mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-29 20:14:01 +00:00
Include layout transformation ops in extended minimal build and above. (#11355)
This commit is contained in:
Edward Chen
GitHub
parent
88ff48910d
commit
20ee399be4
6 changed files with 170 additions and 59 deletions
|
|
@ -13,9 +13,11 @@ std::optional<HashValue> GetHashValueFromStaticKernelHashMap(const std::string&
|
|||
// Since layout transformation can happen in an extended build, if these nodes are not picked up and compiled by
|
||||
// NNAPI or other compiling EPs then we need a way to get the hashes for these nodes. Since the infrastructure
|
||||
// as well as op_schema required to generate these hashes is not available in an extended minimal build,
|
||||
// we maintain a static map of nodes to hash value. This hash value can then be used to retireive the
|
||||
// we maintain a static map of nodes to hash value. This hash value can then be used to retrieve the
|
||||
// kernel for the given op.
|
||||
static std::unordered_map<std::string, HashValue> static_kernel_hashes{
|
||||
static const std::unordered_map<std::string, HashValue> static_kernel_hashes{
|
||||
// Note: these region_begin/end markers are used by tools/ci_build/reduce_op_kernels.py
|
||||
// @@region_begin(layout_transformation_required_kernels)@@
|
||||
{"Transpose_1", 4324835766923221184ULL},
|
||||
{"Transpose_13", 17267477159887372848ULL},
|
||||
{"Squeeze_1", 12889825108950034784ULL},
|
||||
|
|
@ -31,6 +33,7 @@ std::optional<HashValue> GetHashValueFromStaticKernelHashMap(const std::string&
|
|||
{"Identity_13", 16879814636194901248ULL},
|
||||
{"Identity_14", 16515685968327103576ULL},
|
||||
{"Identity_16", 17661628575887109792ULL},
|
||||
// @@region_end(layout_transformation_required_kernels)@@
|
||||
};
|
||||
|
||||
auto key = op_type + "_" + std::to_string(since_version);
|
||||
|
|
@ -71,24 +74,24 @@ void UpdateHashForBackwardsCompatibility(HashValue& hash) {
|
|||
// onnxruntime/test/providers/kernel_def_hash_test.cc regarding how/when hashes might change and the best way to
|
||||
// address that.
|
||||
static const std::unordered_map<HashValue, HashValue> hashes{
|
||||
// old new domain, operator, opset[, type]
|
||||
{2832535737534577496ULL, 16708009824840936392ULL}, // kOnnxDomain, Dropout, 7
|
||||
{12198479371038564912ULL, 1718418059112844640ULL}, // kOnnxDomain, Scan, 9
|
||||
{2560955351529676608ULL, 3668627007850399040ULL}, // kOnnxDomain, Scan, 11
|
||||
{10232409728231027688ULL, 5212043150202938416ULL}, // kOnnxDomain, Not, 1
|
||||
{11912523891622051440ULL, 10225383741733918632ULL}, // kOnnxDomain, RoiAlign, 10, float
|
||||
{18084231515768318048ULL, 17022700455473327752ULL}, // kOnnxDomain, RoiAlign, 10, double
|
||||
{14033689580222898712ULL, 634727773751317256ULL}, // kOnnxDomain, GatherND, 11
|
||||
{646512416908411600ULL, 3064028185911332496ULL}, // kOnnxDomain, GatherND, 12
|
||||
{15019893097608892000ULL, 11311962292460032936ULL}, // kOnnxDomain, GatherND, 13
|
||||
{14259324427750852648ULL, 7767393334034626736ULL}, // kOnnxDomain, StringNormalizer, 10
|
||||
// contrib ops
|
||||
{7642430665819070720ULL, 8620498355864235632ULL}, // kMSDomain, CropAndResize, 1
|
||||
{15019666093341768288ULL, 11924582339825775592ULL}, // kMSDomain, GridSample, 1
|
||||
{8466416990072218056ULL, 18418354579469131656ULL}, // kOnnxDomain, LayerNormalization, 1, float
|
||||
{4058615579523172864ULL, 4827261308628792072ULL}, // kOnnxDomain, LayerNormalization, 1, double
|
||||
{16349480652468900704ULL, 4809288790945391544ULL}, // kOnnxDomain, SimplifiedLayerNormalization, 1, float
|
||||
{418129161279605176ULL, 13556035637124174064ULL}}; // kOnnxDomain, SimplifiedLayerNormalization, 1, double
|
||||
// old new domain, operator, opset[, type]
|
||||
{2832535737534577496ULL, 16708009824840936392ULL}, // kOnnxDomain, Dropout, 7
|
||||
{12198479371038564912ULL, 1718418059112844640ULL}, // kOnnxDomain, Scan, 9
