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Unverified Commit c2cf0733 authored by Po Yen Chen's avatar Po Yen Chen Committed by GitHub
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Merge branch 'develop' into codegen-enable-hiprtc

parents 7643c909 c3a4800c
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Showing with 462 additions and 64 deletions
CMakeLists.txt
View file @ c2cf0733
...@@ -177,18 +177,14 @@ rocm_check_target_ids(SUPPORTED_GPU_TARGETS ...@@ -177,18 +177,14 @@ rocm_check_target_ids(SUPPORTED_GPU_TARGETS
message("Building CK for the following targets: ${SUPPORTED_GPU_TARGETS}") message("Building CK for the following targets: ${SUPPORTED_GPU_TARGETS}")
if (GPU_TARGETS) if (SUPPORTED_GPU_TARGETS MATCHES "gfx9")
if (GPU_TARGETS MATCHES "gfx9") message("Enabling XDL instances")
add_definitions(-DCK_USE_XDL) add_definitions(-DCK_USE_XDL)
set(CK_USE_XDL "ON")
endif()
if (GPU_TARGETS MATCHES "gfx11" OR GPU_TARGETS MATCHES "gfx12")
add_definitions(-DCK_USE_WMMA)
set(CK_USE_WMMA "ON")
endif()
else()
add_definitions(-DCK_USE_WMMA -DCK_USE_XDL)
set(CK_USE_XDL "ON") set(CK_USE_XDL "ON")
endif()
if (SUPPORTED_GPU_TARGETS MATCHES "gfx11" OR SUPPORTED_GPU_TARGETS MATCHES "gfx12")
message("Enabling WMMA instances")
add_definitions(-DCK_USE_WMMA)
set(CK_USE_WMMA "ON") set(CK_USE_WMMA "ON")
endif() endif()
...@@ -202,6 +198,13 @@ if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 500723302) ...@@ -202,6 +198,13 @@ if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 500723302)
add_compile_options(-fno-offload-uniform-block) add_compile_options(-fno-offload-uniform-block)
endif() endif()
endif() endif()
if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 500500000)
check_cxx_compiler_flag("-mllvm --lsr-drop-solution=1" HAS_LSR_DROP_SOLUTION)
if(HAS_LSR_DROP_SOLUTION)
message("Adding the lsr-drop-solution=1 compiler flag")
add_compile_options("SHELL: -mllvm --lsr-drop-solution=1")
endif()
endif()
if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 600140090) if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 600140090)
check_cxx_compiler_flag("-mllvm -enable-post-misched=0" HAS_ENABLE_POST_MISCHED) check_cxx_compiler_flag("-mllvm -enable-post-misched=0" HAS_ENABLE_POST_MISCHED)
if(HAS_ENABLE_POST_MISCHED) if(HAS_ENABLE_POST_MISCHED)
...@@ -571,7 +574,7 @@ rocm_package_setup_component(profiler ...@@ -571,7 +574,7 @@ rocm_package_setup_component(profiler
) )
add_subdirectory(profiler) add_subdirectory(profiler)
if(CK_USE_CODEGEN AND (GPU_TARGETS MATCHES "gfx9" OR GPU_ARCHS)) if(CK_USE_CODEGEN AND (SUPPORTED_GPU_TARGETS MATCHES "gfx9" OR GPU_ARCHS))
add_subdirectory(codegen) add_subdirectory(codegen)
endif() endif()
......
README.md
View file @ c2cf0733
# Composable Kernel # Composable Kernel
> [!NOTE]
> The published documentation is available at [Composable Kernel](https://rocm.docs.amd.com/projects/composable_kernel/en/latest/) in an organized, easy-to-read format, with search and a table of contents. The documentation source files reside in the `docs` folder of this repository. As with all ROCm projects, the documentation is open source. For more information on contributing to the documentation, see [Contribute to ROCm documentation](https://rocm.docs.amd.com/en/latest/contribute/contributing.html).
The Composable Kernel (CK) library provides a programming model for writing performance-critical The Composable Kernel (CK) library provides a programming model for writing performance-critical
kernels for machine learning workloads across multiple architectures (GPUs, CPUs, etc.). The CK library kernels for machine learning workloads across multiple architectures (GPUs, CPUs, etc.). The CK library
uses general purpose kernel languages, such as HIP C++. uses general purpose kernel languages, such as HIP C++.
