mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +00:00
Hip tensor permute (#1002)
* adding files for F32 example
* adding functioning implementation with scalar multiplication and unary operator support
* added fp 16 type check in unary square
* updating scalar multiplication as an operator
* functioning version with scalar operator
* changing strides for col major
* updated column major implementation
* working column major implementation
* cleaned up comments, rearranged/renamed files
[ROCm/composable_kernel commit: 454cf7bd1f]
This commit is contained in:
arai713
@@ -0,0 +1,55 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <memory>
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#include <array>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/device_base.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 InDataTypeTuple,
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typename OutDataTypeTuple,
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typename ElementwiseOperation,
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typename UnaryOperation,
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typename Scale,
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index_t NumDim>
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struct DeviceElementwise : public BaseOperator
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{
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static constexpr int NumInput = InDataTypeTuple::Size();
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static constexpr int NumOutput = OutDataTypeTuple::Size();
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virtual std::unique_ptr<BaseArgument>
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MakeArgumentPointer(const std::array<index_t, NumDim> lengths,
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const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
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const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
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const std::array<const void*, NumInput> in_dev_buffers,
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const std::array<void*, NumOutput> out_dev_buffers,
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ElementwiseOperation elementwise_op,
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UnaryOperation unary_op,
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Scale scale_op) = 0;
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virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
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}; // namespace device
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template <typename InDataTypeTuple,
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typename OutDataTypeTuple,
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typename ElementwiseOperation,
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typename UnaryOperation,
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typename Scale,
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index_t NumDim>
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using DeviceElementwisePtr = std::unique_ptr<DeviceElementwise<InDataTypeTuple,
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OutDataTypeTuple,
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ElementwiseOperation,
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UnaryOperation,
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Scale,
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NumDim>>;
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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@@ -0,0 +1,329 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/math.hpp"
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#include "ck/utility/sequence.hpp"
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#include "ck/tensor_operation/gpu/device/device_elementwise_scale.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_1d_scale.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/host_utility/stream_utility.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 InDataTypeTuple,
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typename OutDataTypeTuple,
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typename ElementwiseOperation,
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typename UnaryOperation,
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typename Scale,
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index_t NumDim,
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index_t MPerThread,
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typename InScalarPerVectorSeq,
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typename OutScalarPerVectorSeq>
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struct DeviceElementwiseImpl : public DeviceElementwise<InDataTypeTuple,
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OutDataTypeTuple,
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ElementwiseOperation,
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UnaryOperation,
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Scale,
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NumDim>
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{
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static constexpr int NumInput = InDataTypeTuple::Size();
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static constexpr int NumOutput = OutDataTypeTuple::Size();
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static_assert(NumInput == InScalarPerVectorSeq::Size() &&
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NumOutput == OutScalarPerVectorSeq::Size(),
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"Tuple size is inconsistent with the number of in/out!");
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static auto GenerateInDataTypePointerTuple()
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{
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return generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
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return static_cast<const DataType*>(nullptr);
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},
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Number<NumInput>{});
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};
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static auto GenerateOutDataTypePointerTuple()
