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Re-apply PyTorch pthreadpool changes

Browse Source 
Summary:
This re-applies D21232894 (b9d3869df3) and D22162524, plus updates jni_deps in a few places
to avoid breaking host JNI tests.

Test Plan: `buck test @//fbandroid/mode/server //fbandroid/instrumentation_tests/com/facebook/caffe2:host-test`

Reviewed By: xcheng16

Differential Revision: D22199952

fbshipit-source-id: df13eef39c01738637ae8cf7f581d6ccc88d37d5
This commit is contained in:
David Reiss
2020-06-23 19:22:31 -07:00
committed by Facebook GitHub Bot
parent bdc00196d1
commit b7e044f0e5
44 changed files with 366 additions and 1756 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.circleci/scripts/binary_ios_upload.sh
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@ -14,7 +14,7 @@ mkdir -p ${ZIP_DIR}/src
cp -R ${ARTIFACTS_DIR}/arm64/include ${ZIP_DIR}/install/
# build a FAT bianry
cd ${ZIP_DIR}/install/lib
target_libs=(libc10.a libclog.a libcpuinfo.a libeigen_blas.a libpytorch_qnnpack.a libtorch_cpu.a libtorch.a libXNNPACK.a)
target_libs=(libc10.a libclog.a libcpuinfo.a libeigen_blas.a libpthreadpool.a libpytorch_qnnpack.a libtorch_cpu.a libtorch.a libXNNPACK.a)
for lib in ${target_libs[*]}
do
if [ -f "${ARTIFACTS_DIR}/x86_64/lib/${lib}" ] && [ -f "${ARTIFACTS_DIR}/arm64/lib/${lib}" ]; then

1
BUILD.bazel
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@ -1350,7 +1350,6 @@ filegroup(
"caffe2/utils/smart_tensor_printer.cc",
"caffe2/utils/string_utils.cc",
"caffe2/utils/threadpool/ThreadPool.cc",
"caffe2/utils/threadpool/ThreadPoolMobile.cc",
"caffe2/utils/threadpool/pthreadpool.cc",
"caffe2/utils/threadpool/pthreadpool_impl.cc",
],

2
CMakeLists.txt
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@ -481,7 +481,7 @@ if(USE_PYTORCH_QNNPACK)
endif()
if(USE_XNNPACK)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_XNNPACK")
endif()
if(USE_VULKAN)

3
android/pytorch_android/CMakeLists.txt
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@ -99,6 +99,7 @@ if(ANDROID_ABI)
import_static_lib(libnnpack)
import_static_lib(libXNNPACK)
import_static_lib(libpytorch_qnnpack)
import_static_lib(libpthreadpool)
import_static_lib(libeigen_blas)
import_static_lib(libcpuinfo)
import_static_lib(libclog)
@ -115,6 +116,7 @@ if(ANDROID_ABI)
libnnpack
libXNNPACK
libpytorch_qnnpack
libpthreadpool
libeigen_blas
libcpuinfo
libclog
@ -129,6 +131,7 @@ else()
nnpack
XNNPACK
pytorch_qnnpack
pthreadpool
cpuinfo
clog
)

8
android/pytorch_android/src/main/cpp/pytorch_jni_common.cpp
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@ -8,8 +8,10 @@
#include "pytorch_jni_common.h"
#if defined(__ANDROID__)
#include <caffe2/utils/threadpool/ThreadPool.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#ifndef USE_PTHREADPOOL
#define USE_PTHREADPOOL
#endif /* USE_PTHREADPOOL */
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#endif
namespace pytorch_jni {
@ -605,7 +607,7 @@ class PyTorchAndroidJni : public facebook::jni::JavaClass<PyTorchAndroidJni> {
}
static void setNumThreads(facebook::jni::alias_ref<jclass>, jint numThreads) {
caffe2::mobile_threadpool()->setNumThreads(numThreads);
caffe2::pthreadpool()->set_thread_count(numThreads);
}
};
#endif