......
docs/sphinx/requirements.in
View file @ c2cf0733
rocm-docs-core==1.8.2 rocm-docs-core==1.8.3
sphinxcontrib-bibtex==2.6.3 sphinxcontrib-bibtex==2.6.3
docs/sphinx/requirements.txt
View file @ c2cf0733
...@@ -103,7 +103,7 @@ requests==2.32.3 ...@@ -103,7 +103,7 @@ requests==2.32.3
# via # via
# pygithub # pygithub
# sphinx # sphinx
rocm-docs-core==1.8.2 rocm-docs-core==1.8.3
# via -r requirements.in # via -r requirements.in
six==1.16.0 six==1.16.0
# via pybtex # via pybtex
......
example/01_gemm/common.hpp
View file @ c2cf0733
...@@ -29,9 +29,9 @@ struct ProblemSize final ...@@ -29,9 +29,9 @@ struct ProblemSize final
ck::index_t N = 4096; ck::index_t N = 4096;
ck::index_t K = 4096; ck::index_t K = 4096;
ck::index_t StrideA = 0; ck::index_t StrideA = -1;
ck::index_t StrideB = 0; ck::index_t StrideB = -1;
ck::index_t StrideC = 0; ck::index_t StrideC = -1;
}; };
struct ProblemSizeStreamK final struct ProblemSizeStreamK final
...@@ -40,9 +40,9 @@ struct ProblemSizeStreamK final ...@@ -40,9 +40,9 @@ struct ProblemSizeStreamK final
ck::index_t N = 4096; ck::index_t N = 4096;
ck::index_t K = 4096; ck::index_t K = 4096;
ck::index_t StrideA = 0; ck::index_t StrideA = -1;
ck::index_t StrideB = 0; ck::index_t StrideB = -1;
ck::index_t StrideC = 0; ck::index_t StrideC = -1;
ck::index_t NumSKBlocks = -1; ck::index_t NumSKBlocks = -1;
}; };
...@@ -52,9 +52,9 @@ struct ProblemSizeStreamK_universal final ...@@ -52,9 +52,9 @@ struct ProblemSizeStreamK_universal final
ck::index_t N = 4096; ck::index_t N = 4096;
ck::index_t K = 4096; ck::index_t K = 4096;
ck::index_t StrideA = 0; ck::index_t StrideA = -1;
ck::index_t StrideB = 0; ck::index_t StrideB = -1;
ck::index_t StrideC = 0; ck::index_t StrideC = -1;
ck::index_t Grid_size = -1; // defaults to max occupancy ck::index_t Grid_size = -1; // defaults to max occupancy
ck::index_t Streamk_sel = 1; // defaults to 1-tile SK ck::index_t Streamk_sel = 1; // defaults to 1-tile SK
...@@ -66,18 +66,19 @@ struct ProblemSizeSplitK final ...@@ -66,18 +66,19 @@ struct ProblemSizeSplitK final
ck::index_t N = 4096; ck::index_t N = 4096;
ck::index_t K = 4096; ck::index_t K = 4096;
ck::index_t StrideA = 0; ck::index_t StrideA = -1;
ck::index_t StrideB = 0; ck::index_t StrideB = -1;
ck::index_t StrideC = 0; ck::index_t StrideC = -1;
ck::index_t KBatch = 1; ck::index_t KBatch = 1;
}; };
struct ExecutionConfig final struct ExecutionConfig final
{ {
bool do_verification = true; // 0 - no verification, 1 - CPU, 2 - GPU, 3 - CPU + GPU
int init_method = 2; int do_verification = 3;
bool time_kernel = false; int init_method = 2;
bool time_kernel = false;
}; };
template <ck::index_t... Is> template <ck::index_t... Is>
...@@ -126,7 +127,7 @@ bool parse_cmd_args<ProblemSize>(int argc, ...@@ -126,7 +127,7 @@ bool parse_cmd_args<ProblemSize>(int argc,
} }
else else
{ {
std::cerr << "arg1: verification (0=no, 1=CPU and GPU)" << std::endl std::cerr << "arg1: verification (0=no, 1=CPU, 2=GPU, 3=CPU and GPU)" << std::endl
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
<< std::endl << std::endl
<< "arg3: time kernel (0=no, 1=yes)" << std::endl << "arg3: time kernel (0=no, 1=yes)" << std::endl
...@@ -176,7 +177,7 @@ bool parse_cmd_args<ProblemSizeStreamK_universal>(int argc, ...@@ -176,7 +177,7 @@ bool parse_cmd_args<ProblemSizeStreamK_universal>(int argc,
else else
{ {
std::cerr std::cerr
<< "arg1: verification (0=no, 1=CPU and GPU)" << std::endl << "arg1: verification (0=no, 1=CPU, 2=GPU, 3=CPU and GPU)" << std::endl
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << std::endl << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << std::endl
<< "arg3: time kernel (0=no, 1=yes)" << std::endl << "arg3: time kernel (0=no, 1=yes)" << std::endl
<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC" << std::endl << "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC" << std::endl
...@@ -225,7 +226,7 @@ bool parse_cmd_args<ProblemSizeStreamK>(int argc, ...@@ -225,7 +226,7 @@ bool parse_cmd_args<ProblemSizeStreamK>(int argc,
} }
else else
{ {
std::cerr << "arg1: verification (0=no, 1=CPU and GPU)" << std::endl std::cerr << "arg1: verification (0=no, 1=CPU, 2=GPU, 3=CPU and GPU)" << std::endl
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
<< std::endl << std::endl
<< "arg3: time kernel (0=no, 1=yes)" << std::endl << "arg3: time kernel (0=no, 1=yes)" << std::endl