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{
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return generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
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return static_cast<DataType*>(nullptr);
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},
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Number<NumOutput>{});
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};
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using InDataTypePointerTuple = decltype(GenerateInDataTypePointerTuple());
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using OutDataTypePointerTuple = decltype(GenerateOutDataTypePointerTuple());
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template <typename Desc_M>
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static auto PadDescriptor_M_1d(Desc_M desc_m, index_t gridSize, index_t blockSize)
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{
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constexpr auto I0 = Number<0>{};
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const auto m = desc_m.GetLength(I0);
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const index_t loop_step = gridSize * blockSize * MPerThread;
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const auto pad = math::integer_least_multiple(m, loop_step) - m;
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const auto desc_m_pad =
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transform_tensor_descriptor(desc_m,
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make_tuple(make_right_pad_transform(m, pad)),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0>{}));
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return desc_m_pad;
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}
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static auto MakeDescriptor_M(const std::array<index_t, NumDim>& lengths,
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const std::array<index_t, NumDim>& 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 lengths[I]; }, Number<NumDim>{});
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auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<NumDim>{});
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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(NumDim > 1)
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{
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const auto desc_m = 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<NumDim>{})),
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make_tuple(Sequence<0>{}));
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return PadDescriptor_M_1d(desc_m, gridSize, blockSize);
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}
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else
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return PadDescriptor_M_1d(desc, gridSize, blockSize);
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}
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template <index_t TupleSize>
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static auto GenerateInOutGrid1dDescTuple(Number<TupleSize>)
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{
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return generate_tuple(
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[&](auto) {
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if constexpr(NumDim > 1)
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{
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return MakeDescriptor_M({1, 1}, {1, 1}, 1, 1);
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}
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else
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{
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return MakeDescriptor_M({1}, {1}, 1, 1);
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};
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},
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Number<TupleSize>{});
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};
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using InGrid1dDescTuple = decltype(GenerateInOutGrid1dDescTuple(Number<NumInput>{}));
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using OutGrid1dDescTuple = decltype(GenerateInOutGrid1dDescTuple(Number<NumOutput>{}));
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using GridwiseElementwise = GridwiseElementwise_1D<InGrid1dDescTuple,
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OutGrid1dDescTuple,
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InDataTypePointerTuple,
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OutDataTypePointerTuple,
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ElementwiseOperation,
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UnaryOperation,
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Scale,
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MPerThread,
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InScalarPerVectorSeq,
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OutScalarPerVectorSeq>;
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struct Argument : public BaseArgument
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{
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Argument(const std::array<index_t, NumDim> lengths,
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const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
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const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
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const std::array<const void*, NumInput> in_dev_buffers,
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const std::array<void*, NumOutput> out_dev_buffers,
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ElementwiseOperation elementwise_op,
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UnaryOperation unary_op,
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Scale scale_op)
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: lengths_(lengths),
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inStridesArray_(inStridesArray),
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outStridesArray_(outStridesArray),
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elementwise_op_(elementwise_op),
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unary_op_(unary_op),
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scale_op_(scale_op),
