#pragma once
#include "src/aarch64/conv_bias/opr_impl.h"
#include "src/common/opr_delegate.h"
#include "src/fallback/conv_bias/opr_impl.h"
namespace megdnn {
namespace aarch64 {
using FallbackConvBiasImpl = fallback::ConvBiasImpl;
class ConvBiasImpl::AlgoS8MatrixMul final : public AlgoBase {
static WorkspaceBundle get_bundle(const NCBKernSizeParam& param);
static void kimpl(const NCBKernParam& param, const NCBKernIndex& ncb_index);
public:
AlgoAttribute attribute() const override { return AlgoAttribute::REPRODUCIBLE; }
const char* name() const override { return "S8MATMUL"; }
bool usable(
const NCBKernSizeParam& param,
AlgoSelectionStrategy algo_selection_strategy) const override;
size_t get_workspace(const NCBKernSizeParam& param) const override {
return get_bundle(param).total_size_in_bytes();
}
SmallVector<NCBKern> dispatch_kerns(const NCBKernSizeParam& param) const override {
size_t group = param.filter_meta.group;
return {{kimpl, {group, 1_z, 1_z}}};
}
bool is_preferred(const NCBKernSizeParam& param) const override {
static CpuOprDelegationStorage<1> storage;
auto conv_bias_opr = storage.get<ConvBias, 0>();
return static_cast<ConvBiasImpl*>(conv_bias_opr)
->is_matmul_quantized_prefer(param);
}
ConvAlgoTypePack get_algo_type() const override {
return {AlgoDataType::QINT8X8X32, AlgoCategory::IM2COL};
}
MEGDNN_DECL_ALGO_TYPE(AARCH64_MATMUL_S8)
};
} }