41
aten/src/ATen/ParallelNative.cpp
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@ -6,8 +6,7 @@
#ifndef C10_MOBILE
#include <c10/core/thread_pool.h>
#else
#include <caffe2/utils/threadpool/ThreadPool.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#endif // C10_MOBILE
#include <atomic>
@ -88,15 +87,15 @@ void _run_with_pool(const std::function<void(int, size_t)>& fn, size_t range) {
// Run the first task on the current thread directly.
fn(0, 0);
#else
caffe2::ThreadPool* pool = caffe2::mobile_threadpool();
if (pool) {
// caffe2::ThreadPool can utilize the current thread.
pool->run(fn, range);
} else {
for (size_t i = 0; i < range; ++i) {
fn(0, i);
}
}
caffe2::PThreadPool* const pool = caffe2::pthreadpool();
TORCH_INTERNAL_ASSERT(pool, "Invalid thread pool!");
pool->run(
// PThreadPool::run() is blocking. A std::function [const] reference to
// this lambda cannot go out of scope before PThreadPool::run() returns.
[&fn](const size_t task_id) {
fn(0 /* unused */, task_id);
}, range);
#endif // C10_MOBILE
}
@ -184,7 +183,7 @@ void init_num_threads() {
#endif
#ifdef C10_MOBILE
caffe2::mobile_threadpool();
caffe2::pthreadpool();
#endif
}
@ -208,7 +207,9 @@ void set_num_threads(int nthreads) {
}
}
#else
TORCH_CHECK(false, "set_num_threads is not supported for mobile.");
caffe2::PThreadPool* const pool = caffe2::pthreadpool();
TORCH_INTERNAL_ASSERT(pool, "Invalid thread pool!");
pool->set_thread_count(nthreads);
#endif // C10_MOBILE
}
@ -226,9 +227,9 @@ int get_num_threads() {
return _get_intraop_pool().size() + 1;
}
#else
caffe2::ThreadPool* pool = caffe2::mobile_threadpool();
// caffe2::ThreadPool::getNumThreads() counts the current thread.
return !pool || in_parallel_region() ? 1 /* current thread */ : pool->getNumThreads();
caffe2::PThreadPool* const pool = caffe2::pthreadpool();
TORCH_INTERNAL_ASSERT(pool, "Invalid thread pool!")
return in_parallel_region() ? 1 /* current thread */ : pool->get_thread_count();
#endif // C10_MOBILE
}
@ -257,8 +258,8 @@ void intraop_launch(std::function<void()> func) {
func();
}
#else
// TODO: caffe2::ThreadPool doesn't support submitting tasks separately and
// running in parallel. Should fix it when this API becomes popular.
// TODO: caffe2::PThreadPool only provides a data-parallel API.
// Task parallelism is not currently supported.
func();
#endif // C10_MOBILE
}
@ -280,8 +281,8 @@ std::shared_ptr<c10::ivalue::Future> intraop_launch_future(
}
return future;
#else
// TODO: caffe2::ThreadPool doesn't support submitting tasks separately and
// running in parallel. Should fix it when this API becomes popular.
// TODO: caffe2::PThreadPool only provides a data-parallel API.
// Task parallelism is not currently supported.
auto future = std::make_shared<c10::ivalue::Future>(NoneType::get());
func();
future->markCompleted();

32
aten/src/ATen/native/NNPACK.cpp
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@ -58,7 +58,7 @@ bool _nnpack_available() {
#include <nnpack.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <ATen/native/ConvUtils.h>
namespace at {
@ -87,35 +87,7 @@ static bool init_nnpack() {
}
static pthreadpool_t nnpack_threadpool() {
// Try initializing a threadpool for NNPACK's use. If we fail to
// successfully initialize an implementation, return nullptr which will
// instruct NNPACK to run single threaded.
#ifdef C10_MOBILE
// If building for mobile, use Caffe 2's mobile-friendly threadpool.
return caffe2::mobile_pthreadpool();
#else
// Otherwise, try using pthreadpool if we manage to initialize it successfully.
static pthreadpool_t nnpack_threadpool_ = nullptr;
static bool called_nnpack_threadpool_ = false;
if (!called_nnpack_threadpool_) {
called_nnpack_threadpool_ = true;
#ifdef INTRA_OP_PARALLEL
const uint32_t threads = at::get_num_threads();
#else
const uint32_t threads = std::thread::hardware_concurrency();
#endif
nnpack_threadpool_ = pthreadpool_create(threads);
if (!nnpack_threadpool_) {
LOG(WARNING) << "Failed to initialize pthreadpool! Running NNPACK in single-threaded mode.";
}
}
return nnpack_threadpool_;
#endif
return caffe2::pthreadpool_();
}
bool _nnpack_available() {