|
||||
{2560955351529676608ULL, 3668627007850399040ULL}, // kOnnxDomain, Scan, 11
|
||||
{10232409728231027688ULL, 5212043150202938416ULL}, // kOnnxDomain, Not, 1
|
||||
{11912523891622051440ULL, 10225383741733918632ULL}, // kOnnxDomain, RoiAlign, 10, float
|
||||
{18084231515768318048ULL, 17022700455473327752ULL}, // kOnnxDomain, RoiAlign, 10, double
|
||||
{14033689580222898712ULL, 634727773751317256ULL}, // kOnnxDomain, GatherND, 11
|
||||
{646512416908411600ULL, 3064028185911332496ULL}, // kOnnxDomain, GatherND, 12
|
||||
{15019893097608892000ULL, 11311962292460032936ULL}, // kOnnxDomain, GatherND, 13
|
||||
{14259324427750852648ULL, 7767393334034626736ULL}, // kOnnxDomain, StringNormalizer, 10
|
||||
// contrib ops
|
||||
{7642430665819070720ULL, 8620498355864235632ULL}, // kMSDomain, CropAndResize, 1
|
||||
{15019666093341768288ULL, 11924582339825775592ULL}, // kMSDomain, GridSample, 1
|
||||
{8466416990072218056ULL, 18418354579469131656ULL}, // kOnnxDomain, LayerNormalization, 1, float
|
||||
{4058615579523172864ULL, 4827261308628792072ULL}, // kOnnxDomain, LayerNormalization, 1, double
|
||||
{16349480652468900704ULL, 4809288790945391544ULL}, // kOnnxDomain, SimplifiedLayerNormalization, 1, float
|
||||
{418129161279605176ULL, 13556035637124174064ULL}}; // kOnnxDomain, SimplifiedLayerNormalization, 1, double
|
||||
|
||||
auto iter = hashes.find(hash);
|
||||
if (iter != hashes.cend()) {
|
||||
|
|
|
|||
|
|
@ -4,17 +4,26 @@
|
|||
//https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gather
|
||||
#include "core/providers/cpu/tensor/gather.h"
|
||||
#include "core/common/common.h"
|
||||
#include "core/platform/threadpool.h"
|
||||
#include "core/framework/op_kernel_type_control_utils.h"
|
||||
#include "core/platform/threadpool.h"
|
||||
#include "core/providers/op_kernel_type_control.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
|
||||
using DefaultIndexTypes = TypeList<int32_t, int64_t>;
|
||||
|
||||
namespace op_kernel_type_control {
|
||||
ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPES_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, Gather, Input, 1, int32_t, int64_t);
|
||||
ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPE_LIST_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, Gather, Input, 1, DefaultIndexTypes);
|
||||
|
||||
#if !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD)
|
||||
// enable all types for layout transformation
|
||||
ORT_SPECIFY_OP_KERNEL_ARG_REQUIRED_TYPE_LIST_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, Gather, Input, 1, DefaultIndexTypes);
|
||||
#else
|
||||
ORT_SPECIFY_OP_KERNEL_ARG_REQUIRED_TYPES_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, Gather, Input, 1, int32_t, int64_t);
|
||||
#endif
|
||||
} // namespace op_kernel_type_control
|
||||
|
||||
using IndexTypes = ORT_OP_KERNEL_ARG_DEFAULT_TYPE_LIST_ALL_OPSETS(kCpuExecutionProvider, kOnnxDomain,
|
||||
|
|
|
|||
|
|
@ -12,11 +12,22 @@
|
|||
|
||||
namespace onnxruntime {
|
||||
|
||||
namespace {
|
||||
using DefaultDataTypes = element_type_lists::All;
|
||||
} // namespace
|
||||
|
||||
namespace op_kernel_type_control {
|
||||
// we're using one set of types for all opsets
|
||||
ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPE_LIST_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, Transpose, Input, 0,
|
||||
element_type_lists::All);
|
||||
DefaultDataTypes);
|
||||
|
||||
#if !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD)
|
||||
// enable all types for layout transformation
|
||||
ORT_SPECIFY_OP_KERNEL_ARG_REQUIRED_TYPE_LIST_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, Transpose, Input, 0,
|
||||
DefaultDataTypes);
|
||||
#endif
|
||||
} // namespace op_kernel_type_control
|
||||
|
||||
namespace {
|
||||
|
|
@ -41,15 +52,15 @@ struct MultiIndex {
|
|||
std::vector<int64_t> stride;
|
||||
|
||||
/* There is one MultiIndex instance per axis in the tensor.