...@@ -275,7 +276,7 @@ bool parse_cmd_args<ProblemSizeSplitK>(int argc, ...@@ -275,7 +276,7 @@ bool parse_cmd_args<ProblemSizeSplitK>(int argc,
} }
else else
{ {
std::cerr << "arg1: verification (0=no, 1=CPU and GPU)" << std::endl std::cerr << "arg1: verification (0=no, 1=CPU, 2=GPU, 3=CPU and GPU)" << std::endl
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
<< std::endl << std::endl
<< "arg3: time kernel (0=no, 1=yes)" << std::endl << "arg3: time kernel (0=no, 1=yes)" << std::endl
......
example/01_gemm/run_gemm_example.inc
View file @ c2cf0733
...@@ -116,21 +116,21 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -116,21 +116,21 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
}; };
auto f_get_default_stride = auto f_get_default_stride =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == 0) if(stride == -1)
{ {
// give a chance if stride is zero, return a default packed stride // give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>) if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{ {
return col; return static_cast<std::size_t>(col);
} }
else else
{ {
return row; return static_cast<std::size_t>(row);
} }
} }
else else
return stride; return static_cast<std::size_t>(stride);
}; };
StrideA = f_get_default_stride(M, K, StrideA, ALayout{}); StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
...@@ -330,7 +330,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -330,7 +330,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
bool pass = true; bool pass = true;
if(config.do_verification) if((config.do_verification == 1) || (config.do_verification == 3))
{ {
// CPU verification // CPU verification
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
...@@ -353,13 +353,16 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -353,13 +353,16 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
#else #else
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data()); c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= !ck::utils::check_err(c_m_n_device_result, pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result, c_m_n_host_result,
"Error: Incorrect results!", "Error: Incorrect results!",
get_rtol<CDataType>(), get_rtol<CDataType>(),
get_atol<CDataType>()); get_atol<CDataType>());
#endif #endif
}
if((config.do_verification == 2) || (config.do_verification == 3))
{
// GPU verification // GPU verification
auto ref_gemm_gpu = ReferenceGemmInstanceGPU{}; auto ref_gemm_gpu = ReferenceGemmInstanceGPU{};
auto ref_invoker_gpu = ref_gemm_gpu.MakeInvoker(); auto ref_invoker_gpu = ref_gemm_gpu.MakeInvoker();
...@@ -381,14 +384,14 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -381,14 +384,14 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
c_m_n_device_ref_buf.FromDevice(c_m_n_device_ref_result.mData.data()); c_m_n_device_ref_buf.FromDevice(c_m_n_device_ref_result.mData.data());
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data()); c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= !ck::utils::check_err(c_m_n_device_result, pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_device_ref_result, c_m_n_device_ref_result,
"Error: Incorrect results!", "Error: Incorrect results!",
get_rtol<CDataType>(), get_rtol<CDataType>(),
get_atol<CDataType>()); get_atol<CDataType>());
} }
return !pass; return pass == true;
} }
bool run_gemm_example(int argc, char* argv[]) bool run_gemm_example(int argc, char* argv[])
......
example/01_gemm/run_gemm_example_streamk_v2.inc
View file @ c2cf0733
...@@ -117,9 +117,9 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -117,9 +117,9 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
auto f_get_default_stride = auto f_get_default_stride =
[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) { [](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == 0) if(stride == -1)
{ {
// give a chance if stride is 0, return a default packed stride // give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>) if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{ {
return static_cast<std::size_t>(col); return static_cast<std::size_t>(col);
...@@ -241,7 +241,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -241,7 +241,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
} }
bool pass = true; bool pass = true;
if(config.do_verification) if((config.do_verification == 1) || (config.do_verification == 3))
{ {
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker(); auto ref_invoker = ref_gemm.MakeInvoker();
......