4
aten/src/ATen/native/quantized/cpu/q_avgpool.cpp
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@ -5,7 +5,7 @@
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <c10/util/math_compat.h>
#include <algorithm>
@ -375,7 +375,7 @@ Tensor qnnpack_avg_pool2d(
CAFFE_ENFORCE(
setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Average Pooling operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(qnnpack_operator, threadpool);
TORCH_INTERNAL_ASSERT(

1
aten/src/ATen/native/quantized/cpu/q_avgpool3d.cpp
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@ -5,7 +5,6 @@
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <c10/util/math_compat.h>
#include <algorithm>

4
aten/src/ATen/native/quantized/cpu/qadd.cpp
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@ -7,7 +7,7 @@
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -194,7 +194,7 @@ Tensor qnnpack_add(Tensor qa, Tensor qb, double scale, int64_t zero_point) {
setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Add operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(qnnpack_operator, threadpool);

4
aten/src/ATen/native/quantized/cpu/qchannel_shuffle.cpp
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@ -8,7 +8,7 @@
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <c10/core/TensorOptions.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -82,7 +82,7 @@ Tensor quantized_channel_shuffle_impl(
setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK ChannelShuffle operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(qnnpack_operator, threadpool);
TORCH_INTERNAL_ASSERT(

4
aten/src/ATen/native/quantized/cpu/qclamp.cpp
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@ -7,7 +7,7 @@
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <ATen/quantized/Quantizer.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -64,7 +64,7 @@ Tensor qnnpack_clamp(Tensor input, Scalar min, Scalar max) {
TORCH_INTERNAL_ASSERT(setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Clamp operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(clamp_op, threadpool);

4
aten/src/ATen/native/quantized/cpu/qconv.cpp
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@ -10,7 +10,7 @@
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <ATen/native/quantized/cpu/quant_utils.h>
#include <ATen/native/quantized/cpu/conv_packed_params.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
template <int kSpatialDim = 2>
bool ConvDimChecks(
@ -603,7 +603,7 @@ at::Tensor PackedConvWeightsQnnp<kSpatialDim>::apply_impl(
output_min,
output_max,
reinterpret_cast<uint8_t*>(output.template data_ptr<c10::quint8>()),
caffe2::mobile_pthreadpool());
caffe2::pthreadpool_());
TORCH_INTERNAL_ASSERT(
run_status == pytorch_qnnp_status_success,

4
aten/src/ATen/native/quantized/cpu/qhardsigmoid.cpp
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@ -5,7 +5,7 @@
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -57,7 +57,7 @@ Tensor qnnpack_hardsigmoid(Tensor input) {
TORCH_INTERNAL_ASSERT(setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Hardsigmoid operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(hardsigmoid_op, threadpool);

4
aten/src/ATen/native/quantized/cpu/qhardswish.cpp
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@ -5,7 +5,7 @@
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -51,7 +51,7 @@ Tensor qnnpack_hardswish(const Tensor& qx, Tensor& qy) {
TORCH_INTERNAL_ASSERT(setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Hardswish operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(hardswish_op, threadpool);

6
aten/src/ATen/native/quantized/cpu/qlinear.cpp
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@ -4,7 +4,7 @@
#include <ATen/native/quantized/cpu/fbgemm_utils.h>
#include <ATen/native/quantized/cpu/packed_params.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <torch/custom_class.h>
#include <torch/library.h>
@ -341,7 +341,9 @@ at::Tensor PackedLinearWeightsQnnp::apply_impl(
packB->getPackedWeights(),
(uint8_t*)output.data_ptr<c10::quint8>(),
rows_w /* output_stride */,
caffe2::mobile_pthreadpool() /* threadpool */);
// TODO (Ashkan): Disabling temporarily.
// Throws a floating point exception with OSS pthreadpool.
nullptr);
TORCH_INTERNAL_ASSERT(
runStatus == pytorch_qnnp_status_success,

