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Commit 3dc5db72 authored by Jun Liu's avatar Jun Liu
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Merge branch 'amd-develop' into amd-master

parents b924e330 e547c141
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example/01_gemm/gemm_xdl_fp64.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
......@@ -41,6 +41,17 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl
BElementOp,
CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/gemm_xdl_fp8.cpp
View file @ 3dc5db72
......@@ -37,6 +37,20 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceComputeType = float;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp,
ReferenceComputeType,
ReferenceComputeType>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/gemm_xdl_fp8_bf8.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
......@@ -44,6 +44,17 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataTyp
ComputeTypeA,
ComputeTypeB>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/gemm_xdl_int8.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
......@@ -33,6 +33,17 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/gemm_xdl_lds_direct_load_fp16.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
......@@ -53,6 +53,17 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/gemm_xdl_lds_direct_load_fp32.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
......@@ -52,6 +52,17 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/gemm_xdl_streamk.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
......@@ -44,6 +44,17 @@ using DeviceGemmInstance = DeviceGemmStreamK;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_streamk_example(argc, argv); }
example/01_gemm/gemm_xdl_wavelet_fp16.cpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
......@@ -37,6 +37,17 @@ using DeviceGemmInstance = DeviceGemmInstance;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstanceGPU = ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/01_gemm/run_gemm_example.inc
View file @ 3dc5db72
......@@ -173,6 +173,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_ref_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
......@@ -193,6 +194,8 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_ref_buf(sizeof(CDataType) *
c_m_n_device_ref_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
......@@ -325,14 +328,18 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
bool pass = true;
if(config.do_verification)
{
// CPU verification
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
std::cout << "Running verification on CPU." << std::endl;
ref_invoker.Run(ref_argument);
#ifdef BUILD_INT4_EXAMPLE
......@@ -346,15 +353,42 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
#else
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result,
pass &= !ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
#endif
// GPU verification
auto ref_gemm_gpu = ReferenceGemmInstanceGPU{};
auto ref_invoker_gpu = ref_gemm_gpu.MakeInvoker();
auto ref_argument_gpu = ref_gemm_gpu.MakeArgument(
static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_ref_buf.GetDeviceBuffer()),
M,
N,
K,
a_element_op,
b_element_op,
c_element_op);
std::cout << "Running verification on GPU." << std::endl;
ref_invoker_gpu.Run(ref_argument_gpu, StreamConfig{});
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());
pass &= !ck::utils::check_err(c_m_n_device_result,
c_m_n_device_ref_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
}
return true;
return !pass;
}
bool run_gemm_example(int argc, char* argv[])
......
example/01_gemm/run_gemm_example_streamk_v2.inc
View file @ 3dc5db72
......@@ -117,9 +117,9 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
auto f_get_default_stride =
[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == -1)
if(stride == 0)
{
// give a chance if stride is -1, return a default packed stride
// give a chance if stride is 0, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return static_cast<std::size_t>(col);
......
example/44_elementwise_permute/CMakeLists.txt
View file @ 3dc5db72