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blockSize_(256)
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{
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in_dev_buffers_ = generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
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return static_cast<const DataType*>(in_dev_buffers[I.value]);
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},
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Number<NumInput>{});
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out_dev_buffers_ = generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
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return static_cast<DataType*>(out_dev_buffers[I.value]);
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},
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Number<NumOutput>{});
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}
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InDataTypePointerTuple in_dev_buffers_;
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OutDataTypePointerTuple out_dev_buffers_;
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std::array<index_t, NumDim> lengths_;
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std::array<std::array<index_t, NumDim>, NumInput> inStridesArray_;
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std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray_;
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ElementwiseOperation elementwise_op_;
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UnaryOperation unary_op_;
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Scale scale_op_;
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index_t blockSize_;
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};
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struct Invoker : public BaseInvoker
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{
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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index_t gridSize = getAvailableComputeUnitCount(stream_config);
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auto in_grid_1d_desc_tuple = generate_tuple(
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[&](auto I) {
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return MakeDescriptor_M(
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arg.lengths_, arg.inStridesArray_[I.value], gridSize, arg.blockSize_);
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},
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Number<NumInput>{});
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auto out_grid_1d_desc_tuple = generate_tuple(
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[&](auto I) {
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return MakeDescriptor_M(
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arg.lengths_, arg.outStridesArray_[I.value], gridSize, arg.blockSize_);
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},
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Number<NumOutput>{});
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const auto kernel = kernel_elementwise_1d<GridwiseElementwise,
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InGrid1dDescTuple,
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OutGrid1dDescTuple,
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InDataTypePointerTuple,
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OutDataTypePointerTuple,
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ElementwiseOperation,
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UnaryOperation,
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Scale>;
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float elapsed_time = launch_and_time_kernel(stream_config,
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kernel,
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dim3(gridSize),
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dim3(arg.blockSize_),
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0,
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in_grid_1d_desc_tuple,
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out_grid_1d_desc_tuple,
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arg.in_dev_buffers_,
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arg.out_dev_buffers_,
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arg.elementwise_op_,
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arg.unary_op_,
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arg.scale_op_);
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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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};
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static bool IsSupportedArgument(const Argument& arg)
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{
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if(arg.lengths_.back() % MPerThread != 0)
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return false;
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auto IsScalarPerVectorValid = [&](const std::array<index_t, NumDim>& lengths,
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const std::array<index_t, NumDim>& strides,
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index_t scalarPerVector) {
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if(strides.back() == 1 && lengths.back() % scalarPerVector == 0)
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return true;
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if(strides.back() != 1 && scalarPerVector == 1)
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return true;
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return false;
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};
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bool valid = true;
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static_for<0, NumInput, 1>{}([&](auto I) {
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if(!IsScalarPerVectorValid(
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arg.lengths_, arg.inStridesArray_[I.value], InScalarPerVectorSeq::At(I)))
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valid = false;
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});
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static_for<0, NumOutput, 1>{}([&](auto I) {
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if(!IsScalarPerVectorValid(
