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setting_handler.cpp
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/*******************************************************************************
* Copyright 2022-2025 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
#include "setting_handler.hpp"
#include "graph/utils.hpp"
namespace graph {
#define DNN_GRAPH_CHECK_SETTINGS(ret, res) \
if (!(ret)) { \
BENCHDNN_PRINT(0, "error settings: [%s:%d] \n", __PRETTY_FUNCTION__, \
__LINE__); \
(res)->state = INVALID_ARGUMENTS; \
}
void assign_stride_padding_val(bool has_h, bool has_d, int64_t &w, int64_t &h,
int64_t &d, const std::vector<int64_t> &val_, int64_t default_val) {
if (has_d) { // 3d tensor, attr input is DHW
d = val_[0];
h = val_[1];
w = val_[2];
} else if (has_h) { // 2d tensor
d = default_val;
h = val_[0];
w = val_[1];
} else { // 1d tensor
d = default_val;
h = default_val;
w = val_[0];
}
};
void assign_dilation_val(bool has_h, bool has_d, int64_t &w, int64_t &h,
int64_t &d, const std::vector<int64_t> &val_, int64_t default_val) {
if (has_d) { // 3d tensor, attr input is DHW
d = val_[0] - 1;
h = val_[1] - 1;
w = val_[2] - 1;
} else if (has_h) { // 2d tensor
d = default_val;
h = val_[0] - 1;
w = val_[1] - 1;
} else { // 1d tensor
d = default_val;
h = default_val;
w = val_[0] - 1;
}
};
void assign_shape_val(int64_t &c, int64_t &w, int64_t &h, int64_t &d,
const std::vector<int64_t> &ncx_shape) {
auto ndims = ncx_shape.size();
bool has_w = ndims > 2;
bool has_h = ndims > 3;
bool has_d = ndims > 4;
// NCDHW
c = ncx_shape[1];
w = has_w ? ncx_shape[ndims - 1] : 1;
h = has_h ? ncx_shape[ndims - 2] : 1;
d = has_d ? ncx_shape[2] : 1;
};
bool get_graph_attr(const deserialized_op_t &base_op_ref,
attr_t::fpmath_mode_t &arg_fpmath_mode) {
const auto &op_kind = base_op_ref.kind_;
static const std::unordered_set<std::string> accept_fpmath_op {"MatMul",
"Convolution", "ConvolutionBackwardData",
"ConvolutionBackwardWeights", "ConvTranspose",
"ConvTransposeBackwardData", "ConvTransposeBackwardWeights"};
if (accept_fpmath_op.find(op_kind) != accept_fpmath_op.end()) {
const auto &fpmath_mode = base_op_ref.fpmath_mode_;
arg_fpmath_mode.set(str2fpmath_mode(fpmath_mode.c_str()),
str2bool(base_op_ref.fpmath_mode_apply_to_int_.c_str()));
}
return true;
}
bool get_driver_tag_by_idx(const deserialized_op_t &base_op_ref,
std::string &tag, int idx = 0, bool from_output = false) {
logical_tensor::dims strides = from_output
? base_op_ref.out_lts_[idx].stride_
: base_op_ref.in_lts_[idx].stride_;
if (base_op_ref.has_NXC_format()) {
// convert the strides to data_format = NCX
change_format_to_ncx(strides);
}
tag = strides2memory_tag(strides.size(), strides, true);
return true;
}
bool get_driver_tag(const deserialized_op_t &base_op_ref, std::string &tag,
bool from_output = false) {
return get_driver_tag_by_idx(base_op_ref, tag, 0, from_output);
}
bool get_driver_stag_and_dtag(const deserialized_op_t &base_op_ref,
std::string &stag, std::string &dtag, bool from_output = false) {
bool ret = get_driver_tag(base_op_ref, stag, from_output);
dtag = stag;
return ret;
}
bool get_driver_axis(const deserialized_op_t &base_op_ref, int &axis) {
int64_t val = 0;
base_op_ref.get_attr_s64(val, "axis");
axis = val >= 0
? val
: val + static_cast<int>(base_op_ref.in_lts_.front().shape_.size());
return true;
}
bool get_driver_bia_dt(const deserialized_op_t &base_op_ref,
dnnl_data_type_t &bia_dt, const dnnl_data_type_t dt) {
if (base_op_ref.in_lts_.size() <= 2)
bia_dt = dnnl_data_type_undef;
else if (is_integral_dt(dt)) {
bia_dt = convert_dt(base_op_ref.in_lts_[2].get_data_type());
} else {
bia_dt = dt;
}
return true;
}
bool get_prb_dims(const deserialized_op_t &base_op_ref, prb_dims_t &prb_dims) {
prb_dims.dims = base_op_ref.in_lts_.front().shape_;
prb_dims.ndims = static_cast<int>(prb_dims.dims.size());
return true;
}
// extend shape in src to match the ndims
// if the rank in tensor is less than ndims, we need to insert 1
void extend_dims(::graph::deserialized_lt <, size_t ndims) {
size_t nelem = 1;
