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/*
* Copyright (c) 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_ASSEMBLY_HELPER_H__
#define __ARM_ASSEMBLY_HELPER_H__
#include "arm_compute/core/ITensor.h"
#include "support/ToolchainSupport.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/Log.h"
#include "arm_compute/core/NEON/kernels/assembly/NEGEMMAssemblyWrapper.h"
#include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
namespace arm_compute
{
/** Assembly kernel glue */
template <typename TypeInput, typename TypeOutput>
class AssemblyKernelGlue final
{
public:
/** Operator type */
using TypeOperator = TypeInput;
/** Result type */
using TypeResult = TypeOutput;
/** Default constructor. */
AssemblyKernelGlue()
: _gemm_kernel_asm(nullptr), _optimised_kernel(nullptr), _a(nullptr), _b(nullptr), _d(nullptr)
{
}
/** Assembly Gemm */
using AssemblyGemm = arm_gemm::GemmCommon<TypeInput, TypeOutput>;
/** Prevent instances of this class from being copy constructed */
const AssemblyKernelGlue<TypeInput, TypeOutput> &operator=(const AssemblyKernelGlue<TypeInput, TypeOutput> &) = delete;
/** Prevent instances of this class from being copied */
AssemblyKernelGlue(const AssemblyKernelGlue<TypeInput, TypeOutput> &) = delete;
/** Assembly Gemm kernel */
std::unique_ptr<AssemblyGemm> _gemm_kernel_asm;
/** Optimised NEON kernel */
std::unique_ptr<INEKernel> _optimised_kernel;
/** Input A */
const ITensor *_a;
/** Input B */
const ITensor *_b;
/** Output */
ITensor *_d;
/** Configures the arrays pointers and strides in the assembly kernel and executes the assembly kernel.
* The call to set_arrays is needed to deal with the input sizes containing batches (dims > 2)
*/
inline void run()
{
const int lda = _a->info()->strides_in_bytes().y() / sizeof(TypeInput);
const int ldb = _b->info()->strides_in_bytes().y() / sizeof(TypeInput);
const int ldd = _d->info()->strides_in_bytes().y() / sizeof(TypeOutput);
// Configure kernel window
Window window = calculate_max_window(*_d->info());
const auto in1_ptr = reinterpret_cast<const TypeInput *>(_b->buffer());
// Only iterate over batches
Window win(window);
win.set(0, Window::Dimension(0, 1, 1));
win.set(1, Window::Dimension(0, 1, 1));
Iterator in0(_a, window);
Iterator out(_d, window);
execute_window_loop(win, [&](const Coordinates &)
{
const auto in0_ptr = reinterpret_cast<const TypeInput *>(in0.ptr());
auto out_ptr = reinterpret_cast<TypeOutput *>(out.ptr());
_gemm_kernel_asm->set_arrays(in0_ptr, lda, in1_ptr, ldb, out_ptr, ldd);
NEScheduler::get().schedule(_optimised_kernel.get(), Window::DimX);
},
in0, out);
}
};
/** Float 32 assembly kernel glue */
using AssemblyKernelGlueF32 = AssemblyKernelGlue<float, float>;
/** Uint 8 to Uint 32 kernel glue */
using AssemblyKernelGlueU8U32 = AssemblyKernelGlue<uint8_t, uint32_t>;
/** Int 8 to Int 32 kernel glue */
using AssemblyKernelGlueS8S32 = AssemblyKernelGlue<int8_t, int32_t>;
/** Allocate a workspace tensor.
*
* @param[in] workspace_size Size to allocate.
* @param[out] workspace Tensor to allocate.
* @param[in] memory_group Tensor memory group.
* @param[in] alignment Workspace memory alignment.
* @param[in] num_threads Number of workspace threads.
*/
inline void allocate_workspace(size_t workspace_size, Tensor &workspace, MemoryGroup &memory_group, size_t alignment, unsigned int num_threads)
{
ARM_COMPUTE_ERROR_ON_MSG(workspace_size == 0, "size cannot be 0");
workspace.allocator()->init(TensorInfo(TensorShape{ (workspace_size + alignment - 1) * num_threads }, 1, DataType::S8));
workspace.allocator()->allocate();
}
/** Create a wrapper kernel.