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arg.lengths_, arg.outStridesArray_[I.value], OutScalarPerVectorSeq::At(I)))
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valid = false;
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});
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return valid;
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};
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bool IsSupportedArgument(const BaseArgument* p_arg) override
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{
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return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
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}
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static auto
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MakeArgument(const std::array<index_t, NumDim> lengths,
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const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
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const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
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const std::array<const void*, NumInput> in_dev_buffers,
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const std::array<void*, NumOutput> out_dev_buffers,
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ElementwiseOperation elementwise_op,
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UnaryOperation unary_op,
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Scale scale_op)
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{
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return Argument{lengths,
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inStridesArray,
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outStridesArray,
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in_dev_buffers,
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out_dev_buffers,
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elementwise_op,
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unary_op,
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scale_op};
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}
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std::unique_ptr<BaseArgument>
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MakeArgumentPointer(const std::array<index_t, NumDim> lengths,
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const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
|
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const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
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const std::array<const void*, NumInput> in_dev_buffers,
|
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const std::array<void*, NumOutput> out_dev_buffers,
|
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ElementwiseOperation elementwise_op,
|
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UnaryOperation unary_op,
|
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Scale scale_op) override
|
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{
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return std::make_unique<Argument>(lengths,
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inStridesArray,
|
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outStridesArray,
|
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in_dev_buffers,
|
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out_dev_buffers,
|
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elementwise_op,
|
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unary_op,
|
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scale_op);
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}
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|
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static auto MakeInvoker() { return Invoker{}; }
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std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
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{
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return std::make_unique<Invoker>();
|
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};
|
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}; // namespace device
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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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@@ -355,8 +355,8 @@ struct UnarySquare
|
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template <typename T>
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__host__ __device__ void operator()(T& y, const T& x) const
|
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{
|
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static_assert(is_same_v<T, float> || is_same_v<T, double> || is_same_v<T, int32_t> ||
|
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is_same_v<T, int8_t>
|
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static_assert(is_same_v<T, float> || is_same_v<T, half_t> || is_same_v<T, double> ||
|
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is_same_v<T, int32_t> || is_same_v<T, int8_t>
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#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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|| is_same_v<T, int4_t>
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#endif
|
||||
|
||||
@@ -0,0 +1,224 @@
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||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/tensor_description/cluster_descriptor.hpp"
|
||||
#include "ck/utility/data_type.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseElementwise1dFunctor,
|
||||
typename InGrid1dDescTuple,
|
||||
typename OutGrid1dDescTuple,
|
||||
typename InDataTypePointerTuple,
|
||||
typename OutDataTypePointerTuple,
|
||||
typename ElementwiseOperation,
|
||||
typename UnaryOperation,
|
||||
typename Scale>
|
||||
__global__ void kernel_elementwise_1d(const InGrid1dDescTuple in_grid_1d_desc_tuple,
|
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const OutGrid1dDescTuple out_grid_1d_desc_tuple,
|
||||
const InDataTypePointerTuple p_in_global_tuple,
|
||||
const OutDataTypePointerTuple p_out_global_tuple,
|
||||
const ElementwiseOperation elementwise_op,
|
||||
const UnaryOperation unary_op,
|
||||
const Scale scale_op)
|
||||
{
|
||||
GridwiseElementwise1dFunctor::Run(in_grid_1d_desc_tuple,
|
||||
out_grid_1d_desc_tuple,
|
||||
p_in_global_tuple,
