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/*
* Copyright (c) 2018-2019 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_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__
#define __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/NEON/kernels/assembly/Helpers.h"
#include "arm_compute/core/NEON/kernels/assembly/INEGEMMWrapperKernel.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
namespace arm_compute
{
/** Unit of work for @ref NEGEMMInterleavedPrepareBWrapperKernel to process */
struct PrepareBWorkload
{
/** Constructor
*
* @param[in] offset_b Offset from the start of b's allocation
* @param[in] offset_transformed_b Offset from the start of transformed_b's allocation.
* @param[in] x0 First value to process along the X dimension (N).
* @param[in] xmax Last value to process along the X dimension (N).
* @param[in] k0 First value to process along the K dimension.
* @param[in] kmax Last value to process along the K dimension.
*/
PrepareBWorkload(unsigned int offset_b, unsigned int offset_transformed_b, unsigned int x0, unsigned int xmax, unsigned int k0, unsigned int kmax)
: _offset_b(offset_b), _offset_transformed_b(offset_transformed_b), _x0(x0), _xmax(xmax), _k0(k0), _kmax(kmax)
{
}
unsigned int _offset_b; /**< Offset from the start of b's allocation.*/
unsigned int _offset_transformed_b; /**< Offset from the start of transformed_b's allocation.*/
unsigned int _x0; /**< First value to process along the X dimension (N). */
unsigned int _xmax; /**< Last value to process along the X dimension (N). */
unsigned int _k0; /**< First value to process along the K dimension. */
unsigned int _kmax; /**< Last value to process along the K dimension. */
};
namespace detail
{
// Call the lambda function for each workload generated by the passed window.
template <typename strategy, bool use_buffer_manager, typename Lambda>
void for_each_element_in_window(const Window &window, const ITensor *b, ITensor *transformed_b, unsigned int N, unsigned int K, Lambda &&lambda)
{
unsigned int wl_index = 0;
unsigned int num_buffers = 0, reshaped_block_size = 0;
if(use_buffer_manager)
{
num_buffers = transformed_b->info()->tensor_shape()[1];
reshaped_block_size = transformed_b->info()->strides_in_bytes().y();
}
unsigned int offset_transformed_b = transformed_b->info()->offset_first_element_in_bytes();
execute_window_loop(window, [&](const Coordinates & coordinates)
{
const unsigned int x0 = coordinates.x();
const unsigned int k0 = coordinates.y();
const unsigned int multi = coordinates.z();
const unsigned int offset_b = b->info()->offset_element_in_bytes(Coordinates(0, 0, multi));
const unsigned int xmax = std::min(x0 + window.x().step(), N);
const unsigned int kmax = std::min(k0 + window.y().step(), K);
/* Figure out the size of each block. */
unsigned int x_size = (xmax - x0);
unsigned int k_size = (kmax - k0);
/* Round sizes up as needed. */
x_size = ceil_to_multiple(x_size, strategy::out_width());
k_size = ceil_to_multiple(k_size, strategy::k_unroll());
lambda(PrepareBWorkload(offset_b, offset_transformed_b, x0, xmax, k0, kmax));
//Each workload represents one block:
if(use_buffer_manager)
{
// Rotate through the BufferManager's buffers:
wl_index++;
offset_transformed_b = (wl_index % num_buffers) * reshaped_block_size;
}
else
{
offset_transformed_b += (x_size * k_size * sizeof(typename strategy::operand_type));
}
});
}
// Calculate the size of transformed_b:
template <typename strategy>
unsigned int get_B_pretransposed_array_size(unsigned int N, unsigned int K, const BlockSizes &bs, unsigned int multis)
{
// How many full blocks do N / K contain ?
size_t num_full_k = K / bs.k_block;
size_t num_full_x = N / bs.x_block;
ARM_COMPUTE_ERROR_ON(bs.x_block % strategy::out_width() != 0);
ARM_COMPUTE_ERROR_ON(bs.k_block % strategy::k_unroll() != 0);
size_t normal_x_size = bs.x_block;
size_t normal_k_size = bs.k_block;
// Round up the leftovers to be a multiple of the strategy processing size:
size_t left_over_x_size = ceil_to_multiple(N % bs.x_block, strategy::out_width());
size_t left_over_k_size = ceil_to_multiple(K % bs.k_block, strategy::k_unroll());
// Calculate the total size of the buffer:
size_t total = num_full_k * normal_k_size * (num_full_x * normal_x_size + left_over_x_size);
total += left_over_k_size * (left_over_x_size + num_full_x * normal_x_size);
total *= multis;
return total;
}
} // namespace detail
/** Common interface for the templated wrappers around the B reshape NEON assembly implementations */
class NEGEMMInterleavedPrepareBWrapperKernel : public INEKernel
{
public:
/** Transform the block at the given coordinates
*
* @param[in] wl Workload to process.