|
||||
* The array keeps track of the position of a pointer walking through the data.
|
||||
* Any function using it creates an array of MultiIndex
|
||||
* then calls function IncrementIndexAndComputeOffsetSetup
|
||||
* to initialize the array. This constructor does not initialize
|
||||
* anything because it would be overwritten by function
|
||||
* IncrementIndexAndComputeOffsetSetup. This one calls method Init.
|
||||
* Function IncrementIndexAndComputeOffset is called to increment
|
||||
* the array of MultiIndex to move to the next data in the tensor.
|
||||
*/
|
||||
* The array keeps track of the position of a pointer walking through the data.
|
||||
* Any function using it creates an array of MultiIndex
|
||||
* then calls function IncrementIndexAndComputeOffsetSetup
|
||||
* to initialize the array. This constructor does not initialize
|
||||
* anything because it would be overwritten by function
|
||||
* IncrementIndexAndComputeOffsetSetup. This one calls method Init.
|
||||
* Function IncrementIndexAndComputeOffset is called to increment
|
||||
* the array of MultiIndex to move to the next data in the tensor.
|
||||
*/
|
||||
MultiIndex() : index(), upper_bound(), stride() { n_axes = 0; }
|
||||
|
||||
void Init(size_t num_axes) {
|
||||
|
|
@ -67,10 +78,10 @@ struct MultiIndex {
|
|||
};
|
||||
|
||||
/* This function initializes an array of MultiIndex of size num_axes (one instance per axis).
|
||||
* target_dims is the shape of the transposed tensor, stride is linked to the tensor to
|
||||
* be transposed, if source_dims is the shape, stride[i] = source_dims[i+1] * source_dims[i+2] * ... * 1.
|
||||
* element_size is the size of the tensor element (sizeof(float), sizeof(double)).
|
||||
*/
|
||||
* target_dims is the shape of the transposed tensor, stride is linked to the tensor to
|
||||
* be transposed, if source_dims is the shape, stride[i] = source_dims[i+1] * source_dims[i+2] * ... * 1.
|
||||
* element_size is the size of the tensor element (sizeof(float), sizeof(double)).
|
||||
*/
|
||||
static void IncrementIndexAndComputeOffsetSetup(MultiIndex& mindex, size_t num_axes, gsl::span<const int64_t> target_dims,
|
||||
const gsl::span<const size_t>& stride, size_t element_size) {
|
||||
mindex.Init(num_axes);
|
||||
|
|
@ -86,13 +97,13 @@ static void IncrementIndexAndComputeOffsetSetup(MultiIndex& mindex, size_t num_a
|
|||
}
|
||||
|
||||
/* This function increments an array of MultiIndex initialized by function IncrementIndexAndComputeOffsetSetup.
|
||||
* It increments the last dimension, checks if it stays within boundary. If it stays in, it returns,
|
||||
* otherwise, it reset the dimension to zero and increments the previous one.
|
||||
* While doing that, every modification brought to the array of indices is applied on the
|
||||
* pointer local_source. It avoids computing again local_source from the source tensor.