example/01_gemm/run_gemm_example_v2.inc
View file @ c2cf0733
...@@ -115,21 +115,21 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -115,21 +115,21 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
}; };
auto f_get_default_stride = auto f_get_default_stride =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == 0) if(stride == -1)
{ {
// give a chance if stride is zero, return a default packed stride // give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>) if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{ {
return col; return static_cast<std::size_t>(col);
} }
else else
{ {
return row; return static_cast<std::size_t>(row);
} }
} }
else else
return stride; return static_cast<std::size_t>(stride);
}; };
StrideA = f_get_default_stride(M, K, StrideA, ALayout{}); StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
...@@ -228,7 +228,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) ...@@ -228,7 +228,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
} }
bool pass = true; bool pass = true;
if(config.do_verification) if((config.do_verification == 1) || (config.do_verification == 3))
{ {
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker(); auto ref_invoker = ref_gemm.MakeInvoker();
......
example/37_batched_gemm_add_add_relu_gemm_add/batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp
View file @ c2cf0733
// SPDX-License-Identifier: MIT // 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.
/* /*
Computes C_m_o = Relu(A0[m, k] * B0[n, k] + D00[m, n] + D01[mn]) * B1[n, o] + D1[m, o] Computes C_m_o = Relu(A0[m, k] * B0[n, k] + D00[m, n] + D01[mn]) * B1[n, o] + D1[m, o]
...@@ -60,14 +60,14 @@ struct AddAddRelu ...@@ -60,14 +60,14 @@ struct AddAddRelu
{ {
const ck::half_t x = c + d0 + d1; const ck::half_t x = c + d0 + d1;
ck::tensor_operation::element_wise::Relu{}.template operator()<ck::half_t>(e, x); ck::tensor_operation::element_wise::Relu{}.operator()(e, x);
} }
__host__ __device__ void __host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{ {
const float x = c + (d0 + d1); const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::Relu{}.template operator()<float>(e, x); ck::tensor_operation::element_wise::Relu{}.operator()(e, x);
} }
}; };
......
example/62_convnd_activ/CMakeLists.txt
View file @ c2cf0733
...@@ -6,6 +6,7 @@ add_subdirectory(convscale_add) ...@@ -6,6 +6,7 @@ add_subdirectory(convscale_add)
add_subdirectory(convscale_reduce) add_subdirectory(convscale_reduce)
add_subdirectory(multi_AB) add_subdirectory(multi_AB)
add_subdirectory(unary) add_subdirectory(unary)
add_subdirectory(dynamic_unary)
add_custom_target(example_convnd_activ_xdl) add_custom_target(example_convnd_activ_xdl)
# ScaleAdd ScaleAdd Relu # ScaleAdd ScaleAdd Relu
......
example/62_convnd_activ/dynamic_unary/CMakeLists.txt 0 → 100644
View file @ c2cf0733
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_custom_target(example_convnd_activ_dynamic_unary_xdl)
# Sigmoid
add_example_executable(example_convnd_fwd_xdl_dynamic_sigmoid_fp16 convnd_fwd_xdl_dynamic_sigmoid_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_sigmoid_fp16)
# Tanh
add_example_executable(example_convnd_fwd_xdl_dynamic_tanh_fp16 convnd_fwd_xdl_dynamic_tanh_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_tanh_fp16)
# Relu
add_example_executable(example_convnd_fwd_xdl_dynamic_relu_fp16 convnd_fwd_xdl_dynamic_relu_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_relu_fp16)
# SoftRelu
add_example_executable(example_convnd_fwd_xdl_dynamic_softrelu_fp16 convnd_fwd_xdl_dynamic_softrelu_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_softrelu_fp16)
# Abs
add_example_executable(example_convnd_fwd_xdl_dynamic_abs_fp16 convnd_fwd_xdl_dynamic_abs_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_abs_fp16)
# Pow
add_example_executable(example_convnd_fwd_xdl_dynamic_pow_fp16 convnd_fwd_xdl_dynamic_pow_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_pow_fp16)
# Clipped Relu
add_example_executable(example_convnd_fwd_xdl_dynamic_clippedrelu_fp16 convnd_fwd_xdl_dynamic_clippedrelu_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_clippedrelu_fp16)
# Leaky Relu
add_example_executable(example_convnd_fwd_xdl_dynamic_leakyrelu_fp16 convnd_fwd_xdl_dynamic_leakyrelu_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_leakyrelu_fp16)
# Elu
add_example_executable(example_convnd_fwd_xdl_dynamic_elu_fp16 convnd_fwd_xdl_dynamic_elu_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_elu_fp16)
# Swish
add_example_executable(example_convnd_fwd_xdl_dynamic_swish_fp16 convnd_fwd_xdl_dynamic_swish_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_swish_fp16)
# PassThrough
add_example_executable(example_convnd_fwd_xdl_dynamic_passthrough_fp16 convnd_fwd_xdl_dynamic_passthrough_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_passthrough_fp16)
# Logistic
add_example_executable(example_convnd_fwd_xdl_dynamic_logistic_fp16 convnd_fwd_xdl_dynamic_logistic_fp16.cpp)
add_example_dependencies(example_convnd_activ_dynamic_unary_xdl example_convnd_fwd_xdl_dynamic_logistic_fp16)
set(target 1)
endif()
endforeach()