for (size_t i = 0; i < lt.shape_.size(); i++) {
nelem *= lt.shape_[i];
}
while (lt.shape_.size() < ndims) {
lt.shape_.insert(lt.shape_.begin(), 1);
}
while (lt.stride_.size() < ndims) {
lt.stride_.insert(lt.stride_.begin(), nelem);
}
}
namespace custom {
::custom::settings_t get_setting(
const deserialized_op_t &base_op_ref, res_t *res) {
::custom::settings_t op_setting;
auto opkind = opstr2kind(base_op_ref.kind_);
switch (opkind) {
case ::graph::op::kind::GenIndex:
op_setting.alg = ::custom::alg_t::GENINDEX;
base_op_ref.get_attr_s64(op_setting.axis, "axis");
break;
case ::graph::op::kind::Select:
op_setting.alg = ::custom::alg_t::SELECT;
break;
case ::graph::op::kind::StaticTranspose:
op_setting.alg = ::custom::alg_t::TRANSPOSE;
base_op_ref.get_attr_s64_vector(op_setting.order, "order");
break;
case ::graph::op::kind::StaticReshape:
op_setting.alg = ::custom::alg_t::RESHAPE;
break;
default:
op_setting.alg = ::custom::alg_t::ALG_UNKNOWN;
assert(!"unknown alg");
res->state = res_state_t::INVALID_ARGUMENTS;
return op_setting;
}
for (size_t i = 0; i < base_op_ref.in_lts_.size(); i++) {
const auto arg = get_prim_arg_name_from_graph_op_input_offset(
opkind, static_cast<int>(i));
const auto < = base_op_ref.in_lts_[i];
auto dim = lt.shape_;
const auto dt = dnnl_f32;
auto tag = strides2memory_tag(lt.stride_.size(), lt.stride_, false);
// 0-dim means scalar input in graph, extend to 1-dim to match behavior.
if (dim.empty()) {
dim.push_back(1);
tag = "a";
}
op_setting.arg_mds_[arg] = ::std::make_tuple(tag, dim, dt);
}
for (size_t i = 0; i < base_op_ref.out_lts_.size(); i++) {
auto arg = get_prim_arg_name_from_graph_op_output_offset(
opkind, static_cast<int>(i));
const auto < = base_op_ref.out_lts_[i];
auto dim = lt.shape_;
const auto dt = dnnl_f32;
auto tag = strides2memory_tag(lt.stride_.size(), lt.stride_, false);
// 0-dim means scalar input in graph, extend to 1-dim to match behavior.
if (dim.empty()) {
dim.push_back(1);
tag = "a";
}
op_setting.arg_mds_[arg] = ::std::make_tuple(tag, dim, dt);
}
return op_setting;
}
} // namespace custom
namespace binary {
bool get_binary_prb_vdims(
const deserialized_op_t &base_op_ref, prb_vdims_t &prb_vdims) {
// since base_op_ref is a copy from the original
// it is safe to modify it
deserialized_op_t &base_op = const_cast<deserialized_op_t &>(base_op_ref);
auto &src0_dims = base_op.in_lts_[0].shape_;
auto &src1_dims = base_op.in_lts_[1].shape_;
auto &dst_dims = base_op.out_lts_[0].shape_;
const auto &ndims = dst_dims.size();
// use Add to implement BiasAdd, need to align channel dims of src1
if (base_op_ref.kind_ == "BiasAdd") {
if (ndims == 1 && src0_dims[0] != src1_dims[0] && src1_dims[0] != 1) {
return false;
}
// src0: [M,N] ---> src1:[1,1] / [M,1] / [1,N]
else if (ndims == 2) {
if (src1_dims[0] == 1 || src1_dims[0] == src0_dims[0]) {
src1_dims.insert(src1_dims.end(), 1);
} else if (src1_dims[0] == src0_dims[1]) {
src1_dims.insert(src1_dims.begin(), 1);
} else {
return false;
}
}
// src0: [N,X,C] / [N,C,X] ---> src1:[1,1..,C] / [1,C,1..]
else if (ndims > 2) {
dims_t src1_dims_tmp(ndims, 1);
// default NCX
int64_t channel_idx = 1;
if (base_op_ref.has_NXC_format()) { channel_idx = ndims - 1; }
src1_dims_tmp[channel_idx] = src0_dims[channel_idx];
src1_dims = std::move(src1_dims_tmp);
// convert NXC to NCX
if (base_op_ref.has_NXC_format()) {
change_format_to_ncx(src0_dims, src1_dims, dst_dims);
}
}
} else {
::graph::extend_dims(base_op.in_lts_[0], ndims);
::graph::extend_dims(base_op.in_lts_[1], ndims);
}
prb_vdims = prb_vdims_t({src0_dims, src1_dims});
return true;
}
bool get_binary_sdt_and_ddt(const deserialized_op_t &base_op_ref,
::binary::settings_t &op_setting) {
auto sdt0 = convert_dt(base_op_ref.in_lts_[0].get_data_type());
auto sdt1 = convert_dt(base_op_ref.in_lts_[1].get_data_type());
auto ddt = convert_dt(base_op_ref.out_lts_[0].get_data_type());
op_setting.sdt = {{sdt0, sdt1}};
op_setting.ddt.front() = ddt;
return true;
}
bool get_binary_stag_and_dtag(const deserialized_op_t &base_op_ref,
::binary::settings_t &op_setting) {
// src1, src2 and dst could have different tags.