4
aten/src/ATen/native/quantized/cpu/qlinear_dynamic.cpp
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@ -5,7 +5,7 @@
#include <ATen/native/quantized/cpu/packed_params.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <ATen/native/quantized/cpu/quant_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <torch/library.h>
#include <torch/custom_class.h>
@ -327,7 +327,7 @@ at::Tensor PackedLinearWeightsQnnp::apply_dynamic_impl(at::Tensor input) {
bias_ptr,
output.data_ptr<float>(),
rows_w /* output_stride */,
caffe2::mobile_pthreadpool() /* threadpool */);
caffe2::pthreadpool_() /* threadpool */);
TORCH_INTERNAL_ASSERT(
runStatus == pytorch_qnnp_status_success,

4
aten/src/ATen/native/quantized/cpu/qpool.cpp
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@ -9,7 +9,7 @@
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
#include <vector>
@ -346,7 +346,7 @@ void check_maxpool2d_params(
setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK MaxPool operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(qnnpack_operator, threadpool);
TORCH_INTERNAL_ASSERT(

4
aten/src/ATen/native/quantized/cpu/qreduction.cpp
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@ -3,7 +3,7 @@
#include <ATen/NativeFunctions.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
namespace at {
namespace native {
@ -66,7 +66,7 @@ Tensor qnnpack_mean(const Tensor& input, IntArrayRef dim) {
CAFFE_ENFORCE(
setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Global Average Pooling operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(qnnpack_operator, threadpool);
TORCH_INTERNAL_ASSERT(

4
aten/src/ATen/native/quantized/cpu/qrelu.cpp
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@ -6,7 +6,7 @@
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <torch/library.h>
#include <algorithm>
@ -69,7 +69,7 @@ Tensor qnnpack_relu(Tensor input) {
setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK Relu operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(qnnpack_operator, threadpool);

4
aten/src/ATen/native/quantized/cpu/qsigmoid.cpp
View File

@ -7,7 +7,7 @@
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -66,7 +66,7 @@ Tensor qnnpack_sigmoid(Tensor input) {
TORCH_INTERNAL_ASSERT(setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK sigmoid operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(sigmoid_op, threadpool);

4
aten/src/ATen/native/quantized/cpu/qtanh.cpp
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@ -7,7 +7,7 @@
#include <ATen/native/quantized/cpu/quantized_ops.h>
#include <ATen/native/quantized/cpu/init_qnnpack.h>
#include <ATen/native/quantized/cpu/qnnpack_utils.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <algorithm>
@ -64,7 +64,7 @@ Tensor qnnpack_tanh(Tensor input) {
TORCH_INTERNAL_ASSERT(setupStatus == pytorch_qnnp_status_success,
"failed to setup QNNPACK TanH operator");
pthreadpool_t threadpool = caffe2::mobile_pthreadpool();
pthreadpool_t threadpool = caffe2::pthreadpool_();
const pytorch_qnnp_status runStatus =
pytorch_qnnp_run_operator(tanh_op, threadpool);

2
aten/src/ATen/native/xnnpack/Common.h
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@ -5,7 +5,7 @@
#ifdef USE_XNNPACK
#include <xnnpack.h>
#include <caffe2/utils/threadpool/ThreadPoolXNNPACK.h>
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
namespace at {
namespace native {

4
aten/src/ATen/native/xnnpack/Convolution.cpp
View File

@ -208,7 +208,7 @@ Tensor run(
padded_input_nhwc.size(Layout::Activation4D::width), // input_width
padded_input_nhwc.data_ptr<float>(), // input
output.data_ptr<float>(), // output
caffe2::xnnpack_threadpool()); // threadpool
caffe2::pthreadpool_()); // threadpool
TORCH_CHECK(
xnn_status_success == setup_status,
@ -216,7 +216,7 @@ Tensor run(
const xnn_status run_status = xnn_run_operator(
context.op.get(), // operator
caffe2::xnnpack_threadpool()); // threadpool
caffe2::pthreadpool_()); // threadpool
TORCH_INTERNAL_ASSERT(
xnn_status_success == run_status,

4
aten/src/ATen/native/xnnpack/Linear.cpp
View File

@ -137,7 +137,7 @@ Tensor run(
Layout::ActivationND::batch(padded_input.sizes()), // Batch,
padded_input.data_ptr<float>(), // input
output.data_ptr<float>(), // output
caffe2::xnnpack_threadpool()); // threadpool
caffe2::pthreadpool_()); // threadpool
TORCH_CHECK(
xnn_status_success == setup_status,
@ -145,7 +145,7 @@ Tensor run(
const xnn_status run_status = xnn_run_operator(
context.op.get(), // operator
caffe2::xnnpack_threadpool()); // threadpool
caffe2::pthreadpool_()); // threadpool
TORCH_INTERNAL_ASSERT(
xnn_status_success == run_status,