|
||||
* At every time, the following condition is verified:
|
||||
* local_source = source + (sum_i mindex[i].index * mindex[i].stride
|
||||
*/
|
||||
* It increments the last dimension, checks if it stays within boundary. If it stays in, it returns,
|
||||
* otherwise, it reset the dimension to zero and increments the previous one.
|
||||
* While doing that, every modification brought to the array of indices is applied on the
|
||||
* pointer local_source. It avoids computing again local_source from the source tensor.
|
||||
* At every time, the following condition is verified:
|
||||
* local_source = source + (sum_i mindex[i].index * mindex[i].stride
|
||||
*/
|
||||
template <typename T>
|
||||
static inline void IncrementIndexAndComputeOffset(MultiIndex& mindex, const T*& local_source) {
|
||||
// Increment the last dimension.
|
||||
|
|
|
|||
|
|
@ -7,8 +7,3 @@ ai.onnx;8;MaxPool{"inputs": {"0": ["float"]}},Sum{"inputs": {"0": ["float"]}}
|
|||
ai.onnx;9;Cast{"inputs": {"0": ["float"]}, "outputs": {"0": ["bool"]}}
|
||||
ai.onnx;11;ArgMax{"inputs": {"0": ["float"]}},If,Loop
|
||||
ai.onnx.ml;1;ArrayFeatureExtractor,LinearClassifier,Normalizer,ZipMap
|
||||
|
||||
# Note: The lines below were added manually.
|
||||
# TODO find a way to avoid manual modification of this file
|
||||
# also include Transpose added by layout transformation
|
||||
ai.onnx;1;Transpose
|
||||
|
|
|
|||
|
|
@ -2411,12 +2411,14 @@ def main():
|
|||
if is_reduced_ops_build(args):
|
||||
from reduce_op_kernels import reduce_ops
|
||||
|
||||
is_extended_minimal_build_or_higher = args.minimal_build is None or "extended" in args.minimal_build
|
||||
for config in configs:
|
||||
reduce_ops(
|
||||
config_path=args.include_ops_by_config,
|
||||
build_dir=get_config_build_dir(build_dir, config),
|
||||
enable_type_reduction=args.enable_reduced_operator_type_support,
|
||||
use_cuda=args.use_cuda,
|
||||
is_extended_minimal_build_or_higher=is_extended_minimal_build_or_higher,
|
||||
)
|
||||
|
||||
cmake_extra_args = []
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@
|
|||
# Licensed under the MIT License.
|
||||
|
||||
import argparse
|
||||
import io
|
||||
import re
|
||||
import shutil
|
||||
import sys
|
||||
import typing
|
||||
|
|
@ -25,6 +27,71 @@ from util.ort_format_model.operator_type_usage_processors import OpTypeImplFilte
|
|||
log = get_logger("reduce_op_kernels")
|
||||
|
||||
|
||||
def _adapt_filters_for_extended_minimal_build(
|
||||
base_required_ops: typing.Optional[dict], base_op_type_impl_filter: typing.Optional[OpTypeImplFilterInterface]
|
||||
):
|
||||
"""
|
||||
Adapts the values returned by parse_config() for an extended minimal build or higher.