std::string stag0, stag1, dtag;
if (!get_driver_tag_by_idx(base_op_ref, dtag, 0, true)
|| !get_driver_tag_by_idx(base_op_ref, stag1, 1, false)
|| !get_driver_tag_by_idx(base_op_ref, stag0, 0, false)) {
return false;
}
op_setting.stag = {{std::move(stag0), std::move(stag1)}};
op_setting.dtag.front() = std::move(dtag);
return true;
}
bool get_binary_alg(
const deserialized_op_t &base_op_ref, ::binary::alg_t &alg) {
static const std::unordered_map<std::string, ::binary::alg_t>
map_kind_to_alg {{"Add", ::binary::alg_t::ADD},
{"BiasAdd", ::binary::alg_t::ADD},
{"Divide", ::binary::alg_t::DIV},
{"Maximum", ::binary::alg_t::MAX},
{"Minimum", ::binary::alg_t::MIN},
{"Multiply", ::binary::alg_t::MUL},
{"Subtract", ::binary::alg_t::SUB},
{"GreaterEqual", ::binary::alg_t::GE}};
const auto &op_kind = base_op_ref.kind_;
if (map_kind_to_alg.find(op_kind) == map_kind_to_alg.end()) return false;
alg = map_kind_to_alg.at(op_kind);
return true;
}
::binary::settings_t get_setting(
const deserialized_op_t &base_op_ref, res_t *res) {
::binary::settings_t op_setting;
DNN_GRAPH_CHECK_SETTINGS(
binary::get_binary_prb_vdims(base_op_ref, op_setting.prb_vdims),
res);
DNN_GRAPH_CHECK_SETTINGS(
binary::get_binary_sdt_and_ddt(base_op_ref, op_setting), res);
DNN_GRAPH_CHECK_SETTINGS(
binary::get_binary_stag_and_dtag(base_op_ref, op_setting), res);
DNN_GRAPH_CHECK_SETTINGS(
binary::get_binary_alg(base_op_ref, op_setting.alg.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_graph_attr(base_op_ref, op_setting.fpmath_mode.front()), res);
return op_setting;
}
} // namespace binary
namespace bnorm {
bool get_bnorm_desc(const deserialized_op_t &base_op_ref, ::bnorm::desc_t &d) {
const auto &src_ncx_shape = base_op_ref.get_NCX_shape(0, true);
d.mb = src_ncx_shape[0];
d.ndims = static_cast<int>(src_ncx_shape.size());
assign_shape_val(d.ic, d.iw, d.ih, d.id, src_ncx_shape);
base_op_ref.get_attr_f32(d.eps, "epsilon");
return true;
}
bool get_bnorm_dir(const deserialized_op_t &base_op_ref, dir_t &dir) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "BatchNormForwardTraining") {
dir = dir_t::FWD_D;
} else if (op_kind == "BatchNormInference") {
dir = dir_t::FWD_I;
} else if (op_kind == "BatchNormTrainingBackward") {
if (base_op_ref.out_lts_.size() == 1) {
dir = dir_t::BWD_D;
} else if (base_op_ref.out_lts_.size() == 3) {
dir = dir_t::BWD_DW;
} else {
return false;
}
} else {
assert(!"unsupported op_kind");
return false;
}
return true;
}
bool get_bnorm_dt(const deserialized_op_t &base_op_ref, dnnl_data_type_t &dt) {
dt = convert_dt(base_op_ref.in_lts_.front().get_data_type());
return true;
}
bool get_bnorm_flag(
const deserialized_op_t &base_op_ref, ::bnorm::flags_t &flag) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "BatchNormForwardTraining") {
if (base_op_ref.in_lts_.size() == 3) {
flag = ::bnorm::NONE;
} else if (base_op_ref.in_lts_.size() == 5) {
flag = ::bnorm::USE_SCALE | ::bnorm::USE_SHIFT;
} else {
return false;
}
} else if (op_kind == "BatchNormInference") {
flag = ::bnorm::GLOB_STATS | ::bnorm::USE_SCALE | ::bnorm::USE_SHIFT;
} else if (op_kind == "BatchNormTrainingBackward") {
if (base_op_ref.out_lts_.size() == 1) {
flag = ::bnorm::GLOB_STATS;
} else if (base_op_ref.out_lts_.size() == 3) {
flag = ::bnorm::USE_SCALE | ::bnorm::USE_SHIFT;
} else {
return false;
}
} else {
assert(!"unsupported op_kind");
return false;
}
return true;
}
::bnorm::settings_t get_setting(
const deserialized_op_t &base_op_ref, res_t *res) {
::bnorm::settings_t op_setting;
DNN_GRAPH_CHECK_SETTINGS(
bnorm::get_bnorm_desc(base_op_ref, op_setting.desc), res);
DNN_GRAPH_CHECK_SETTINGS(
bnorm::get_bnorm_dir(base_op_ref, op_setting.dir.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