......@@ -5,3 +5,4 @@ add_example_executable(example_elementwise_permute_4D_fp32_col elementwise_permu
add_example_executable(example_elementwise_permute_4D_fp16_col elementwise_permute_4D_fp16_col.cpp)
add_example_executable(example_elementwise_binary_4D_fp16 elementwise_binary_4D_fp16.cpp)
add_example_executable(example_elementwise_trinary_4D_fp16 elementwise_trinary_4D_fp16.cpp)
add_example_executable(elementwise_scale_permute_amax_2D_fp16_fp8 elementwise_scale_permute_amax_2D_fp16_fp8.cpp)
example/44_elementwise_permute/elementwise_scale_permute_amax_2D_fp16_fp8.cpp 0 → 100644
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_elementwise.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/utility/reduction_enums.hpp"
using F16 = ck::half_t;
using F32 = float;
using F8 = ck::f8_t;
using InputDataType = F16;
using ScaleDataType = F32;
using OutputDataType = F8;
static constexpr ck::index_t NumDim = 2;
constexpr ck::ReduceTensorOp ReduceOpId = ck::ReduceTensorOp::MAX;
constexpr bool PropagateNan = true;
constexpr bool OutputIndex = false;
using ReduceOperation = typename ck::reduce_binary_operator<ReduceOpId>::opType;
struct ScalePassThrough
{
ScalePassThrough(const float alpha = 1.f) : alpha_(alpha) {}
__host__ __device__ constexpr void
operator()(OutputDataType& y0, OutputDataType& y1, const InputDataType& x0) const
{
y0 = ck::type_convert<OutputDataType>(ck::type_convert<ScaleDataType>(x0) * alpha_);
y1 = y0;
}
const ScaleDataType alpha_;
};
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using UnaryAbs = ck::tensor_operation::element_wise::UnaryAbs;
using DeviceElementwisePermuteInstance = ck::tensor_operation::device::DeviceElementwiseImpl<
ck::Tuple<InputDataType>, // InDataTypeTuple
ck::Tuple<OutputDataType, OutputDataType>, // OutDataTypeTuple
ScalePassThrough, // Elementwise
NumDim, // NumDim
256, // BlockSize
128, // M0PerBlock
128, // M1PerBlock
8, // M0PerThread
8, // M1PerThread
ck::Sequence<1, 0>, // ThreadClusterArrangeOrder
ck::Sequence<8>, // InScalarPerVectorSeq
ck::Sequence<8, 1>>; // OutScalarPerVectorSeq
using DeviceReduceInstance =
ck::tensor_operation::device::DeviceReduceMultiBlock<OutputDataType,
OutputDataType,
OutputDataType,
NumDim,
NumDim,
ReduceOperation,
UnaryAbs,
PassThrough,
ck::InMemoryDataOperationEnum::Set,
PropagateNan,
OutputIndex,
false, // HaveIndexInputIfOutputIndex
1024, // BlockSize
1, // MThreadClusterSize
1024, // KThreadClusterSize
1, // MThreadSliceSize
16, // KThreadSliceSize
1, // InSrcVectorDim
16, // InSrceVectorSize
1>; // OutDstVectorSize
void reference_scale_permute_amax(Tensor<InputDataType>& input,
Tensor<OutputDataType>& host_output_scaled_casted_transposed,
Tensor<OutputDataType>& host_output_scaled_casted,
Tensor<OutputDataType>& host_output_amax,
const float scale)
{
ScalePassThrough out_element_op(scale);
const ck::index_t M = input.GetLengths()[0];
const ck::index_t K = input.GetLengths()[1];
for(ck::index_t m = 0; m < M; m++)
{
for(ck::index_t k = 0; k < K; k++)
{
OutputDataType y0, y1;
out_element_op(y0, y1, input(m, k));
host_output_scaled_casted(m, k) = y0;
host_output_scaled_casted_transposed(m, k) = y1;
const OutputDataType y_fabs =
ck::type_convert<OutputDataType>(ck::math::abs(ck::type_convert<float>(y0)));
host_output_amax(0) = ck::math::max(y_fabs, host_output_amax(0));
}
}
}
int main(int argc, char* argv[])
{
bool do_verification = true;
bool time_kernel = true;
const float scale = 2.f;
ck::index_t M = 1024;
ck::index_t K = 1024;
if(argc == 3)
{
M = std::stoi(argv[1]);
K = std::stoi(argv[2]);
}
std::array<ck::index_t, 2> dims = {M, K};
std::array<ck::index_t, 2> in_strides = {K, 1};
std::array<ck::index_t, 2> out_strides = {1, M};
Tensor<InputDataType> input(dims, in_strides);
Tensor<OutputDataType> output_scaled_casted_transposed(dims, out_strides);
Tensor<OutputDataType> output_scaled_casted(dims, in_strides);
Tensor<OutputDataType> output_amax({1});
input.GenerateTensorValue(GeneratorTensor_3<InputDataType>{0.0, 1.0});
DeviceMem input_dev_buf(sizeof(InputDataType) * input.mDesc.GetElementSpaceSize());
DeviceMem output_scaled_casted_transposed_dev_buf(
