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Backmerging with Msft Commits #654

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2f8d79e
Remove build-nuget from dml-vs-2022.yml (#24372)
snnn Apr 10, 2025
f44f1a9
Make test `CApiTest.RequestLoadCancellation` deterministic (#24348)
yuslepukhin Apr 10, 2025
d58ff6b
[web] allow NPM tests to run nodejs binding for webgpu (#24370)
fs-eire Apr 11, 2025
7a03764
MlasTranspose multi-threads support. (#24261)
msy-kato Apr 11, 2025
6c9711b
Merge branch 'master' into sync_msft_11_4_25
jatinwadhwa921 Apr 11, 2025
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2 changes: 1 addition & 1 deletion js/web/script/test-runner-cli-args.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -405,7 +405,7 @@ export function parseTestRunnerCliArgs(cmdlineArgs: string[]): TestRunnerCliArgs
// and ChromeCanary is not in CI.

const defaultBrowserBackends = ['webgl', 'webgpu', 'wasm' /*, 'webnn'*/];
const nodejsBackends = ['cpu', 'wasm'];
const nodejsBackends = ['cpu', 'wasm', 'webgpu'];
const backendArgs = args.backend || args.b;
const backend =
typeof backendArgs !== 'string'
Expand Down
11 changes: 10 additions & 1 deletion js/web/test/test-main.ts
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Expand Up @@ -13,7 +13,16 @@ import { Logger } from '../lib/onnxjs/instrument';

import { Test } from './test-types';

if (ORT_WEB_TEST_CONFIG.model.some((testGroup) => testGroup.tests.some((test) => test.backend === 'cpu'))) {
if (
// when NPM test is launched with `-e=node` and (`-b=cpu` or `-b=webgpu`), load ONNXRuntime Node.js binding.
platform.name === 'Node.js' &&
(ORT_WEB_TEST_CONFIG.model.some((testGroup) =>
testGroup.tests.some((test) => test.backend === 'cpu' || test.backend === 'webgpu'),
) ||
ORT_WEB_TEST_CONFIG.op.some((testGroup) =>
testGroup.tests.some((test) => test.backend === 'cpu' || test.backend === 'webgpu'),
))
) {
// require onnxruntime-node
require('../../node');
}
Expand Down
35 changes: 19 additions & 16 deletions onnxruntime/core/framework/transpose_helper.cc
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Expand Up @@ -22,7 +22,8 @@ struct has_mlas_transpose<uint32_t> : std::true_type {};
template <typename T>
typename std::enable_if<!has_mlas_transpose<T>::value, void>::type SimpleTransposeSingleAxisOutwards(
const T* input_data, T* output_data, int64_t num_loops, int64_t num_writers, int64_t writes_per_loop,
int64_t writes_per_writer_per_loop) {
int64_t writes_per_writer_per_loop, concurrency::ThreadPool* tp = nullptr) {
ORT_UNUSED_PARAMETER(tp);
const T* end;
for (int64_t l = 0; l < num_loops; ++l) {
T* output_for_first_writer = output_data;
Expand All @@ -48,10 +49,10 @@ typename std::enable_if<!has_mlas_transpose<T>::value, void>::type SimpleTranspo
template <typename T>
typename std::enable_if<has_mlas_transpose<T>::value, void>::type SimpleTransposeSingleAxisOutwards(
const T* input_data, T* output_data, int64_t num_loops, int64_t num_writers, int64_t writes_per_loop,
int64_t writes_per_writer_per_loop) {
int64_t writes_per_writer_per_loop, concurrency::ThreadPool* tp = nullptr) {
for (int64_t l = 0; l < num_loops; ++l) {
MlasTranspose(input_data, output_data, static_cast<size_t>(writes_per_writer_per_loop),
static_cast<size_t>(num_writers));
static_cast<size_t>(num_writers), tp);
input_data += writes_per_loop;
output_data += writes_per_loop;
}
Expand Down Expand Up @@ -82,25 +83,25 @@ void TransposeSingleAxisOutwards(gsl::span<const size_t> permutations, const Ten
switch (bytes_per_write) {
case (sizeof(uint8_t)): {
SimpleTransposeSingleAxisOutwards(input_data, output_data, num_loops, num_writers, writes_per_loop,
writes_per_writer_per_loop);
writes_per_writer_per_loop, tp);
break;
}
case (sizeof(uint16_t)): {
SimpleTransposeSingleAxisOutwards(reinterpret_cast<const uint16_t*>(input_data),
reinterpret_cast<uint16_t*>(output_data), num_loops, num_writers,
writes_per_loop, writes_per_writer_per_loop);
writes_per_loop, writes_per_writer_per_loop, tp);
break;
}
case (sizeof(uint32_t)): {
SimpleTransposeSingleAxisOutwards(reinterpret_cast<const uint32_t*>(input_data),
reinterpret_cast<uint32_t*>(output_data), num_loops, num_writers,
writes_per_loop, writes_per_writer_per_loop);
writes_per_loop, writes_per_writer_per_loop, tp);
break;
}
case (sizeof(uint64_t)): {
SimpleTransposeSingleAxisOutwards(reinterpret_cast<const uint64_t*>(input_data),
reinterpret_cast<uint64_t*>(output_data), num_loops, num_writers,
writes_per_loop, writes_per_writer_per_loop);
writes_per_loop, writes_per_writer_per_loop, tp);
break;
}
default: {
Expand All @@ -125,7 +126,8 @@ void TransposeSingleAxisOutwards(gsl::span<const size_t> permutations, const Ten
template <typename T>
typename std::enable_if<!has_mlas_transpose<T>::value, void>::type SimpleTransposeSingleAxisInwards(
const T* input_data, T* output_data, int64_t num_loops, int64_t num_readers, int64_t reads_per_loop,
int64_t reads_per_reader_per_loop) {
int64_t reads_per_reader_per_loop, concurrency::ThreadPool* tp = nullptr) {
ORT_UNUSED_PARAMETER(tp);
T* end;
for (int64_t l = 0; l < num_loops; ++l) {
const T* input_for_first_reader = input_data;
Expand All @@ -150,10 +152,10 @@ typename std::enable_if<!has_mlas_transpose<T>::value, void>::type SimpleTranspo
template <typename T>
typename std::enable_if<has_mlas_transpose<T>::value, void>::type SimpleTransposeSingleAxisInwards(
const T* input_data, T* output_data, int64_t num_loops, int64_t num_readers, int64_t reads_per_loop,
int64_t reads_per_reader_per_loop) {
int64_t reads_per_reader_per_loop, concurrency::ThreadPool* tp = nullptr) {
for (int64_t l = 0; l < num_loops; ++l) {
MlasTranspose(input_data, output_data, static_cast<size_t>(num_readers),
static_cast<size_t>(reads_per_reader_per_loop));
static_cast<size_t>(reads_per_reader_per_loop), tp);
input_data += reads_per_loop;
output_data += reads_per_loop;
}
Expand All @@ -162,7 +164,8 @@ typename std::enable_if<has_mlas_transpose<T>::value, void>::type SimpleTranspos
// moving a single axis inwards where the read/write size is a power of 2 and between 8 and 64 bits.
// `input_shape_override` overrides the shape of `input` for compute purposes.
void TransposeSingleAxisInwards(gsl::span<const size_t> permutations, const Tensor& input, Tensor& output,
size_t from, size_t to, const TensorShape* input_shape_override = nullptr) {
size_t from, size_t to, const TensorShape* input_shape_override = nullptr,
concurrency::ThreadPool* tp = nullptr) {
ORT_UNUSED_PARAMETER(permutations);