bnorm::get_bnorm_dt(base_op_ref, op_setting.dt.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_driver_tag(base_op_ref, op_setting.tag.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
bnorm::get_bnorm_flag(base_op_ref, op_setting.flags.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_graph_attr(base_op_ref, op_setting.fpmath_mode.front()), res);
return op_setting;
}
} // namespace bnorm
namespace concat {
bool get_concat_prb_vdims(
const deserialized_op_t &base_op_ref, prb_vdims_t &prb_vdims) {
std::vector<dims_t> vdims;
vdims.reserve(base_op_ref.in_lts_.size());
for (const auto &in : base_op_ref.in_lts_) {
vdims.push_back(in.shape_);
}
prb_vdims = prb_vdims_t(vdims);
return true;
}
bool get_concat_sdt_and_ddt(const deserialized_op_t &base_op_ref,
::concat::settings_t &op_setting) {
const auto &dt = convert_dt(base_op_ref.in_lts_.front().get_data_type());
op_setting.sdt.front() = dt;
op_setting.ddt.front() = dt;
return true;
}
bool get_concat_stag_and_dtag(const deserialized_op_t &base_op_ref,
::concat::settings_t &op_setting) {
size_t in_size = base_op_ref.in_lts_.size();
std::vector<std::string> stags(in_size);
std::string dtag;
for (int i = 0; i < (int)in_size; ++i) {
if (!get_driver_tag_by_idx(base_op_ref, stags[i], i)) { return false; }
}
if (!get_driver_tag_by_idx(base_op_ref, dtag, 0, true)) { return false; }
op_setting.stag.front() = std::move(stags);
op_setting.dtag.front() = std::move(dtag);
return true;
}
::concat::settings_t get_setting(
const deserialized_op_t &base_op_ref, res_t *res) {
::concat::settings_t op_setting;
DNN_GRAPH_CHECK_SETTINGS(
concat::get_concat_prb_vdims(base_op_ref, op_setting.prb_vdims),
res);
DNN_GRAPH_CHECK_SETTINGS(
concat::get_concat_sdt_and_ddt(base_op_ref, op_setting), res);
DNN_GRAPH_CHECK_SETTINGS(
concat::get_concat_stag_and_dtag(base_op_ref, op_setting), res);
DNN_GRAPH_CHECK_SETTINGS(
get_driver_axis(base_op_ref, op_setting.axis.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_graph_attr(base_op_ref, op_setting.fpmath_mode.front()), res);
return op_setting;
}
} // namespace concat
namespace conv {
bool get_conv_desc(const deserialized_op_t &base_op_ref, ::conv::desc_t &d) {
d.g = 1;
d.sd = d.sh = d.sw = 1;
d.pd = d.ph = d.pw = -1;
std::string data_format {}, weights_format {};
std::vector<int64_t> pads_begin {}, pads_end {}, strides {}, dilations {};
int64_t g = 1;
base_op_ref.get_attr_string(data_format, "data_format");
base_op_ref.get_attr_string(weights_format, "weights_format");
base_op_ref.get_attr_s64_vector(pads_begin, "pads_begin");
base_op_ref.get_attr_s64_vector(pads_end, "pads_end");
base_op_ref.get_attr_s64_vector(strides, "strides");
base_op_ref.get_attr_s64_vector(dilations, "dilations");
base_op_ref.get_attr_s64(g, "groups");
logical_tensor::dims src_ncx_shape {}, wei_dims {}, dst_ncx_shape {};
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "Convolution") {
src_ncx_shape = base_op_ref.get_NCX_shape(0, true);
wei_dims = base_op_ref.in_lts_[1].shape_;
dst_ncx_shape = base_op_ref.get_NCX_shape(0, false);
} else if (op_kind == "ConvolutionBackwardData") {
src_ncx_shape = base_op_ref.get_NCX_shape(0, false);
wei_dims = base_op_ref.in_lts_[1].shape_;
dst_ncx_shape = base_op_ref.get_NCX_shape(0, true);
} else if (op_kind == "ConvolutionBackwardWeights") {
src_ncx_shape = base_op_ref.get_NCX_shape(0, true);
wei_dims = base_op_ref.out_lts_[0].shape_;
dst_ncx_shape = base_op_ref.get_NCX_shape(1, true);
} else {
assert(!"unexpected op_kind");
return false;
}
d.ndims = static_cast<int>(src_ncx_shape.size());
d.mb = src_ncx_shape[0];
assign_shape_val(d.ic, d.iw, d.ih, d.id, src_ncx_shape);
assign_shape_val(d.oc, d.ow, d.oh, d.od, dst_ncx_shape);
bool has_h = d.ndims > 3;
bool has_d = d.ndims > 4;
if (weights_format == "OIX") {