4
aten/src/ATen/native/xnnpack/MaxPooling.cpp
View File

@ -219,7 +219,7 @@ Tensor max_pool2d(
input_padded_contig_nhwc.size(Layout::Activation4D::width), // input_width
input_padded_contig_nhwc.data_ptr<float>(), // input
output_padded_contig_nhwc.data_ptr<float>(), // output
caffe2::xnnpack_threadpool()); // threadpool
caffe2::pthreadpool_()); // threadpool
TORCH_CHECK(
xnn_status_success == setup_status,
@ -227,7 +227,7 @@ Tensor max_pool2d(
const xnn_status run_status = xnn_run_operator(
max_pool_op, // operator
caffe2::xnnpack_threadpool()); // threadpool
caffe2::pthreadpool_()); // threadpool
TORCH_INTERNAL_ASSERT(
xnn_status_success == run_status,

5
caffe2/CMakeLists.txt
View File

@ -87,7 +87,6 @@ endif()
# Note: the folders that are being commented out have not been properly
# addressed yet.
# For pthreadpool_new_if_impl. TODO: Remove when threadpools are unitied.
if(NOT MSVC AND USE_XNNPACK)
if(NOT TARGET fxdiv)
set(FXDIV_BUILD_TESTS OFF CACHE BOOL "")
@ -96,10 +95,6 @@ if(NOT MSVC AND USE_XNNPACK)
"${FXDIV_SOURCE_DIR}"
"${CMAKE_BINARY_DIR}/FXdiv")
endif()
if(NOT (INTERN_BUILD_MOBILE AND NOT BUILD_CAFFE2_MOBILE))
set_source_files_properties(
utils/threadpool/pthreadpool_new_if_impl.c PROPERTIES COMPILE_FLAGS -fno-openmp)
endif()
endif()
add_subdirectory(core)

31
caffe2/utils/CMakeLists.txt
View File

@ -1,15 +1,8 @@
# TODO: Add ThreadPoolXNNPACK.cc when XNNPACK integration is updated
# to pass the actual threadpool ptr instead of nullptr.
if(INTERN_BUILD_MOBILE AND NOT BUILD_CAFFE2_MOBILE)
add_definitions(-DUSE_INTERNAL_THREADPOOL_IMPL)
list(APPEND Caffe2_CPU_SRCS
utils/string_utils.cc
utils/threadpool/pthreadpool.cc
utils/threadpool/pthreadpool_impl.cc
utils/threadpool/pthreadpool_new_if_impl.c
utils/threadpool/pthreadpool-cpp.cc
utils/threadpool/ThreadPool.cc
utils/threadpool/ThreadPoolMobile.cc
utils/threadpool/ThreadPoolXNNPACK.cc
)
set(Caffe2_CPU_SRCS ${Caffe2_CPU_SRCS} PARENT_SCOPE)
return()
@ -28,23 +21,19 @@ list(APPEND Caffe2_CPU_SRCS
utils/proto_convert.cc
utils/proto_utils.cc
utils/proto_wrap.cc
utils/threadpool/ThreadPool.cc
utils/signal_handler.cc
utils/smart_tensor_printer.cc
utils/string_utils.cc
utils/threadpool/ThreadPool.cc)
utils/string_utils.cc)
# ---[ threadpool/pthreadpool* is a local modification of the NNPACK
# pthreadpool with a very similar interface. Neither NNPACK, nor this
# thread pool supports Windows.
if(NOT MSVC AND USE_XNNPACK)
add_definitions(-DUSE_INTERNAL_THREADPOOL_IMPL)
set(Caffe2_CPU_SRCS ${Caffe2_CPU_SRCS}
if(USE_PTHREADPOOL)
list(APPEND Caffe2_CPU_SRCS
utils/threadpool/pthreadpool-cpp.cc)
if(USE_INTERNAL_PTHREADPOOL_IMPL)
list(APPEND Caffe2_CPU_SRCS
utils/threadpool/pthreadpool.cc
utils/threadpool/pthreadpool_impl.cc
utils/threadpool/pthreadpool_new_if_impl.c
utils/threadpool/ThreadPoolMobile.cc
utils/threadpool/ThreadPoolXNNPACK.cc
)
utils/threadpool/pthreadpool_impl.cc)
endif()
endif()
set(Caffe2_GPU_SRCS ${Caffe2_GPU_SRCS}

