#ifndef GRAPH_BACKEND_DNNL_KERNELS_CONV_HPP
#define GRAPH_BACKEND_DNNL_KERNELS_CONV_HPP
#include "graph/backend/dnnl/kernels/conv_base.hpp"
namespace dnnl {
namespace impl {
namespace graph {
namespace dnnl_impl {
template <bool quantized>
struct conv_fwd_t : public conv_base_t {
public:
status_t compile_impl(const dnnl_partition_impl_t *part,
const engine_t *g_engine,
const std::vector<logical_tensor_t> &inputs,
const std::vector<logical_tensor_t> &outputs) override;
status_t prepare_inplace_pairs_impl() override;
DEF_KERNEL_METHOD_STR(conv_fwd_t)
};
using float_conv_fwd = conv_fwd_t< false>;
using quantized_conv = conv_fwd_t< true>;
#if BUILD_TRAINING
struct conv_bwd_data_t : public conv_base_t {
public:
status_t compile_impl(const dnnl_partition_impl_t *part,
const engine_t *g_engine,
const std::vector<logical_tensor_t> &inputs,
const std::vector<logical_tensor_t> &outputs) override;
DEF_KERNEL_METHOD_STR(conv_bwd_data_t)
};
struct conv_bwd_weights_t : public conv_base_t {
public:
status_t compile_impl(const dnnl_partition_impl_t *part,
const engine_t *g_engine,
const std::vector<logical_tensor_t> &inputs,
const std::vector<logical_tensor_t> &outputs) override;
DEF_KERNEL_METHOD_STR(conv_bwd_weights_t)
};
#endif
} } } }
#endif