// oiw, oihw, oidhw
d.kw = wei_dims[d.ndims - 1];
d.kh = has_h ? wei_dims[d.ndims - 2] : 1;
d.kd = has_d ? wei_dims[2] : 1;
} else if (weights_format == "XIO") {
// wio, hwio, dhwio
d.kw = wei_dims[d.ndims - 3];
d.kh = has_h ? wei_dims[d.ndims - 4] : 1;
d.kd = has_d ? wei_dims[0] : 1;
} else {
return FAIL;
}
assign_stride_padding_val(has_h, has_d, d.sw, d.sh, d.sd, strides, 1);
assign_dilation_val(has_h, has_d, d.dw, d.dh, d.dd, dilations, 0);
assign_stride_padding_val(has_h, has_d, d.pw, d.ph, d.pd, pads_begin, 0);
assign_stride_padding_val(
has_h, has_d, d.pw_r, d.ph_r, d.pd_r, pads_end, 0);
if (g > 1) { // has group
d.g = g;
d.has_groups = true;
}
if (d.has_groups && d.g <= 0) return false;
if (d.ic == 0 || d.oc == 0) return false;
if (d.sd <= 0 || d.sh <= 0 || d.sw <= 0) return false;
return true;
}
bool get_conv_dir(const deserialized_op_t &base_op_ref, dir_t &dir) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "Convolution") {
dir = dir_t::FWD_I;
} else if (op_kind == "ConvolutionBackwardData") {
dir = dir_t::BWD_D;
} else if (op_kind == "ConvolutionBackwardWeights") {
dir = dir_t::BWD_W;
} else {
assert(!"unexpected op_kind");
return false;
}
return true;
}
bool get_conv_dt(const deserialized_op_t &base_op_ref,
std::vector<dnnl_data_type_t> &dt) {
dnnl_data_type_t src_dt, wei_dt, dst_dt;
auto in_lt0_dt = convert_dt(base_op_ref.in_lts_[0].get_data_type());
auto in_lt1_dt = convert_dt(base_op_ref.in_lts_[1].get_data_type());
auto out_lt_dt = convert_dt(base_op_ref.out_lts_[0].get_data_type());
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "Convolution") {
src_dt = in_lt0_dt;
wei_dt = in_lt1_dt;
dst_dt = out_lt_dt;
} else if (op_kind == "ConvolutionBackwardData") {
src_dt = out_lt_dt;
wei_dt = in_lt1_dt;
dst_dt = in_lt0_dt;
} else if (op_kind == "ConvolutionBackwardWeights") {
src_dt = in_lt0_dt;
wei_dt = out_lt_dt;
dst_dt = in_lt1_dt;
} else {
assert(!"unexpected op_kind");
return false;
}
dt = {src_dt, wei_dt, dst_dt};
return true;
}
bool get_conv_wtag(const deserialized_op_t &base_op_ref, std::string &tag) {
std::string weights_format {};
if (!base_op_ref.get_attr_string(weights_format, "weights_format"))
return false;
logical_tensor::dims strides {}, shape {};
if (base_op_ref.kind_ == "ConvolutionBackwardWeights") {
strides = base_op_ref.out_lts_[0].stride_;
shape = base_op_ref.out_lts_[0].shape_;
} else {
strides = base_op_ref.in_lts_[1].stride_;
shape = base_op_ref.in_lts_[1].shape_;
}
if (weights_format == "XIO") {
// convert the strides to data_format = OIX
strides.insert(strides.begin(), strides[strides.size() - 1]);
strides.insert(strides.begin() + 1, strides[strides.size() - 2]);
strides.erase(strides.end() - 2, strides.end());
}
int64_t groups = 1;
bool has_group = base_op_ref.get_attr_s64(groups, "groups");
if (has_group && groups > 1) {
// convert the strides from w/o group to strides w/ group
dnnl::memory::dim shape_oc = weights_format == "XIO"
? shape[strides.size() - 1]
: shape[0];
dnnl::memory::dim stride_oc = strides[0];
strides.insert(strides.begin(), stride_oc * shape_oc / groups);
}
size_t ndims = strides.size();
tag = strides2memory_tag(ndims, strides, true);
return true;
}
bool get_conv_stag_and_dtag(
const deserialized_op_t &base_op_ref, ::conv::settings_t &op_setting) {
std::string stag, dtag;
if (base_op_ref.kind_ == "Convolution") {
if (!get_driver_tag_by_idx(base_op_ref, stag, 0)
|| !get_driver_tag_by_idx(base_op_ref, dtag, 0, true)) {
return false;
}
} else if (base_op_ref.kind_ == "ConvolutionBackwardData") {
if (!get_driver_tag_by_idx(base_op_ref, dtag, 0)
|| !get_driver_tag_by_idx(base_op_ref, stag, 0, true)) {
return false;
}
} else if (base_op_ref.kind_ == "ConvolutionBackwardWeights") {
if (!get_driver_tag_by_idx(base_op_ref, stag, 0)