|
||||
In particular:
|
||||
- Includes ONNX ops needed by layout transformation
|
||||
"""
|
||||
# layout transformation requires certain ONNX ops to be available
|
||||
layout_transformation_required_ops = dict() # op name -> set of opset versions
|
||||
layout_transformation_required_ops_file = ORT_ROOT / "onnxruntime/core/framework/kernel_def_hash_helpers.cc"
|
||||
with open(layout_transformation_required_ops_file, mode="r") as f:
|
||||
region_boundary_pattern = re.compile(r"@@region_(begin|end)\(layout_transformation_required_kernels\)@@")
|
||||
op_to_hash_pattern = re.compile(r'\{"(\w+)_(\d+)",\s+\w+\},')
|
||||
in_region = False
|
||||
for line in f:
|
||||
region_boundary_match = region_boundary_pattern.search(line)
|
||||
if region_boundary_match:
|
||||
in_region = region_boundary_match.group(1) == "begin"
|
||||
continue
|
||||
|
||||
if not in_region:
|
||||
continue
|
||||
|
||||
op_to_hash_match = op_to_hash_pattern.search(line)
|
||||
if op_to_hash_match:
|
||||
op_name, opset = op_to_hash_match.group(1, 2)
|
||||
layout_transformation_required_ops.setdefault(op_name, set()).add(int(opset))
|
||||
|
||||
adapted_required_ops = None
|
||||
if base_required_ops is not None:
|
||||
adapted_required_ops = base_required_ops.copy()
|
||||
required_onnx_ops = adapted_required_ops.setdefault("ai.onnx", dict())
|
||||
for op_type, opsets in layout_transformation_required_ops.items():
|
||||
for opset in opsets:
|
||||
required_onnx_opset_ops = required_onnx_ops.setdefault(opset, set())
|
||||
required_onnx_opset_ops.add(op_type)
|
||||
|
||||
adapted_op_type_impl_filter = None
|
||||
if base_op_type_impl_filter is not None:
|
||||
|
||||
class _AdaptedFilter(OpTypeImplFilterInterface):
|
||||
def __init__(self, filter_to_adapt: OpTypeImplFilterInterface, required_optypes: typing.Set[str]):
|
||||
self.filter_to_adapt = filter_to_adapt
|
||||
self.required_optypes = required_optypes
|
||||
|
||||
def is_typed_registration_needed(self, domain: str, optype: str, type_registration_str: str):
|
||||
# Always require registration for ONNX ops in self.required_optypes.
|
||||
if domain == "ai.onnx" and optype in self.required_optypes:
|
||||
return True
|
||||
return self.filter_to_adapt.is_typed_registration_needed(domain, optype, type_registration_str)
|
||||
|
||||
def get_cpp_entries(self):
|
||||
# The required types for ops in self.required_optypes must be specified in the C++ implementation.
|
||||
# Doing that also accounts for globally allowed types.
|
||||
# We don't need to do anything special with the allowed type overrides here.
|
||||
return self.filter_to_adapt.get_cpp_entries()
|
||||
|
||||
adapted_op_type_impl_filter = _AdaptedFilter(
|
||||
base_op_type_impl_filter, set(layout_transformation_required_ops.keys())
|
||||
)
|
||||
|
||||
return (adapted_required_ops, adapted_op_type_impl_filter)
|
||||
|
||||
|
||||
class _ExcludingRegistrationProcessor(op_registration_utils.RegistrationProcessor):
|
||||
"""Registration processor that excludes registrations and writes the result to an output file."""
|
||||
|
||||
|
|
@ -32,7 +99,7 @@ class _ExcludingRegistrationProcessor(op_registration_utils.RegistrationProcesso
|
|||
self,
|
||||
required_ops: typing.Optional[dict],
|
||||
op_type_impl_filter: typing.Optional[OpTypeImplFilterInterface],
|
||||
output_file: str,
|
||||
output_file: io.TextIOWrapper,
|
||||
):
|
||||
self._required_ops = required_ops
|
||||
self._op_type_impl_filter = op_type_impl_filter
|
||||
|
|
@ -40,7 +107,7 @@ class _ExcludingRegistrationProcessor(op_registration_utils.RegistrationProcesso
|
|||
|
||||
def _is_op_required(
|
||||
self, domain: str, operator: str, start_version: int, end_version: typing.Optional[int]
|
||||
) -> typing.Tuple[bool, str]:
|
||||
) -> bool:
|
||||
"""See if an op is required."""
|
||||
if self._required_ops is None:
|
||||
return True
|
||||
|
|
@ -107,14 +174,20 @@ class _ExcludingRegistrationProcessor(op_registration_utils.RegistrationProcesso
|
|||
return True
|
||||
|
||||
|
||||
def _get_op_reduction_file_path(ort_root: Path, build_dir: Path, original_path: typing.Optional[Path] = None):
|
||||
def _get_op_reduction_root(build_dir: Path):
|
||||
"""
|
||||
Return the op reduction file path corresponding to `original_path` or the op reduction file root if unspecified.
|
||||
Op reduction files are in a subdirectory of `build_dir` but otherwise share the same components of `original_path`
|
||||
Return the op reduction root directory which is a subdirectory of `build_dir`.