21
caffe2/utils/threadpool/ThreadPoolMobile.cc
View File

@ -1,21 +0,0 @@
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/ThreadPool.h>
#include <caffe2/utils/threadpool/pthreadpool.h>
namespace caffe2 {
caffe2::ThreadPool* mobile_threadpool() {
#ifdef C10_MOBILE
static std::unique_ptr<caffe2::ThreadPool> thread_pool =
caffe2::ThreadPool::defaultThreadPool();
return thread_pool.get();
#else
return nullptr;
#endif
}
pthreadpool_t mobile_pthreadpool() {
return reinterpret_cast<pthreadpool_t>(mobile_threadpool());
}
} // namespace caffe2

24
caffe2/utils/threadpool/ThreadPoolMobile.h
View File

@ -1,24 +0,0 @@
#pragma once
#include <caffe2/utils/threadpool/pthreadpool.h>
// TODO Implement a parallel_for version for Mobile here, add to Aten/Parallel.h
namespace caffe2 {
class ThreadPool;
// Return a singleton instance of caffe2::ThreadPool for ATen/TH multithreading.
ThreadPool* mobile_threadpool();
// NOTE: This interface is temporary and should not be used.
// Please use Aten/Parallel.h for parallel primitives in pytorch.
// This implementation will be used by pytorch mobile, specifically
// NNPACK/QNNPACK. For mobile we need to use caffe2::ThreadPool instead of the
// 3rd party pthreadpool. Future work (TODO) Implement a mobile version of
// "at::parallel_for" using caffe2::ThreadPool so all ATen/TH multithreading
// usage is mobile friendly; Refactor QNNPACK or pthreadpool to explicitly using
// "at::parallel_for" primitive to replace pthreadpool_compute_1d for Pytorch;
pthreadpool_t mobile_pthreadpool();
size_t getDefaultNumThreads();
} // namespace caffe2

22
caffe2/utils/threadpool/ThreadPoolXNNPACK.cc
View File

@ -1,22 +0,0 @@
#include <caffe2/utils/threadpool/pthreadpool.h>
#include <caffe2/utils/threadpool/ThreadPoolMobile.h>
#include <caffe2/utils/threadpool/ThreadPoolXNNPACK.h>
#include <memory>
namespace caffe2 {
// Will be unified.
pthreadpool_t xnnpack_threadpool() {
// Depending on internal implemenation vs. OSS we will link against pthreadpool_create_xnnpack
// or pthreadpool_create. This is only temporary. It will be unified soon.
#ifdef USE_INTERNAL_THREADPOOL_IMPL
static std::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy_xnnpack)>
threadpool(pthreadpool_create_xnnpack(getDefaultNumThreads()), pthreadpool_destroy_xnnpack);
#else
static std::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)>
threadpool(pthreadpool_create(getDefaultNumThreads()), pthreadpool_destroy);
#endif
return threadpool.get();
}
} // namespace caffe2

7
caffe2/utils/threadpool/ThreadPoolXNNPACK.h
View File

@ -1,7 +0,0 @@
#pragma once
// Creating a separate .h/.cc file for creating threadpool for XNNPACK
// to avoid touching existing internal builds.
// When we unify threadpools this should all go away.
namespace caffe2 {
pthreadpool_t xnnpack_threadpool();
} // namespace caffe2

