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/*
* Copyright (c) 2021-2023 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.
*/
/* Utilities for constructing functions which constrain which kernels are
* selected for a given depthwise problem.
*
* It is expected that this will be included in the files which list the
* available kernels. To avoid multiple definitions, an anonymous namespace is
* used.
*/
#pragma once
#include "arm_gemm.hpp"
#include "depthwise.hpp"
namespace arm_conv
{
namespace depthwise
{
namespace
{
template <class OutputStage>
using ConstraintFn = std::function<bool(const DepthwiseArgs &, const OutputStage &)>;
using GenericConstraintFn = std::function<bool(const DepthwiseArgs &, const void *)>;
GenericConstraintFn make_constraint(const GenericConstraintFn &f) __attribute__ ((unused));
GenericConstraintFn make_constraint(const GenericConstraintFn &f)
{
return f;
}
template <typename ... Fs>
GenericConstraintFn make_constraint(const GenericConstraintFn &f, Fs ... fs)
{
return [f, fs...] (const DepthwiseArgs &args, const void *os) -> bool {
return f(args, os) && make_constraint(fs...)(args, os);
};
}
template <typename OutputStage=Nothing, typename ... Fs>
ConstraintFn<OutputStage> constraint(Fs ... fs)
{
return [fs...] (const DepthwiseArgs &args, const OutputStage &os) -> bool {
return make_constraint(fs...)(args, &os);
};
}
// Some useful constraints
template <class Strategy>
bool is_supported(const DepthwiseArgs &args, const void *)
{
return ((args.kernel_rows == Strategy::kernel_rows) &&
(args.kernel_cols == Strategy::kernel_cols) &&
(args.stride_rows == Strategy::stride_rows) &&
(args.stride_cols == Strategy::stride_cols));
}
bool cpu_has_dot_product(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool cpu_has_dot_product(const DepthwiseArgs &args, const void *)
{
return args.cpu_info->has_dotprod();
}
bool cpu_has_sme(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool cpu_has_sme(const DepthwiseArgs &args, const void *)
{
return args.cpu_info->has_sme();
}
bool cpu_has_sme2(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool cpu_has_sme2(const DepthwiseArgs &args, const void *)
{
return args.cpu_info->has_sme2();
}
bool cpu_has_sve(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool cpu_has_sve(const DepthwiseArgs &args, const void *)
{
return args.cpu_info->has_sve();
}
bool cpu_has_sve2(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool cpu_has_sve2(const DepthwiseArgs &args, const void *)
{
return args.cpu_info->has_sve2();
}
bool cpu_has_fp16(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool cpu_has_fp16(const DepthwiseArgs &args, const void *)
{
return args.cpu_info->has_fp16();
}
bool has_no_channel_multiplier(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool has_no_channel_multiplier(const DepthwiseArgs &args, const void *)
{
return args.channel_multiplier == 1;
}
bool has_channel_multiplier(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool has_channel_multiplier(const DepthwiseArgs &args, const void *)
{
return args.channel_multiplier > 1;
}
// Planar kernels require a "priming" step before the main processing loop. The kernels can prime with left padding
// or input data, but not right padding - which could be needed in some extreme cases such as a 5x5 kernel, width 1
// padding 2. These are rare enough and can be handled with other kernels anyway, so filter them out with this.
bool no_prime_right_pad(const DepthwiseArgs &args, const void *) __attribute__ ((unused));
bool no_prime_right_pad(const DepthwiseArgs &args, const void *)
{
return (args.input_cols + args.padding.left) >= (args.kernel_cols - 1);
}
bool qp_has_no_left_shift(const DepthwiseArgs &args, const void *_qp) __attribute__ ((unused));
bool qp_has_no_left_shift(const DepthwiseArgs &, const void *_qp)
{
const auto qp = static_cast<const arm_gemm::Requantize32 *>(_qp);
return qp->per_channel_requant ?
(qp->per_channel_left_shifts == nullptr) :
(qp->per_layer_left_shift == 0);
}
bool qp_zero_a_offset(const DepthwiseArgs &args, const void *_qp) __attribute__ ((unused));
bool qp_zero_a_offset(const DepthwiseArgs &, const void *_qp)
{
const auto qp = static_cast<const arm_gemm::Requantize32 *>(_qp);
return qp->a_offset == 0;
}
template <typename T> bool qp_skip_clamp(const DepthwiseArgs &args, const void *_qp) __attribute__ ((unused));
template <typename T> bool qp_skip_clamp(const DepthwiseArgs &, const void *_qp)
{
const auto qp = static_cast<const arm_gemm::Requantize32 *>(_qp);
return (qp->minval == std::numeric_limits<T>::min() &&
qp->maxval == std::numeric_limits<T>::max());
}
} // namespace
} // namespace depthwise
} // namespace arm_conv
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