#pragma once
#include "megdnn/thin/small_vector.h"
#include "src/common/utils.h"
#include "src/fallback/conv_bias/opr_impl.h"
#include "src/fallback/matrix_mul/opr_impl.h"
namespace megdnn {
namespace fallback {
class ConvBiasImpl::AlgoConv1x1 final : public AlgoBase {
WorkspaceBundle get_bundle_according_packmode(const NCBKernSizeParam& param) const;
SmallVector<NCBKern> get_kerns_according_packmode(
const NCBKernSizeParam& param, bool weight_preprocess) const;
public:
AlgoConv1x1(MatrixMulImpl::AlgoBase* matmul_algo, size_t oc_block_size)
: m_matmul_algo(matmul_algo), m_oc_block_size(oc_block_size) {}
AlgoAttribute attribute() const override { return m_matmul_algo->attribute(); }
const char* name() const override {
if (m_name.empty()) {
m_name = ssprintf("CONV1x1:%s:%zu", m_matmul_algo->name(), m_oc_block_size);
}
return m_name.c_str();
}
bool usable(
const NCBKernSizeParam& param,
AlgoSelectionStrategy algo_selection_strategy) const override;
size_t get_workspace(const NCBKernSizeParam& param) const override;
SmallVector<NCBKern> dispatch_kerns(const NCBKernSizeParam& param) const override;
bool is_preferred(const NCBKernSizeParam&) const override;
SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
const NCBKernSizeParam& param) const override;
size_t get_preprocess_workspace(const NCBKernSizeParam& ) const override {
return 0;
}
SmallVector<NCBKern> dispatch_preprocess_kerns(
const NCBKernSizeParam& param) const override;
ConvAlgoTypePack get_algo_type() const override {
return {m_matmul_algo->matmul_description().algo_type.data_type,
AlgoCategory::IM2COL};
}
MEGDNN_DECL_ALGO_TYPE(FB_CONV1x1)
std::string param() const override {
std::string ret;
serialize_write_pod(m_oc_block_size, ret);
return ret;
}
protected:
size_t get_oc_tile_size_heuristic(const NCBKernSizeParam& param) const;
private:
MatrixMulImpl::AlgoBase* m_matmul_algo;
mutable std::string m_name;
const size_t m_oc_block_size = 0;
};
} }