|| !get_driver_tag_by_idx(base_op_ref, dtag, 1)) {
return false;
}
} else {
assert(!"unexpected op_kind");
return false;
}
op_setting.stag.front() = std::move(stag);
op_setting.dtag.front() = std::move(dtag);
return true;
}
::conv::settings_t get_setting(
const deserialized_op_t &base_op_ref, res_t *res) {
::conv::settings_t op_setting;
DNN_GRAPH_CHECK_SETTINGS(
conv::get_conv_desc(base_op_ref, op_setting.desc), res);
DNN_GRAPH_CHECK_SETTINGS(
conv::get_conv_dir(base_op_ref, op_setting.dir.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
conv::get_conv_dt(base_op_ref, op_setting.dt.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_driver_bia_dt(base_op_ref, op_setting.bia_dt.front(),
op_setting.dt.front()[0]),
res);
DNN_GRAPH_CHECK_SETTINGS(
conv::get_conv_stag_and_dtag(base_op_ref, op_setting), res);
DNN_GRAPH_CHECK_SETTINGS(
conv::get_conv_wtag(base_op_ref, op_setting.wtag.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_graph_attr(base_op_ref, op_setting.fpmath_mode.front()), res);
return op_setting;
}
} // namespace conv
namespace deconv {
bool get_deconv_desc(
const deserialized_op_t &base_op_ref, ::deconv::desc_t &d) {
d.g = 1;
d.sd = d.sh = d.sw = 1;
d.pd = d.ph = d.pw = -1;
std::string data_format {}, weights_format {};
std::vector<int64_t> pads_begin, pads_end, strides, dilations;
int64_t g = 1;
base_op_ref.get_attr_string(data_format, "data_format");
base_op_ref.get_attr_string(weights_format, "weights_format");
base_op_ref.get_attr_s64_vector(pads_begin, "pads_begin");
base_op_ref.get_attr_s64_vector(pads_end, "pads_end");
base_op_ref.get_attr_s64_vector(strides, "strides");
base_op_ref.get_attr_s64_vector(dilations, "dilations");
base_op_ref.get_attr_s64(g, "groups");
logical_tensor::dims src_ncx_shape {}, wei_dims {}, dst_ncx_shape {};
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "ConvTranspose") {
src_ncx_shape = base_op_ref.get_NCX_shape(0, true);
wei_dims = base_op_ref.in_lts_[1].shape_;
dst_ncx_shape = base_op_ref.get_NCX_shape(0, false);
} else if (op_kind == "ConvTransposeBackwardData") {
src_ncx_shape = base_op_ref.get_NCX_shape(0, false);
wei_dims = base_op_ref.in_lts_[1].shape_;
dst_ncx_shape = base_op_ref.get_NCX_shape(0, true);
} else if (op_kind == "ConvTransposeBackwardWeights") {
src_ncx_shape = base_op_ref.get_NCX_shape(0, true);
wei_dims = base_op_ref.out_lts_[0].shape_;
dst_ncx_shape = base_op_ref.get_NCX_shape(1, true);
} else {
assert(!"unexpected op_kind");
return false;
}
d.ndims = static_cast<int>(src_ncx_shape.size());
d.mb = src_ncx_shape[0];
assign_shape_val(d.ic, d.iw, d.ih, d.id, src_ncx_shape);
assign_shape_val(d.oc, d.ow, d.oh, d.od, dst_ncx_shape);
bool has_h = d.ndims > 3;
bool has_d = d.ndims > 4;
if (weights_format == "IOX") {
// iow, iohw, iodhw
d.kw = wei_dims[d.ndims - 1];
d.kh = has_h ? wei_dims[d.ndims - 2] : 1;
d.kd = has_d ? wei_dims[2] : 1;
} else if (weights_format == "XOI") {
// woi, hwoi, dhwoi
d.kw = wei_dims[d.ndims - 3];
d.kh = has_h ? wei_dims[d.ndims - 4] : 1;
d.kd = has_d ? wei_dims[0] : 1;
} else {
return false;
}
assign_dilation_val(has_h, has_d, d.dw, d.dh, d.dd, dilations, 0);
assign_stride_padding_val(has_h, has_d, d.sw, d.sh, d.sd, strides, 1);
assign_stride_padding_val(has_h, has_d, d.pw, d.ph, d.pd, pads_begin, 0);
assign_stride_padding_val(
has_h, has_d, d.pw_r, d.ph_r, d.pd_r, pads_end, 0);
if (g > 1) { // has group
d.g = g;
d.has_groups = true;
}
return true;
}
bool get_deconv_dir(const deserialized_op_t &base_op_ref, dir_t &dir) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "ConvTranspose") {
dir = dir_t::FWD_I;
} else if (op_kind == "ConvTransposeBackwardData") {
dir = dir_t::BWD_D;
} else if (op_kind == "ConvTransposeBackwardWeights") {
dir = dir_t::BWD_W;
} else {
