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https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 20:51:23 +00:00
elementwise op (#238)
* Add elementwise operation kernel and example * Add comment * Add template argument of dim . Prepare to support multiple dimension * Rename example * Support 1 dimension * Add static assert * Add comment * Extract pad * Remove redundant argument * Support any dimension for elementwise operation * Remove line * Let it be the multiple number of CU * Move thread per block to the parameter of constructor * rename threadPerBlock with blockSize * Support double * rename kernel function name * remove redundant include header * Refine type * Need to the final dimension * Refine variable name * Refine type * Use index_t instead of int in API Co-authored-by: rocking <chunylai@amd.com>
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rocking5566
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#pragma once
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#include <iostream>
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#include <vector>
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#include "device.hpp"
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#include "device_base.hpp"
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#include "gridwise_binary_elementwise_1d.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ComputeDataType,
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typename ElementwiseFunctor,
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index_t Dim,
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index_t ScalarPerVector>
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struct DeviceBinaryElementwise : public BaseOperator
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{
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DeviceBinaryElementwise(index_t blockSize = 256) : BaseOperator(), blockSize_(blockSize) {}
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static constexpr auto I0 = Number<0>{};
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template <typename Desc_M0>
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static auto PadDescriptor_M0_1d(Desc_M0 desc_m0, index_t gridSize, index_t blockSize)
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{
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const auto m0 = desc_m0.GetLength(I0);
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const index_t loop_step = gridSize * blockSize * ScalarPerVector;
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const auto pad = math::integer_least_multiple(m0, loop_step) - m0;
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const auto desc_m0_pad =
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transform_tensor_descriptor(desc_m0,
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make_tuple(make_right_pad_transform(m0, pad)),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0>{}));
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return desc_m0_pad;
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}
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static auto MakeDescriptor_M0(const std::vector<index_t>& shape,
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const std::vector<index_t>& stride,
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index_t gridSize,
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index_t blockSize)
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{
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auto tupleOfShape = generate_tuple([&](auto I) { return shape[I]; }, Number<Dim>{});
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auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<Dim>{});
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// nd desc - [s0, s1, s2, ...]
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const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
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// merge nd to 1d desc - [s0 * s1 * ...]
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if constexpr(Dim > 1)
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{
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const auto desc_m0 = transform_tensor_descriptor(
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desc,
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make_tuple(make_merge_transform(tupleOfShape)),
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make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<Dim>{})),
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make_tuple(Sequence<0>{}));
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return PadDescriptor_M0_1d(desc_m0, gridSize, blockSize);
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}
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else
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return PadDescriptor_M0_1d(desc, gridSize, blockSize);
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}
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using GridDesc_M0 = decltype(MakeDescriptor_M0({1, 1}, {1, 1}, 1, 1));
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using GridwiseBinEltwise = GridwiseBinaryElementwise_1D<ADataType,
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BDataType,
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CDataType,
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ComputeDataType,
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GridDesc_M0,
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ElementwiseFunctor,
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ScalarPerVector>;
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struct Argument : public BaseArgument
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{
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Argument(const ADataType* p_a,
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const BDataType* p_b,
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CDataType* p_c,
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const std::vector<index_t>& shape,
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const std::vector<index_t>& stride_a,
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const std::vector<index_t>& stride_b,
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const std::vector<index_t>& stride_c,
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ElementwiseFunctor functor,
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index_t blockSize)
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: p_a_(p_a),
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p_b_(p_b),
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p_c_(p_c),
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shape_(shape),
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functor_(functor),
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gridSize_(120) // FIXME - Calculate the grid size by number of CU in the future
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{
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a_grid_desc_m0_ = MakeDescriptor_M0(shape, stride_a, gridSize_, blockSize);
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b_grid_desc_m0_ = MakeDescriptor_M0(shape, stride_b, gridSize_, blockSize);
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c_grid_desc_m0_ = MakeDescriptor_M0(shape, stride_c, gridSize_, blockSize);
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}
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const ADataType* p_a_;
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const BDataType* p_b_;
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CDataType* p_c_;
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std::vector<int> shape_;
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GridDesc_M0 a_grid_desc_m0_;
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GridDesc_M0 b_grid_desc_m0_;
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GridDesc_M0 c_grid_desc_m0_;
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ElementwiseFunctor functor_;
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index_t gridSize_;
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};
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struct Invoker : public BaseInvoker
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{
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Invoker(index_t blockSize) : BaseInvoker(), blockSize_(blockSize) {}
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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const auto kernel = kernel_binary_elementwise_1d<GridwiseBinEltwise,
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ADataType,
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BDataType,
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CDataType,
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GridDesc_M0,
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ElementwiseFunctor>;
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float elapsed_time = launch_and_time_kernel(stream_config,
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kernel,
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dim3(arg.gridSize_),
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dim3(blockSize_),
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0,
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arg.p_a_,
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arg.p_b_,
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arg.p_c_,
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arg.a_grid_desc_m0_,
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arg.b_grid_desc_m0_,
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arg.c_grid_desc_m0_,
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arg.functor_);
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return elapsed_time;
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}
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// polymorphic
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float Run(const BaseArgument* p_arg,
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const StreamConfig& stream_config = StreamConfig{}) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
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}
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index_t blockSize_;
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};
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bool IsSupportedArgument(const BaseArgument* p_arg) override
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{
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const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
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if(pArg == nullptr)
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return false;
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if(pArg->shape_.back() % ScalarPerVector != 0)
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return false;
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return true;
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};
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std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
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const void* p_b,
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void* p_c,
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std::vector<index_t> shape,
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std::vector<index_t> stride_a,
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std::vector<index_t> stride_b,
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std::vector<index_t> stride_c,
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ElementwiseFunctor functor)
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{
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return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
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static_cast<const BDataType*>(p_b),
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static_cast<CDataType*>(p_c),
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shape,
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stride_a,
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stride_b,
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stride_c,
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functor,
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blockSize_);
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}
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std::unique_ptr<BaseInvoker> MakeInvokerPointer()
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{
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return std::make_unique<Invoker>(Invoker{blockSize_});
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}
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std::string GetTypeString() const override
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{
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auto str = std::stringstream();
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// clang-format off
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str << "DeviceBinaryElementwise"
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<< "<"
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<< "ScalarPerVector = " << ScalarPerVector
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<< ">";
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// clang-format on
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return str.str();
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}
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index_t blockSize_;
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};
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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