|
||||
p_out_global_tuple,
|
||||
elementwise_op,
|
||||
unary_op,
|
||||
scale_op);
|
||||
}
|
||||
|
||||
template <typename InGrid1dDescTuple,
|
||||
typename OutGrid1dDescTuple,
|
||||
typename InDataTypePointerTuple,
|
||||
typename OutDataTypePointerTuple,
|
||||
typename ElementwiseOperation,
|
||||
typename UnaryOperation,
|
||||
typename Scale,
|
||||
index_t MPerThread,
|
||||
typename InScalarPerVectorSeq,
|
||||
typename OutScalarPerVectorSeq>
|
||||
struct GridwiseElementwise_1D
|
||||
{
|
||||
static constexpr index_t NumInput = InDataTypePointerTuple::Size();
|
||||
static constexpr index_t NumOutput = OutDataTypePointerTuple::Size();
|
||||
|
||||
static_assert(NumInput == InScalarPerVectorSeq::Size() &&
|
||||
NumOutput == OutScalarPerVectorSeq::Size() &&
|
||||
NumInput == InGrid1dDescTuple::Size() &&
|
||||
NumOutput == OutGrid1dDescTuple::Size(),
|
||||
"Tuple size is inconsistent with the number of in/out!");
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
|
||||
static constexpr auto thread_buffer_desc_m =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<MPerThread>{}));
|
||||
|
||||
using PassThroughOp = tensor_operation::element_wise::PassThrough;
|
||||
|
||||
__device__ static void Run(const InGrid1dDescTuple in_grid_1d_desc_tuple,
|
||||
const OutGrid1dDescTuple out_grid_1d_desc_tuple,
|
||||
const InDataTypePointerTuple p_in_global_tuple,
|
||||
const OutDataTypePointerTuple p_out_global_tuple,
|
||||
const ElementwiseOperation elementwise_op,
|
||||
const UnaryOperation unary_op,
|
||||
const Scale scale_op)
|
||||
{
|
||||
const index_t thread_global_id = get_thread_global_1d_id();
|
||||
|
||||
auto in_thread_buf_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
|
||||
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
|
||||
|
||||
return StaticBuffer<AddressSpaceEnum::Vgpr, DataType, MPerThread, true>{};
|
||||
},
|
||||
Number<NumInput>{});
|
||||
|
||||
auto out_thread_buf_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
|
||||
using DataType = remove_pointer_t<DataTypePointer>;
|
||||
|
||||
return StaticBuffer<AddressSpaceEnum::Vgpr, DataType, MPerThread, true>{};
|
||||
},
|
||||
Number<NumOutput>{});
|
||||
|
||||
auto in_global_buf_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
static_assert(in_grid_1d_desc_tuple[I].GetNumOfDimension() == 1);
|
||||
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_in_global_tuple[I], in_grid_1d_desc_tuple[I].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumInput>{});
|
||||
|
||||
auto out_global_buf_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
static_assert(out_grid_1d_desc_tuple[I].GetNumOfDimension() == 1);
|
||||
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_out_global_tuple[I], out_grid_1d_desc_tuple[I].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumOutput>{});
|
||||
|
||||
const auto thread_global_offset = make_multi_index(thread_global_id * MPerThread);
|
||||
|
||||
const index_t blockSize = get_block_size();
|
||||
const index_t blockPerGrid = get_grid_size();
|
||||
const auto M = in_grid_1d_desc_tuple[I0].GetLength(I0);
|
||||
const index_t loop_step = blockPerGrid * blockSize * MPerThread;
|
||||
const auto loop_step_index = make_multi_index(loop_step);
|
||||
|
||||
auto in_global_load_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
|
||||
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
|
||||
|
||||
return ThreadwiseTensorSliceTransfer_v2<DataType,
|
||||
DataType,
|
||||
decltype(in_grid_1d_desc_tuple[I]),
|
||||
decltype(thread_buffer_desc_m),
|
||||
Sequence<MPerThread>, // SliceLengths
|
||||
Sequence<0>, // DimAccessOrder
|
||||
0, // SrcVectorDim
|
||||
InScalarPerVectorSeq::At(
|
||||
I), // ScalarPerVector
|
||||
1, // SrcScalarStrideInVector
|
||||
false>{in_grid_1d_desc_tuple[I],
|
||||
thread_global_offset};
|
||||
},
|
||||
Number<NumInput>{});
|
||||
|
||||
auto out_global_store_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
|
||||
using DataType = remove_pointer_t<DataTypePointer>;
|
||||
|
||||
return ThreadwiseTensorSliceTransfer_v1r3<DataType,
|
||||
DataType,
|
||||
decltype(thread_buffer_desc_m),
|
||||
decltype(out_grid_1d_desc_tuple[I]),
|
||||
PassThroughOp,
|
||||
Sequence<MPerThread>, // SliceLengths
|
||||
Sequence<0>, // DimAccessOrder
|
||||
0, // SrcVectorDim
|
||||
OutScalarPerVectorSeq::At(I),
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
false>(
|
||||
out_grid_1d_desc_tuple[I], thread_global_offset, PassThroughOp{});
|
||||
},
|
||||
Number<NumOutput>{});
|
||||
|
||||
index_t num_iter = M / (loop_step);
|
||||
do
|
||||
{
|
||||
static_for<0, NumInput, 1>{}([&](auto I) {
|
||||
in_global_load_tuple(I).Run(in_grid_1d_desc_tuple[I],
|
||||
in_global_buf_tuple[I],
|
||||
thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
in_thread_buf_tuple(I));
|
||||
|
||||
in_global_load_tuple(I).MoveSrcSliceWindow(in_grid_1d_desc_tuple[I],
|
||||
loop_step_index);
|
||||
});
|
||||
|
||||
static_for<0, MPerThread, 1>{}([&](auto iM) {
|
||||
// get reference to in data
|
||||
auto uop_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto I) -> auto& { return in_thread_buf_tuple(I)(iM); },
|
||||
Number<NumInput>{});
|
||||
|
||||
// get reference to dst data
|
||||
auto out_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto I) -> auto& { return out_thread_buf_tuple(I)(iM); },
|
||||
Number<NumOutput>{});
|
||||
|
||||
unpack2(unary_op, uop_data_refs, uop_data_refs);
|
||||
|
||||
auto sop_in_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto I) -> auto& { return in_thread_buf_tuple(I)(iM); },
|
||||
Number<NumInput>{});
|
||||
|
||||
auto sop_out_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto I) -> auto& { return in_thread_buf_tuple(I)(iM); },
|
||||
Number<NumInput>{});
|
||||
|
||||
unpack2(scale_op, sop_out_data_refs, sop_in_data_refs);
|
||||
|
||||
const auto in_data_refs = generate_tie(
|
||||
// return type should be lvalue
|
||||
[&](auto I) -> const auto& { return in_thread_buf_tuple(I)(iM); },
|
||||
Number<NumInput>{});
|
||||
|
||||
unpack2(elementwise_op, out_data_refs, in_data_refs);
|
||||
});
|
||||
|
||||
static_for<0, NumOutput, 1>{}([&](auto I) {
|
||||
out_global_store_tuple(I).Run(thread_buffer_desc_m,
|
||||
make_tuple(I0),
|
||||
out_thread_buf_tuple[I],
|
||||
out_grid_1d_desc_tuple[I],
|
||||
out_global_buf_tuple(I));
|
||||
|
||||
out_global_store_tuple(I).MoveDstSliceWindow(out_grid_1d_desc_tuple[I],
|
||||
loop_step_index);
|
||||
});
|
||||
} while(--num_iter);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user