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/*
* Copyright (c) 2021 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.
*/
#pragma once
#include "arm_gemm.hpp"
#include "common.hpp"
namespace arm_conv
{
namespace depthwise
{
using arm_gemm::Nothing;
enum class DepthwiseMethod
{
DEFAULT,
DEPTHFIRST,
PLANAR,
};
struct KernelDescription
{
DepthwiseMethod method = DepthwiseMethod::DEFAULT;
std::string name = "";
bool is_default = false;
uint64_t cycle_estimate = 0;
KernelDescription(
DepthwiseMethod method,
std::string name,
bool is_default,
uint64_t cycle_estimate)
: method(method), name(name), is_default(is_default), cycle_estimate(cycle_estimate)
{
}
KernelDescription() noexcept {};
};
class IDepthwiseCommon
{
public:
virtual ~IDepthwiseCommon() = default;
// Determine the amount of storage space required for the rearranged weights
// and bias.
virtual size_t get_storage_size(void) const = 0;
// Rearrange the weights and biases into a storage buffer.
// Accepts a pointer to a buffer into which to store the packed parameters, a
// pointer the bias vector (which may be nullptr in the case of no bias) and
// a pointer to the array of weights (stored in HWIO order).
virtual void pack_parameters(
void *buffer,
const void *biases,
const void *weights,
size_t ld_weight_col = 0,
size_t ld_weight_row = 0) = 0;
// Determine the amount of working space required
virtual size_t get_working_size(unsigned int n_threads, unsigned int n_input_channels) const = 0;
// Execute the convolution over the specified area of memory.
virtual void execute(
const void *input, // Pointer to input tensor
const void *parameters, // Packed parameters buffer
void *output,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const = 0;
virtual void execute(
const void *input,
size_t ld_input_col,
size_t ld_input_row,
size_t ld_input_batch,
const void *parameters,
void *output,
size_t ld_output_col,
size_t ld_output_row,
size_t ld_output_batch,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const = 0;
virtual void execute(
unsigned int batches,
unsigned int input_height,
unsigned int input_width,
unsigned int channels,
const PaddingValues &,
const void *input,
size_t ld_input_col,
size_t ld_input_row,
size_t ld_input_batch,
const void *parameters,
unsigned int output_height,
unsigned int output_width,
void *output,
size_t ld_output_col,
size_t ld_output_row,
size_t ld_output_batch,
void *working_space,
unsigned int thread_id,
unsigned int n_threads) const = 0;
};
} // namespace depthwise
} // namespace arm_conv
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