sizeof(OutputDataType) * output_scaled_casted_transposed.mDesc.GetElementSpaceSize());
DeviceMem output_scaled_casted_dev_buf(sizeof(OutputDataType) *
output_scaled_casted.mDesc.GetElementSpaceSize());
DeviceMem output_amax_dev_buf(sizeof(OutputDataType) * output_amax.mDesc.GetElementSpaceSize());
input_dev_buf.ToDevice(input.mData.data());
std::array<const void*, 1> inputs = {input_dev_buf.GetDeviceBuffer()};
std::array<void*, 2> outputs = {output_scaled_casted_transposed_dev_buf.GetDeviceBuffer(),
output_scaled_casted_dev_buf.GetDeviceBuffer()};
std::cout << "Input: " << input.mDesc << std::endl;
std::cout << "Scale: " << scale << std::endl;
std::cout << "Output scaled casted transposed: " << output_scaled_casted_transposed.mDesc
<< std::endl;
std::cout << "Output scaled casted: " << output_scaled_casted.mDesc << std::endl;
std::cout << "Output amax: " << output_amax.mDesc << std::endl;
auto launch_transpose_scale = [&]() {
auto transposeScale = DeviceElementwisePermuteInstance{};
auto argument = transposeScale.MakeArgumentPointer(dims,
{in_strides},
{out_strides, in_strides},
inputs,
outputs,
ScalePassThrough{scale});
if(!transposeScale.IsSupportedArgument(argument.get()))
{
throw std::runtime_error(
"The runtime parameters seems not supported by the device instance, exiting!");
};
auto transposeScale_invoker_ptr = transposeScale.MakeInvokerPointer();
return transposeScale_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
};
auto launch_reduce = [&]() {
auto reduce = DeviceReduceInstance{};
auto reduce_argument_ptr =
reduce.MakeArgumentPointer(dims,
in_strides,
{1}, // Output Lengths
{1}, // Output Strides
{0, 1}, // Reduce Dims
static_cast<double>(1.f),
static_cast<double>(0.f),
output_scaled_casted_dev_buf.GetDeviceBuffer(),
nullptr,
output_amax_dev_buf.GetDeviceBuffer(),
nullptr,
UnaryAbs{},
PassThrough{});
if(!reduce.IsSupportedArgument(reduce_argument_ptr.get()))
{
throw std::runtime_error(
"The runtime parameters seems not supported by the device instance, exiting!");
};
auto invoker_ptr = reduce.MakeInvokerPointer();
return invoker_ptr->Run(reduce_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
};
float ave_time = launch_transpose_scale();
ave_time += launch_reduce();
std::cout << "Perf: " << ave_time << " ms" << std::endl;
bool pass = true;
if(do_verification)
{
Tensor<OutputDataType> host_output_scaled_casted_transposed(dims, out_strides);
Tensor<OutputDataType> host_output_scaled_casted(dims, in_strides);
Tensor<OutputDataType> host_output_amax({1});
reference_scale_permute_amax(input,
host_output_scaled_casted_transposed,
host_output_scaled_casted,
host_output_amax,
scale);
output_scaled_casted_transposed_dev_buf.FromDevice(
output_scaled_casted_transposed.mData.data());
output_scaled_casted_dev_buf.FromDevice(output_scaled_casted.mData.data());
output_amax_dev_buf.FromDevice(output_amax.mData.data());
pass &= ck::utils::check_err(output_scaled_casted_transposed.mData,
host_output_scaled_casted_transposed.mData,
"Error: Incorrect results scaled transposed",
1e-3,
1e-3);
pass &= ck::utils::check_err(output_scaled_casted.mData,
host_output_scaled_casted.mData,
"Error: Incorrect results scaled",
1e-3,
1e-3);
pass &= ck::utils::check_err(
output_amax.mData, host_output_amax.mData, "Error: Incorrect results amax", 1e-3, 1e-3);
}
return pass ? 0 : 1;
}
example/66_complex_contraction_bilinear/CMakeLists.txt 0 → 100755
View file @ 3dc5db72
add_example_executable(example_complex_contraction_bilinear_xdl_fp32 complex_contraction_bilinear_xdl_fp32.cpp)
add_example_executable(example_complex_contraction_bilinear_xdl_fp64 complex_contraction_bilinear_xdl_fp64.cpp)
example/66_complex_contraction_bilinear/README.md 0 → 100755
View file @ 3dc5db72
# Instructions for ```example_complex_contraction_bilinear_xdl_fp32```
## Run
```bash
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: time kernel (0=no, 1=yes)
./bin/example_contraction_bilinear_xdl_fp32 1 1 1
```
example/66_complex_contraction_bilinear/common_instances.hpp 0 → 100644
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This diff is collapsed.