example/62_convnd_activ/dynamic_unary/convnd_fwd_activ_dynamic_unary_common.hpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <type_traits>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
#include "ck/library/utility/algorithm.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/convolution_parameter.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
constexpr ck::index_t NDimSpatial = 3;
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
using AccDataType = float;
using CShuffleDataType = ck::half_t;
using OutDataType = ck::half_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using InLayout = ck::tensor_layout::convolution::GNDHWC;
using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
using OutLayout = ck::tensor_layout::convolution::GNDHWK;
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using DynamicElementOp = ck::tensor_operation::element_wise::DynamicUnaryOp;
static constexpr auto ConvSpec =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using DeviceGroupedConvNDActivInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
NDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<>,
OutDataType,
InElementOp,
WeiElementOp,
DynamicElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
1, //
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
32, // KPerBlock
8, // AK1
8, // BK1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
1,
1,
S<1, 32, 1, 8>,
8>;
template <ck::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename InElementOp,
typename WeiElementOp,
typename OutElementOp,
typename DeviceConvNDFwdInstance>
bool run_grouped_conv(bool do_verification,
int init_method,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param,
const HostTensorDescriptor& in_g_n_c_wis_desc,
const HostTensorDescriptor& wei_g_k_c_xs_desc,
const HostTensorDescriptor& out_g_n_k_wos_desc,
const InElementOp& in_element_op,
const WeiElementOp& wei_element_op,
const OutElementOp& out_element_op)
{
Tensor<InDataType> in(in_g_n_c_wis_desc);
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
Tensor<OutDataType> out_host(out_g_n_k_wos_desc);
Tensor<OutDataType> out_device(out_g_n_k_wos_desc);
std::cout << "in: " << in.mDesc << std::endl;
std::cout << "wei: " << wei.mDesc << std::endl;
std::cout << "out: " << out_host.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-2, 2});
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-2, 2});
break;
default:
in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-1.0, 1.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.05, 0.05});
}
DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
in_device_buf.ToDevice(in.mData.data());
wei_device_buf.ToDevice(wei.mData.data());
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_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> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
copy(conv_param.input_right_pads_, input_right_pads);
// do Conv
auto conv = DeviceConvNDFwdInstance{};
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
std::array<const void*, 0>{},
out_device_buf.GetDeviceBuffer(),
a_g_n_c_wis_lengths,
a_g_n_c_wis_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{{}},
std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{{}},
e_g_n_k_wos_lengths,
e_g_n_k_wos_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op);
if(!conv.IsSupportedArgument(argument))
{
throw std::runtime_error("The device op with the specified compilation parameters does "
"not support this convolution problem.");
}
float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< conv.GetTypeString() << std::endl;
if(do_verification)
{
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in,
wei,
out_host,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
in_element_op,
wei_element_op,
out_element_op);
ref_invoker.Run(ref_argument);
out_device_buf.FromDevice(out_device.mData.data());
return ck::utils::check_err(out_device, out_host, "Error: incorrect results!");
}
return true;
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_abs_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::UnaryAbs out_element_op;
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_clippedrelu_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::ClippedRelu out_element_op(0.f, 1.f);
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_elu_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::Elu out_element_op(2.f);
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_leakyrelu_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::LeakyRelu out_element_op(0.f);
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_logistic_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::Logistic out_element_op(1.0f);
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_passthrough_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::PassThrough out_element_op;
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_pow_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::Power out_element_op(4.f, 1.f, 2.f);
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_relu_fp16.cpp 0 → 100644
View file @ c2cf0733
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::Relu out_element_op;
return !run_convnd_example(argc, argv, out_element_op);
}
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