* @param[in] info Information about the current thread.
*/
virtual void transform(const PrepareBWorkload &wl, const ThreadInfo &info) = 0;
/** Generate an array of workloads
*
* @param[out] workloads Container to store the generated workloads.
*/
virtual void create_workloads(std::vector<PrepareBWorkload> &workloads) = 0;
/** Return the block_sizes used to resape B
*
* The same block sizes must be used to reshape A and for the matrix multiplication
*
* @return The block sizes used to reshape B.
*/
virtual BlockSizes block_sizes() const = 0;
// Inherited methods overridden:
const char *name() const override
{
return "NEGEMMInterleavedPrepareBWrapperKernel";
}
bool is_parallelisable() const override
{
return false; // Can't run on arbitrary windows but can be parallelised using an array of workloads
}
};
/** Equivalent to arm_gemm::GemmInterleaved's strategy::transforms::PrepareB() but using Compute Library types.
*/
template <typename strategy>
class NEGEMMInterleavedPrepareBWrapperKernelTemplate : public NEGEMMInterleavedPrepareBWrapperKernel
{
public:
/** Configure the reshape B routine.
*
* @param[in] b Input matrix B.
* @param[out] transformed_b Reshaped matrix B.
* @param[in] transpose_b Also transpose B ?
* @param[in] ci CPU information
* @param[in] params M, N, K sizes.
*/
void configure(const ITensor *b, ITensor *transformed_b, bool transpose_b, const CPUInfo &ci, const INEGEMMWrapperKernel::Params ¶ms)
{
const unsigned int multis = b->info()->tensor_shape().z();
_Nsize = b->info()->tensor_shape().x();
_Ksize = b->info()->tensor_shape().y();
_b = b;
_transformed_b = transformed_b;
_transpose_b = transpose_b;
_block_sizes = calculate_block_sizes<strategy>(ci, params.M, params.N, params.K);
auto_init_if_empty(*transformed_b->info(), b->info()->clone()->set_tensor_shape(TensorShape{ detail::get_B_pretransposed_array_size<strategy>(_Nsize, _Ksize, _block_sizes, multis) }));
Window window;
window.set(Window::DimX, Window::Dimension(0, ceil_to_multiple(_Nsize, _block_sizes.x_block), _block_sizes.x_block));
window.set(Window::DimY, Window::Dimension(0, ceil_to_multiple(_Ksize, _block_sizes.k_block), _block_sizes.k_block));
window.set(Window::DimZ, Window::Dimension(0, multis));
INEKernel::configure(window);
}
// Inherited methods overridden:
void transform(const PrepareBWorkload &wl, const ThreadInfo &info) override
{
strategy strat(info.cpu_info);
strat.transforms.PrepareB(reinterpret_cast<typename strategy::operand_type *>(_transformed_b->buffer() + wl._offset_transformed_b),
reinterpret_cast<typename strategy::operand_type *>(_b->buffer() + wl._offset_b),
_b->info()->strides_in_bytes().y() / sizeof(typename strategy::operand_type),
wl._x0, wl._xmax, wl._k0, wl._kmax, _transpose_b);
}
void create_workloads(std::vector<PrepareBWorkload> &workloads) override
{
detail::for_each_element_in_window<strategy, true>(window(), _b, _transformed_b, _Nsize, _Ksize, [&workloads](PrepareBWorkload && wl)
{
workloads.push_back(std::move(wl));
});
}
void run(const Window &window, const ThreadInfo &info) override
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(window, INEKernel::window());
detail::for_each_element_in_window<strategy, false>(window, _b, _transformed_b, _Nsize, _Ksize, [&](PrepareBWorkload && wl)
{
this->transform(wl, info);
});
}
BlockSizes block_sizes() const override
{
return _block_sizes;
}
private:
const ITensor *_b
{
nullptr
};
ITensor *_transformed_b{ nullptr };
unsigned int _Nsize{ 0 };
unsigned int _Ksize{ 0 };
bool _transpose_b{ false };
BlockSizes _block_sizes{};
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__ */
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