemm.cpp profile_batched_gemm.cpp
profile_batched_gemm_gemm.cpp
profile_batched_gemm_add_relu_gemm_add.cpp
profile_batched_gemm_reduce.cpp profile_batched_gemm_reduce.cpp
profile_grouped_gemm.cpp
profile_conv_fwd.cpp profile_conv_fwd.cpp
profile_conv_fwd_bias_relu.cpp profile_conv_fwd_bias_relu.cpp
profile_conv_fwd_bias_relu_add.cpp profile_conv_fwd_bias_relu_add.cpp
...@@ -26,13 +17,11 @@ set(PROFILER_SOURCES ...@@ -26,13 +17,11 @@ set(PROFILER_SOURCES
profile_reduce.cpp profile_reduce.cpp
profile_groupnorm.cpp profile_groupnorm.cpp
profile_layernorm.cpp profile_layernorm.cpp
profile_avg_pool2d_fwd.cpp
profile_max_pool3d_fwd.cpp profile_max_pool3d_fwd.cpp
profile_softmax.cpp profile_softmax.cpp
profile_batchnorm_fwd.cpp profile_batchnorm_fwd.cpp
profile_batchnorm_bwd.cpp profile_batchnorm_bwd.cpp
profile_batchnorm_infer.cpp profile_batchnorm_infer.cpp
profile_grouped_gemm_fastgelu.cpp
profile_contraction_bilinear.cpp profile_contraction_bilinear.cpp
profile_contraction_scale.cpp profile_contraction_scale.cpp
profile_grouped_conv_bwd_data.cpp profile_grouped_conv_bwd_data.cpp
...@@ -40,6 +29,18 @@ set(PROFILER_SOURCES ...@@ -40,6 +29,18 @@ set(PROFILER_SOURCES
if(DL_KERNELS) if(DL_KERNELS)
list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp) list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
endif() endif()
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_streamk.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_bilinear.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_add_fastgelu.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_add_add_fastgelu.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_add_relu_add_layernorm.cpp)
list(APPEND PROFILER_SOURCES profile_batched_gemm_add_relu_gemm_add.cpp)
list(APPEND PROFILER_SOURCES profile_grouped_gemm.cpp)
list(APPEND PROFILER_SOURCES profile_grouped_gemm_fastgelu.cpp)
endif()
set(PROFILER_EXECUTABLE ckProfiler) set(PROFILER_EXECUTABLE ckProfiler)
...@@ -49,20 +50,11 @@ target_compile_options(${PROFILER_EXECUTABLE} PRIVATE -Wno-global-constructors) ...@@ -49,20 +50,11 @@ target_compile_options(${PROFILER_EXECUTABLE} PRIVATE -Wno-global-constructors)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE utility) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE utility)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_streamk_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bilinear_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_multiply_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_multiply_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_add_relu_gemm_add_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_fwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_fwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_fwd_instance)
...@@ -79,13 +71,24 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instan ...@@ -79,13 +71,24 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instan
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_bilinear_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_bilinear_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool_fwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool3d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
if(DL_KERNELS) if(DL_KERNELS)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
endif() endif()
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance) if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bilinear_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_streamk_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_add_relu_gemm_add_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
endif()
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler) rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
profiler/src/profile_avg_pool2d_fwd.cpp deleted 100644 → 0
View file @ 54df59bf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <vector>
#include <unordered_map>
#include "profiler/data_type_enum.hpp"