const auto& input_shape = input_shape_override ? *input_shape_override : input.Shape();
Expand All @@ -184,25 +187,25 @@ void TransposeSingleAxisInwards(gsl::span<const size_t> permutations, const Tens
switch (bytes_per_read) {
case (sizeof(uint8_t)): {
SimpleTransposeSingleAxisInwards(input_data, output_data, num_loops, num_readers, reads_per_loop,
reads_per_reader_per_loop);
reads_per_reader_per_loop, tp);
break;
}
case (sizeof(uint16_t)): {
SimpleTransposeSingleAxisInwards(reinterpret_cast<const uint16_t*>(input_data),
reinterpret_cast<uint16_t*>(output_data), num_loops, num_readers, reads_per_loop,
reads_per_reader_per_loop);
reads_per_reader_per_loop, tp);
break;
}
case (sizeof(uint32_t)): {
SimpleTransposeSingleAxisInwards(reinterpret_cast<const uint32_t*>(input_data),
reinterpret_cast<uint32_t*>(output_data), num_loops, num_readers, reads_per_loop,
reads_per_reader_per_loop);
reads_per_reader_per_loop, tp);
break;
}
case (sizeof(uint64_t)): {
SimpleTransposeSingleAxisInwards(reinterpret_cast<const uint64_t*>(input_data),
reinterpret_cast<uint64_t*>(output_data), num_loops, num_readers, reads_per_loop,
reads_per_reader_per_loop);
reads_per_reader_per_loop, tp);
break;
}
default: {
Expand Down Expand Up @@ -236,7 +239,7 @@ void SingleAxisTranspose(gsl::span<const size_t> permutations, const Tensor& inp
if (from > to) {
TransposeSingleAxisOutwards(permutations, input, output, from, to, input_shape_override, tp);
} else {
TransposeSingleAxisInwards(permutations, input, output, from, to, input_shape_override);
TransposeSingleAxisInwards(permutations, input, output, from, to, input_shape_override, tp);
}
}

Expand Down
52 changes: 10 additions & 42 deletions onnxruntime/core/mlas/inc/mlas.h
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Expand Up @@ -1056,49 +1056,15 @@ MlasComputeTanh(
// Transpose routines.
//

template<typename DataType>
void
MLASCALL
MlasTranspose(
const uint8_t* Input,
uint8_t* Output,
size_t M,
size_t N
);

void
MLASCALL
MlasTranspose(
const int8_t* Input,
int8_t* Output,
size_t M,
size_t N
);

void
MLASCALL
MlasTranspose(
const uint16_t* Input,
uint16_t* Output,
size_t M,
size_t N
);

void
MLASCALL
MlasTranspose(
const uint32_t* Input,
uint32_t* Output,
const DataType* Input,
DataType* Output,
size_t M,
size_t N
);

void
MLASCALL
MlasTranspose(
const float* Input,
float* Output,
size_t M,
size_t N
size_t N,
MLAS_THREADPOOL* ThreadPool
);

//
Expand Down Expand Up @@ -1940,20 +1906,22 @@ MlasConvDepthwise(
MLAS_HALF_GEMM_POSTPROCESSOR* PostProc
);


inline
void
MlasTranspose(
const MLAS_FP16* Input,
MLAS_FP16* Output,
size_t M,
size_t N
size_t N,
MLAS_THREADPOOL* ThreadPool
)
{
MlasTranspose(
reinterpret_cast<const uint16_t*>(Input),
reinterpret_cast<uint16_t*>(Output),
M, N);
M,
N,
ThreadPool);
}


Expand Down
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