example/66_complex_contraction_bilinear/complex_contraction_bilinear_xdl_fp32.cpp 0 → 100755
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "common_instances.hpp"
using ADataType = F32;
using BDataType = F32;
using AccDataType = F32;
using CShuffleDataType = F32;
using DDataType = F32;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F32;
using ComputeDataType = F32;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
static constexpr ck::index_t NumDimK = 2;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CDEElementOp = ck::tensor_operation::element_wise::Bilinear;
using DeviceOpInstanceKKNN = DeviceOpInstanceKK_Generic<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstanceKNNN = DeviceOpInstanceKN_Generic<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstanceMKNN = DeviceOpInstanceMK_Generic<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstanceMNNN = DeviceOpInstanceMN_Generic<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstance = DeviceOpInstanceKKNN;
#include "run_complex_contraction_bilinear_example.inc"
int main(int argc, char* argv[]) { return run_complex_contraction_bilinear_example(argc, argv); }
example/66_complex_contraction_bilinear/complex_contraction_bilinear_xdl_fp64.cpp 0 → 100755
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "common_instances.hpp"
using ADataType = F64;
using BDataType = F64;
using AccDataType = F64;
using CShuffleDataType = F64;
using DDataType = F64;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F64;
using ComputeDataType = F64;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
static constexpr ck::index_t NumDimK = 2;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CDEElementOp = ck::tensor_operation::element_wise::Bilinear;
using DeviceOpInstanceKKNN = DeviceOpInstanceKK_FP64<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstanceKNNN = DeviceOpInstanceKN_FP64<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstanceMKNN = DeviceOpInstanceMK_FP64<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstanceMNNN = DeviceOpInstanceMN_FP64<NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
ComputeDataType,
AElementOp,
BElementOp,
CDEElementOp>;
using DeviceOpInstance = DeviceOpInstanceKKNN;
#include "run_complex_contraction_bilinear_example.inc"
int main(int argc, char* argv[]) { return run_complex_contraction_bilinear_example(argc, argv); }
example/CMakeLists.txt
View file @ 3dc5db72
......@@ -45,11 +45,7 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
endforeach()
endif()
if(INSTANCES_ONLY)
set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
else()
set(EX_TARGETS ${GPU_TARGETS})
endif()
set(EX_TARGETS ${SUPPORTED_GPU_TARGETS})
#Do not build any DL examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME)
......@@ -147,11 +143,8 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
endforeach()
endif()
if(INSTANCES_ONLY)
set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
else()
set(EX_TARGETS ${GPU_TARGETS})
endif()
set(EX_TARGETS ${SUPPORTED_GPU_TARGETS})
#Do not build any DL examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
......
example/ck_tile/01_fmha/README.md
View file @ 3dc5db72
......@@ -6,7 +6,8 @@ This folder contains example for fmha(fused multi-head attention) using ck_tile
```
# in the root of ck_tile
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
make tile_example_fmha_fwd -j
```
This will result in an executable `build/bin/tile_example_fmha_fwd`
......@@ -23,7 +24,7 @@ There are 3 template parameters for this kernel template.
To speed up compile time, we instantiate the kernels into separate file. In this way we can benefit from parallel building from CMake/Make system. This is achieved by `generate.py` script. Besides, you can look into this script to learn how to instantiate a kernel instance step by step, which is described in `FMHA_FWD_KERNEL_BODY` variable.
## executable
`tile_example_fmha_fwd` is the example executable, implemented in `fmha_fwd.cpp`. You can type `./bin/tile_example_fmha_fwd -?` to list all supported args. Below is an example of the output (may subject to change)
`tile_example_fmha_fwd` is the example executable, implemented in `fmha_fwd.cpp`. You can type `./bin/tile_example_fmha_fwd -?` to list all the arguments. Below is an example of the output (may subject to change)
```
args:
-v weather do CPU validation or not (default:1)
......@@ -35,7 +36,7 @@ args:
-s seqlen_q. if group-mode, means the average value of seqlen_q (default:3328)
total_seqlen_q = seqlen_q * batch, and seqlen_q per batch may vary
also with "-s=s0,s1,s2..." comma seperated int to set per batch seqlen(group-mode)
-s_k seqlen_k, -1 means equal to s (default:-1)
-s_k seqlen_k (including new key/value), -1 means equal to s (default:-1)
-d head dim for q, k (default:128)
-d_v head dim for v, -1 means equal to d (default:-1)
-scale_s scale factor of S. 0 means equal to 1/sqrt(hdim). (default:0)
......@@ -68,10 +69,15 @@ args:
-init init method. ui, uniform random int, ni, normalized random int (default:uf)
uf, uniform random float, nf, normalized random float, tf, trig float, uf:q, quantization
-seed random seed used for initializing input tensors. 0 for non-deterministic seed (default:11939)
-drop_seed seed for random number generator (default:1)
-drop_offset offset for random number generator (default:0)
-drop_prefs seed and offset values are present on GPU; 0 - host, 1 - device/GPU (default:0)
-warmup number of iterations before benchmark the kernel (default:5)
-repeat number of iterations to benchmark the kernel (default:20)
```
Example: `./bin/tile_example_fmha_fwd -b=1 -h=16 -s=16384 -d=128` will run a fmha case with batch=1, nhead=16, sequence length=16384, hdim=128, fp16 case.
Example 1: `./bin/tile_example_fmha_fwd -b=1 -h=16 -s=16384 -d=128` will run a fmha case with batch=1, nhead=16, sequence length=16384, hdim=128, fp16 case.
Example 2: `./bin/tile_example_fmha_fwd -b=1 -h=8 -s=16384 -d=64 -drop_prefs=1 -drop_seed=10 -drop_offset=1234` will run a fmha case with
batch=1, nhead=8, sequence length=16384, hdim=64, drop_seed=0 (in GPU memory), drop_offset=1234 (in GPU memory) fp16 case
## support features
Currently we are still in rapid development stage, so more features/optimizations will be coming soon.
......
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