#include "profiler/profile_pool2d_fwd_impl.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
struct avgPoolFwdArgParser
{
std::unordered_map<std::string, std::vector<int>> long_opts = {
{"length", {}}, {"wsize", {}}, {"wstride", {}}, {"pad1", {}}, {"pad2", {}}};
bool parse_opt(int argc, char* argv[], const std::string& key, int i)
{
if(std::string("--") + key == argv[i])
{
int pos = i;
while(++i < argc && argv[i][0] != '-') {}
int end = i;
for(int j = pos + 1; j < end; j++)
{
long_opts[key].push_back(std::stoi(argv[j]));
}
return true;
}
return false;
}
void operator()(int argc, char* argv[])
{
for(auto& kv : long_opts)
{
for(int i = 1; i < argc; i++)
{
if(parse_opt(argc, argv, kv.first, i))
break;
}
}
}
};
void print_help_avg_pool2d_fwd()
{
std::cout << "arg1: data type (0: fp16; 1: fp32)\n"
<< "arg2: verification (0: no; 1: yes)\n"
<< "arg3: initialization (0: no init; 1: integer value; 2: decimal value)\n"
<< "arg4: print tensor value (0: no; 1: yes)\n"
<< "arg5: time kernel (0=no, 1=yes)\n"
<< "--length: input tensor length for NDHW(e.g, --length 2 32 30 30) \n"
<< "--wsize: window size for YX (e.g, --wsize 2 2) \n"
<< "--wstride: window stride for HW (e.g, --wstride 2 2) \n"
<< "--pad1: left side of padding in HW (e.g, --pad1 1 1) \n"
<< "--pad2: right side of padding in HW (e.g, --pad2 1 1) \n"
<< "eg: ckProfiler avg_pool2d_fwd 0 1 2 0 1 0 --length 2 32 30 30 --wsize 2 2 "
"--wstride 2 2 --pad1 1 1 --pad2 1 1"
<< std::endl;
}
int profile_avg_pool2d_fwd(int argc, char* argv[])
{
ck::DataTypeEnum data_type = ck::DataTypeEnum::Half;
bool do_verification = true;
int init_method = 0;
bool do_log = false;
bool time_kernel = true;
std::vector<index_t> in_length = {2, 32, 30, 30};
std::vector<index_t> wsize = {2, 2};
std::vector<index_t> wstride = {2, 2};
std::vector<index_t> pad1 = {1, 1};
std::vector<index_t> pad2 = {1, 1};
if(argc != 2 && argc != 25)
{
print_help_avg_pool2d_fwd();
return 0;
}
else if(argc == 25)
{
data_type = static_cast<ck::DataTypeEnum>(std::stoi(argv[2]));
do_verification = std::stoi(argv[3]);
init_method = std::stoi(argv[4]);
do_log = std::stoi(argv[5]);
time_kernel = std::stoi(argv[6]);
// parse the long options
avgPoolFwdArgParser arg_parser;
arg_parser(argc, argv);
in_length = arg_parser.long_opts["length"];
wsize = arg_parser.long_opts["wsize"];
wstride = arg_parser.long_opts["wstride"];
pad1 = arg_parser.long_opts["pad1"];
pad2 = arg_parser.long_opts["pad2"];
}
using F16 = ck::half_t;
using F32 = float;
using I32 = int32_t;
constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
if(data_type == ck::DataTypeEnum::Half)
{
ck::profiler::profile_pool2d_fwd_impl<F16, F16, F32, I32, ReduceOpId, false, false>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::Float)
{
ck::profiler::profile_pool2d_fwd_impl<F32, F32, F32, I32, ReduceOpId, false, false>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
pad1,
pad2);
}
else
{
throw std::runtime_error("not implemented yet");
}
return 0;
}
REGISTER_PROFILER_OPERATION("avg_pool2d_fwd", "avg_pool2d fwd", profile_avg_pool2d_fwd);
profiler/src/profile_batched_gemm_multi_d.cpp
View file @ b93575ca
...@@ -71,7 +71,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[]) ...@@ -71,7 +71,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
const int BatchCount = std::stoi(argv[17]); const int BatchCount = std::stoi(argv[17]);
using F16 = ck::half_t; using F16 = ck::half_t;
#ifdef __int8__ #ifdef CK_ENABLE_INT8
using INT8 = int8_t; using INT8 = int8_t;
#endif #endif
...@@ -165,7 +165,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[]) ...@@ -165,7 +165,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
{ {
return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{}); return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{});
} }
#ifdef __int8__ #ifdef CK_ENABLE_INT8
else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(INT8{}, INT8{}, INT8{}, Row{}, Row{}, Row{}); return profile(INT8{}, INT8{}, INT8{}, Row{}, Row{}, Row{});
......