*
* @param[in] a Input tensor A.
* @param[in] b Input tensor B.
* @param[in] c (Optional) Input tensor C.
* @param[out] d Output tensor.
* @param[in] alpha Alpha value.
* @param[in] beta Beta value.
*
* @return the wrapper kernel.
*/
template <typename T>
std::unique_ptr<NEGEMMAssemblyWrapper<T>> create_wrapper_kernel(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta)
{
// rework this function, why are we checking data type and other things here ? should we create another function can_run_optimised_kernel() ?
#if defined(__arm__)
if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && a->info()->data_type() == DataType::F32 && (c == nullptr || beta == 0.f))
{
return support::cpp14::make_unique<NEGEMMAssemblyWrapper<T>>();
}
#elif defined(__aarch64__)
if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && a->info()->data_type() == DataType::F32 && (c == nullptr || beta == 0.f))
{
return support::cpp14::make_unique<NEGEMMAssemblyWrapper<T>>();
}
else if(a->info()->data_type() == DataType::F16 && (c == nullptr || beta == 0.f))
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
return support::cpp14::make_unique<NEGEMMAssemblyWrapper<T>>();
#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
ARM_COMPUTE_ERROR("Recompile the library with arch=arm64-v8.2-a to enable support for FP16.");
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
}
#endif /* defined(__arm__) || defined(__aarch64__) */
return nullptr;
}
/** Setup assembly kernel.
*
* @param[in] a Input tensor A.
* @param[in] b Input tensor B.
* @param[in] c (Optional) Input tensor C.
* @param[in] d Output tensor.
* @param[in] alpha Alpha value.
* @param[in] beta Beta value.
* @param[out] workspace Workspace tensor
* @param[in] memory_group Tensor memory group.
* @param[out] asm_glue Assembly glue kernel.
*
* @return True if the assembly kernel is setup correctly.
*/
template <typename T>
inline bool setup_assembly_kernel(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta,
Tensor &workspace, MemoryGroup &memory_group, T &asm_glue)
{
const ::CPUInfo *ci = get_CPUInfo();
const int M = d->info()->tensor_shape().y();
const int N = d->info()->tensor_shape().x();
const int K = a->info()->tensor_shape().x();
unsigned int num_threads = NEScheduler::get().num_threads();
// unique_ptr to a Gemm object
std::unique_ptr<typename T::AssemblyGemm> asm_gemm(arm_gemm::gemm<typename T::TypeOperator, typename T::TypeResult>(*ci, M, N, K, false, false, alpha, beta, num_threads,
false));
// arm_compute wrapper for the Gemm object (see above)
std::unique_ptr<NEGEMMAssemblyWrapper<typename T::AssemblyGemm>> acl_gemm_wrapper = create_wrapper_kernel<typename T::AssemblyGemm>(a, b, c, d, alpha, beta);
if(acl_gemm_wrapper != nullptr && asm_gemm != nullptr)
{
acl_gemm_wrapper->configure(asm_gemm.get());
const size_t workspace_size = asm_gemm->get_working_size();
if(workspace_size)
{
// Allocate workspace
allocate_workspace(workspace_size, workspace, memory_group, 4096, num_threads);
asm_gemm->set_working_space(reinterpret_cast<typename T::TypeResult *>(workspace.buffer()));
}
const unsigned int window_size = asm_gemm->get_window_size();
if(window_size < num_threads)
{
num_threads = window_size;
asm_gemm->set_nthreads(num_threads);
}
asm_glue._gemm_kernel_asm = std::move(asm_gemm);
asm_glue._optimised_kernel = std::move(acl_gemm_wrapper);
// We need to setup the ptrs in the run() method
asm_glue._a = a;
asm_glue._b = b;
asm_glue._d = d;
return true;
}
return false;
}
}
#endif /* __ARM_ASSEMBLY_HELPER_H__ */
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