|
||||
"""
|
||||
return Path(build_dir, OP_REDUCTION_DIR)
|
||||
|
||||
|
||||
def _get_op_reduction_file_path(ort_root: Path, build_dir: Path, original_path: Path):
|
||||
"""
|
||||
Return the op reduction file path corresponding to `original_path`.
|
||||
Op reduction files are in the op reduction root but otherwise share the same components of `original_path`
|
||||
relative to `ort_root`.
|
||||
"""
|
||||
op_reduction_root = Path(build_dir, OP_REDUCTION_DIR)
|
||||
return (op_reduction_root / original_path.relative_to(ort_root)) if original_path is not None else op_reduction_root
|
||||
return _get_op_reduction_root(build_dir) / original_path.relative_to(ort_root)
|
||||
|
||||
|
||||
def _generate_provider_registrations(
|
||||
|
|
@ -195,21 +268,31 @@ def _generate_type_control_overrides(ort_root: Path, build_dir: Path, cpp_lines:
|
|||
raise RuntimeError("Insertion point was not found in {}".format(target))
|
||||
|
||||
|
||||
def reduce_ops(config_path: str, build_dir: str, enable_type_reduction: bool = False, use_cuda: bool = True):
|
||||
def reduce_ops(
|
||||
config_path: str,
|
||||
build_dir: str,
|
||||
enable_type_reduction: bool,
|
||||
use_cuda: bool,
|
||||
is_extended_minimal_build_or_higher: bool,
|
||||
):
|
||||
"""
|
||||
Reduce op kernel implementations.
|
||||
:param config_path: Path to configuration file that specifies the ops to include
|
||||
:param build_dir: Path to the build directory. The op reduction files will be generated under the build directory.
|
||||
:param enable_type_reduction: Whether per operator type reduction is enabled
|
||||
:param use_cuda: Whether to reduce op kernels for the CUDA provider
|
||||
:param is_extended_minimal_build_or_higher: Whether this build has at least the features of an extended minimal
|
||||
build enabled.
|
||||
"""
|
||||
build_dir = Path(build_dir).resolve()
|
||||
build_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
required_ops, op_type_impl_filter = parse_config(config_path, enable_type_reduction)
|
||||
if is_extended_minimal_build_or_higher:
|
||||
required_ops, op_type_impl_filter = _adapt_filters_for_extended_minimal_build(required_ops, op_type_impl_filter)
|
||||
|
||||
# delete any existing generated files first
|
||||
op_reduction_root = _get_op_reduction_file_path(ORT_ROOT, build_dir)
|
||||
op_reduction_root = _get_op_reduction_root(build_dir)
|
||||
if op_reduction_root.is_dir():
|
||||
log.info(f"Deleting existing op reduction file root directory: {op_reduction_root}")
|
||||
shutil.rmtree(op_reduction_root)
|
||||
|
|
@ -231,7 +314,8 @@ if __name__ == "__main__":
|
|||
type=str,
|
||||
help="Path to configuration file. "
|
||||
"Create with <ORT root>/tools/python/create_reduced_build_config.py and edit if needed. "
|
||||
"See /docs/ONNX_Runtime_Format_Model_Usage.md for more information.",
|
||||
"See https://onnxruntime.ai/docs/reference/reduced-operator-config-file.html for more "
|
||||
"information.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
|
|
@ -241,6 +325,12 @@ if __name__ == "__main__":
|
|||
help="Path to the build directory. " "The op reduction files will be generated under the build directory.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--is_extended_minimal_build_or_higher",
|
||||
action="store_true",
|
||||
help="Whether this build has at least the features of an extended minimal build enabled.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--enable_type_reduction", action="store_true", help="Whether per operator type reduction is enabled."
|
||||
)
|
||||
|
|
@ -254,4 +344,5 @@ if __name__ == "__main__":
|
|||
build_dir=args.cmake_build_dir,
|
||||
enable_type_reduction=args.enable_type_reduction,
|
||||
use_cuda=args.use_cuda,
|
||||
is_extended_minimal_build_or_higher=args.is_extended_minimal_build_or_higher,
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in a new issue