profiler/src/profile_conv_bwd_data.cpp
View file @ b93575ca
...@@ -77,7 +77,7 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -77,7 +77,7 @@ int profile_conv_bwd_data(int argc, char* argv[])
using F32 = float; using F32 = float;
using F16 = ck::half_t; using F16 = ck::half_t;
using BF16 = ck::bhalf_t; using BF16 = ck::bhalf_t;
#ifdef __int8__ #ifdef CK_ENABLE_INT8
using INT8 = int8_t; using INT8 = int8_t;
#endif #endif
...@@ -140,7 +140,7 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -140,7 +140,7 @@ int profile_conv_bwd_data(int argc, char* argv[])
{ {
return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{}); return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{});
} }
#ifdef __int8__ #ifdef CK_ENABLE_INT8
else if(data_type == ConvDataType::INT8_INT8_INT8) else if(data_type == ConvDataType::INT8_INT8_INT8)
{ {
return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{}); return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{});
...@@ -161,7 +161,7 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -161,7 +161,7 @@ int profile_conv_bwd_data(int argc, char* argv[])
{ {
return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{}); return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{});
} }
#ifdef __int8__ #ifdef CK_ENABLE_INT8
else if(data_type == ConvDataType::INT8_INT8_INT8) else if(data_type == ConvDataType::INT8_INT8_INT8)
{ {
return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{}); return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{});
...@@ -182,7 +182,7 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -182,7 +182,7 @@ int profile_conv_bwd_data(int argc, char* argv[])
{ {
return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{}); return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{});
} }
#ifdef __int8__ #ifdef CK_ENABLE_INT8
else if(data_type == ConvDataType::INT8_INT8_INT8) else if(data_type == ConvDataType::INT8_INT8_INT8)
{ {
return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{}); return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{});
......
profiler/src/profile_gemm.cpp
View file @ b93575ca
...@@ -69,10 +69,10 @@ int profile_gemm(int argc, char* argv[]) ...@@ -69,10 +69,10 @@ int profile_gemm(int argc, char* argv[])
using F32 = float; using F32 = float;
using F16 = ck::half_t; using F16 = ck::half_t;
#ifdef __bf16__ #ifdef CK_ENABLE_BF16
using BF16 = ck::bhalf_t; using BF16 = ck::bhalf_t;
#endif #endif
#ifdef __int8__ #ifdef CK_ENABLE_INT8
using INT8 = int8_t; using INT8 = int8_t;
using INT32 = int32_t; using INT32 = int32_t;
#endif #endif
...@@ -121,7 +121,10 @@ int profile_gemm(int argc, char* argv[]) ...@@ -121,7 +121,10 @@ int profile_gemm(int argc, char* argv[])
return pass ? 0 : 1; return pass ? 0 : 1;
}; };
if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN) if(false)
;
#ifdef CK_ENABLE_FP32
else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(Row{}, Row{}, Row{}, F32{}, F32{}, F32{}, F32{}); return profile(Row{}, Row{}, Row{}, F32{}, F32{}, F32{}, F32{});
} }
...@@ -137,6 +140,8 @@ int profile_gemm(int argc, char* argv[]) ...@@ -137,6 +140,8 @@ int profile_gemm(int argc, char* argv[])
{ {
return profile(Col{}, Col{}, Row{}, F32{}, F32{}, F32{}, F32{}); return profile(Col{}, Col{}, Row{}, F32{}, F32{}, F32{}, F32{});
} }
#endif
#ifdef CK_ENABLE_FP16
else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(Row{}, Row{}, Row{}, F16{}, F16{}, F32{}, F16{}); return profile(Row{}, Row{}, Row{}, F16{}, F16{}, F32{}, F16{});
...@@ -153,7 +158,8 @@ int profile_gemm(int argc, char* argv[]) ...@@ -153,7 +158,8 @@ int profile_gemm(int argc, char* argv[])