assert(!"unexpected op_kind");
return false;
}
return true;
}
bool get_deconv_dt(const deserialized_op_t &base_op_ref,
std::vector<dnnl_data_type_t> &dt) {
dnnl_data_type_t src_dt, wei_dt, dst_dt;
auto in_lt0_dt = convert_dt(base_op_ref.in_lts_[0].get_data_type());
auto in_lt1_dt = convert_dt(base_op_ref.in_lts_[1].get_data_type());
auto out_lt_dt = convert_dt(base_op_ref.out_lts_[0].get_data_type());
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "ConvTranspose") {
src_dt = in_lt0_dt;
wei_dt = in_lt1_dt;
dst_dt = out_lt_dt;
} else if (op_kind == "ConvTransposeBackwardData") {
src_dt = out_lt_dt;
wei_dt = in_lt1_dt;
dst_dt = in_lt0_dt;
} else if (op_kind == "ConvTransposeBackwardWeights") {
src_dt = in_lt0_dt;
wei_dt = out_lt_dt;
dst_dt = in_lt1_dt;
} else {
assert(!"unexpected op_kind");
return false;
}
dt = {src_dt, wei_dt, dst_dt};
return true;
}
bool get_deconv_wtag(const deserialized_op_t &base_op_ref, std::string &tag) {
std::string weights_format {};
if (!base_op_ref.get_attr_string(weights_format, "weights_format"))
return false;
if (weights_format != "XOI" && weights_format != "IOX") return false;
logical_tensor::dims strides {}, shape {};
if (base_op_ref.kind_ == "ConvTransposeBackwardWeights") {
strides = base_op_ref.out_lts_[0].stride_;
shape = base_op_ref.out_lts_[0].shape_;
} else {
strides = base_op_ref.in_lts_[1].stride_;
shape = base_op_ref.in_lts_[1].shape_;
}
if (weights_format == "XOI") {
// convert the strides to weights_format = OIX
strides.insert(strides.begin(), strides[strides.size() - 2]);
strides.insert(strides.begin() + 1, strides[strides.size() - 1]);
strides.erase(strides.end() - 2, strides.end());
} else if (weights_format == "IOX") {
// convert the strides to filter_format = OIX
std::swap(strides[0], strides[1]);
}
int64_t groups = 1;
bool has_group = base_op_ref.get_attr_s64(groups, "groups");
if (has_group && groups > 1) {
// convert the strides from w/o group to strides w/ group
dnnl::memory::dim shape_ic
= weights_format == "XOI" ? shape[shape.size() - 1] : shape[0];
dnnl::memory::dim stride_ic = strides[1];
strides.insert(strides.begin(), stride_ic * shape_ic / groups);
}
const size_t ndims = strides.size();
tag = strides2memory_tag(ndims, strides, true);
return true;
}
::deconv::settings_t get_setting(
const deserialized_op_t &base_op_ref, res_t *res) {
::deconv::settings_t op_setting;
DNN_GRAPH_CHECK_SETTINGS(
deconv::get_deconv_desc(base_op_ref, op_setting.desc), res);
DNN_GRAPH_CHECK_SETTINGS(
deconv::get_deconv_dir(base_op_ref, op_setting.dir.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
deconv::get_deconv_dt(base_op_ref, op_setting.dt.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_driver_bia_dt(base_op_ref, op_setting.bia_dt.front(),
op_setting.dt.front()[0]),
res);
DNN_GRAPH_CHECK_SETTINGS(
get_driver_stag_and_dtag(base_op_ref, op_setting.stag.front(),
op_setting.dtag.front(),
base_op_ref.kind_ == "ConvTransposeBackwardData"),
res);
DNN_GRAPH_CHECK_SETTINGS(
deconv::get_deconv_wtag(base_op_ref, op_setting.wtag.front()), res);
DNN_GRAPH_CHECK_SETTINGS(
get_graph_attr(base_op_ref, op_setting.fpmath_mode.front()), res);
return op_setting;
}
} // namespace deconv
namespace eltwise {
const std::unordered_map<std::string, ::eltwise::alg_t> &
get_eltwise_kind_map() {
static const std::unordered_map<std::string, ::eltwise::alg_t> map_ {
{"Abs", ::eltwise::alg_t::ABS},
{"AbsBackward", ::eltwise::alg_t::ABS},
{"Clamp", ::eltwise::alg_t::CLIP_V2},
{"ClampBackward", ::eltwise::alg_t::CLIP_V2},
{"Elu", ::eltwise::alg_t::ELU},
{"EluBackward", ::eltwise::alg_t::ELU},
{"Exp", ::eltwise::alg_t::EXP},
{"GELU", ::eltwise::alg_t::GELU_ERF},
{"GELUBackward", ::eltwise::alg_t::GELU_ERF},
{"HardSigmoid", ::eltwise::alg_t::HARDSIGMOID},