{ {
return profile(Col{}, Col{}, Row{}, F16{}, F16{}, F32{}, F16{}); return profile(Col{}, Col{}, Row{}, F16{}, F16{}, F32{}, F16{});
} }
#ifdef __bf16__ #endif
#ifdef CK_ENABLE_BF16
else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(Row{}, Row{}, Row{}, BF16{}, BF16{}, F32{}, BF16{}); return profile(Row{}, Row{}, Row{}, BF16{}, BF16{}, F32{}, BF16{});
...@@ -171,7 +177,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -171,7 +177,7 @@ int profile_gemm(int argc, char* argv[])
return profile(Col{}, Col{}, Row{}, BF16{}, BF16{}, F32{}, BF16{}); return profile(Col{}, Col{}, Row{}, BF16{}, BF16{}, F32{}, BF16{});
} }
#endif #endif
#ifdef __int8__ #ifdef CK_ENABLE_INT8
else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(Row{}, Row{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{}); return profile(Row{}, Row{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{});
......
profiler/src/profile_gemm_splitk.cpp
View file @ b93575ca
...@@ -23,6 +23,8 @@ enum struct GemmDataType ...@@ -23,6 +23,8 @@ enum struct GemmDataType
F16_F16_F16, // 1 F16_F16_F16, // 1
BF16_BF16_BF16, // 2 BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3 INT8_INT8_INT8, // 3
F8_F16_F16, // 4
F16_F8_F16, // 5
}; };
#define OP_NAME "gemm_splitk" #define OP_NAME "gemm_splitk"
...@@ -33,7 +35,7 @@ int profile_gemm_splitk(int argc, char* argv[]) ...@@ -33,7 +35,7 @@ int profile_gemm_splitk(int argc, char* argv[])
if(argc != 15) if(argc != 15)
{ {
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"); printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"); printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\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(" 1: A[m, k] * B[n, k] = C[m, n];\n");
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n"); printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
...@@ -65,6 +67,7 @@ int profile_gemm_splitk(int argc, char* argv[]) ...@@ -65,6 +67,7 @@ int profile_gemm_splitk(int argc, char* argv[])
using F32 = float; using F32 = float;
using F16 = ck::half_t; using F16 = ck::half_t;
using F8 = ck::f8_t;
using Row = ck::tensor_layout::gemm::RowMajor; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor; using Col = ck::tensor_layout::gemm::ColumnMajor;
...@@ -143,6 +146,38 @@ int profile_gemm_splitk(int argc, char* argv[]) ...@@ -143,6 +146,38 @@ int profile_gemm_splitk(int argc, char* argv[])
{ {
return profile(F16{}, F16{}, F32{}, F16{}, Col{}, Col{}, Row{}); return profile(F16{}, F16{}, F32{}, F16{}, Col{}, Col{}, Row{});
} }
else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
{
return profile(F8{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(F8{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
{
return profile(F8{}, F16{}, F32{}, F16{}, Col{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
{
return profile(F8{}, F16{}, F32{}, F16{}, Col{}, Col{}, Row{});
}
else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_KN_MN)
{
return profile(F16{}, F8{}, F32{}, F16{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(F16{}, F8{}, F32{}, F16{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::KM_KN_MN)
{
return profile(F16{}, F8{}, F32{}, F16{}, Col{}, Row{}, Row{});
}
else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::KM_NK_MN)
{
return profile(F16{}, F8{}, F32{}, F16{}, Col{}, Col{}, Row{});
}
else else
{ {
std::cout << "this data_type & layout is not implemented" << std::endl; std::cout << "this data_type & layout is not implemented" << std::endl;
......
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