{"HardSigmoidBackward", ::eltwise::alg_t::HARDSIGMOID},
{"HardSwish", ::eltwise::alg_t::HARDSWISH},
{"HardSwishBackward", ::eltwise::alg_t::HARDSWISH},
{"LeakyReLU", ::eltwise::alg_t::RELU},
{"Log", ::eltwise::alg_t::LOG},
{"Mish", ::eltwise::alg_t::MISH},
{"MishBackward", ::eltwise::alg_t::MISH},
{"Pow", ::eltwise::alg_t::POW},
{"Reciprocal", ::eltwise::alg_t::POW},
{"ReLU", ::eltwise::alg_t::RELU},
{"ReLUBackward", ::eltwise::alg_t::RELU},
{"Round", ::eltwise::alg_t::ROUND},
{"Log", ::eltwise::alg_t::LOG},
{"Sigmoid", ::eltwise::alg_t::LOGISTIC},
{"SigmoidBackward", ::eltwise::alg_t::LOGISTIC},
{"SoftPlus", ::eltwise::alg_t::SRELU},
{"SoftPlusBackward", ::eltwise::alg_t::SRELU},
{"Sqrt", ::eltwise::alg_t::SQRT},
{"SqrtBackward", ::eltwise::alg_t::SQRT},
{"Square", ::eltwise::alg_t::SQUARE},
{"Tanh", ::eltwise::alg_t::TANH},
{"TanhBackward", ::eltwise::alg_t::TANH},
};
return map_;
}
bool get_flag_use_dst_for_bwd_compute(const deserialized_op_t &base_op_ref) {
const auto it = base_op_ref.attrs_.find("use_dst");
if (it == base_op_ref.attrs_.end()) return false;
return it->second.bool_value_;
}
bool get_eltwise_dir(const deserialized_op_t &base_op_ref, dir_t &dir) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind.rfind("Backward") == std::string::npos) {
dir = dir_t::FWD_D;
} else {
dir = dir_t::BWD_D;
}
return true;
}
bool get_eltwise_dt(
const deserialized_op_t &base_op_ref, dnnl_data_type_t &dt) {
dt = convert_dt(base_op_ref.in_lts_.front().get_data_type());
return true;
}
bool get_eltwise_alg(
const deserialized_op_t &base_op_ref, ::eltwise::alg_t &alg) {
static const std::unordered_map<std::string, ::eltwise::alg_t>
map_kind_to_alg_dst {
{"ClampBackward", ::eltwise::alg_t::CLIP_V2_DST},
{"EluBackward", ::eltwise::alg_t::ELU_DST},
{"Exp", ::eltwise::alg_t::EXP_DST},
{"LeakyReLU", ::eltwise::alg_t::RELU_DST},
{"ReLUBackward", ::eltwise::alg_t::RELU_DST},
{"SigmoidBackward", ::eltwise::alg_t::LOGISTIC_DST},
{"SqrtBackward", ::eltwise::alg_t::SQRT_DST},
{"TanhBackward", ::eltwise::alg_t::TANH_DST}};
const auto &op_kind = base_op_ref.kind_;
if (get_flag_use_dst_for_bwd_compute(base_op_ref)) {
if (map_kind_to_alg_dst.find(op_kind) == map_kind_to_alg_dst.end())
return false;
alg = map_kind_to_alg_dst.at(op_kind);
} else {
const auto &map_kind_to_alg = get_eltwise_kind_map();
if (map_kind_to_alg.find(op_kind) == map_kind_to_alg.end())
return false;
alg = map_kind_to_alg.at(op_kind);
}
return true;
}
bool get_eltwise_alpha(const deserialized_op_t &base_op_ref, float &alpha) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "Clamp" || op_kind == "ClampBackward") {
base_op_ref.get_attr_f32(alpha, "min");
} else if (op_kind == "Elu" || op_kind == "EluBackward"
|| op_kind == "LeakyReLU" || op_kind == "HardSigmoid"
|| op_kind == "HardSigmoidBackward") {
base_op_ref.get_attr_f32(alpha, "alpha");
} else if (op_kind == "Reciprocal") {
alpha = 1; // Reciprocal is pow(-1)
} else if (op_kind == "SoftPlus" || op_kind == "SoftPlusBackward") {
// forced data type conversion due to discrepancy between setting and JSON file
base_op_ref.get_attr_f32(alpha, "beta");
} else if (op_kind == "HardSwish" || op_kind == "HardSwishBackward") {
alpha = 1.f / 6.f;
} else if (op_kind == "Pow") {
alpha = 1; // alpha is constant 1 according to graph API Pow definition
}
return true;
}
bool get_eltwise_beta(const deserialized_op_t &base_op_ref, float &beta) {
const auto &op_kind = base_op_ref.kind_;
if (op_kind == "Reciprocal") {
beta = -1; // Reciprocal is pow(-1)
} else if (op_kind == "Clamp" || op_kind == "ClampBackward") {
base_op_ref.get_attr_f32(beta, "max");
} else if (op_kind == "HardSigmoid" || op_kind == "HardSigmoidBackward"
|| op_kind == "Pow") {
base_op_ref.get_attr_f32(beta, "beta");