71
caffe2/utils/threadpool/pthreadpool-cpp.cc Normal file
View File

@ -0,0 +1,71 @@
#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
#include <c10/util/Exception.h>
namespace caffe2 {
PThreadPool::PThreadPool(const size_t thread_count)
: threadpool_(pthreadpool_create(thread_count), pthreadpool_destroy) {}
size_t PThreadPool::get_thread_count() const {
std::lock_guard<std::mutex> lock{mutex_};
TORCH_INTERNAL_ASSERT(threadpool_.get(), "Invalid threadpool!");
return pthreadpool_get_threads_count(threadpool_.get());
}
void PThreadPool::set_thread_count(const size_t thread_count) {
std::lock_guard<std::mutex> lock{mutex_};
// As it stands, pthreadpool is an entirely data parallel framework with no
// support for task parallelism. Hence, all functions are blocking, and no
// user-provided tasks can be in flight when the control is returned to the
// user of the API, which means re-initializing the library, without the
// need to wait on any pending tasks, is all one needs to do to re-adjust
// the thread count.
threadpool_.reset(pthreadpool_create(thread_count));
}
void PThreadPool::run(
const std::function<void(size_t)>& fn,
const size_t range) {
std::lock_guard<std::mutex> lock{mutex_};
TORCH_INTERNAL_ASSERT(threadpool_.get(), "Invalid threadpool!");
struct Context final {
const std::function<void(size_t)>& fn;
} context{
fn,
};
pthreadpool_parallelize_1d(
threadpool_.get(),
// Note: pthreadpool_parallelize_1d() is a blocking function. The
// function pointer to this lambda passed on to
// pthreadpool_parallelize_1d() cannot go out of scope until
// pthreadpool_parallelize_1d() returns.
[](void* const context, const size_t item) {
reinterpret_cast<Context*>(context)->fn(item);
},
&context,
range,
0u);
}
// Forward declaration
size_t getDefaultNumThreads();
PThreadPool* pthreadpool() {
static std::unique_ptr<PThreadPool> threadpool =
std::make_unique<PThreadPool>(getDefaultNumThreads());
return threadpool.get();
}
pthreadpool_t pthreadpool_() {
PThreadPool* const threadpool = pthreadpool();
TORCH_INTERNAL_ASSERT(
threadpool, "Failed to acquire an instance of PThreadPool!");
return threadpool->threadpool_.get();
}
} // namespace caffe2

54
caffe2/utils/threadpool/pthreadpool-cpp.h Normal file
View File

@ -0,0 +1,54 @@
#pragma once
#ifdef USE_PTHREADPOOL
#ifdef USE_INTERNAL_PTHREADPOOL_IMPL
#include <caffe2/utils/threadpool/pthreadpool.h>
#else
#include <pthreadpool.h>
#endif
#include <functional>
#include <memory>
#include <mutex>
namespace caffe2 {
class PThreadPool final {
public:
explicit PThreadPool(size_t thread_count);
~PThreadPool() = default;
PThreadPool(const PThreadPool&) = delete;
PThreadPool& operator=(const PThreadPool&) = delete;
PThreadPool(PThreadPool&&) = delete;
PThreadPool& operator=(PThreadPool&&) = delete;
size_t get_thread_count() const;
void set_thread_count(size_t thread_count);
// Run, in parallel, function fn(task_id) over task_id in range [0, range).
// This function is blocking. All input is processed by the time it returns.
void run(const std::function<void(size_t)>& fn, size_t range);
private:
friend pthreadpool_t pthreadpool_();
private:
mutable std::mutex mutex_;
std::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)> threadpool_;
};
// Return a singleton instance of PThreadPool for ATen/TH multithreading.
PThreadPool* pthreadpool();
// Exposes the underlying implementation of PThreadPool.
// Only for use in external libraries so as to unify threading across
// internal (i.e. ATen, etc.) and external (e.g. NNPACK, QNNPACK, XNNPACK)
// use cases.
pthreadpool_t pthreadpool_();
} // namespace caffe2
#endif /* USE_PTHREADPOOL */

54
caffe2/utils/threadpool/pthreadpool.cc
View File

@ -32,7 +32,7 @@ static inline size_t min(size_t a, size_t b) {
}
struct compute_1d_tiled_context {
pthreadpool_function_1d_tiled_t function;
legacy_pthreadpool_function_1d_tiled_t function;
void* argument;
size_t range;
size_t tile;
@ -46,9 +46,9 @@ static void compute_1d_tiled(void* context_, size_t linear_index) {
context->function(context->argument, index, tile);
}
void pthreadpool_compute_1d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_1d_tiled_t function,
void legacy_pthreadpool_compute_1d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_1d_tiled_t function,
void* argument,
size_t range,
size_t tile)
@ -65,12 +65,12 @@ void pthreadpool_compute_1d_tiled(
/*.argument = */ argument,
/*.range = */ range,
/*.tile = */ tile};
pthreadpool_compute_1d(threadpool, (pthreadpool_function_1d_t) compute_1d_tiled, &context, tile_range);
legacy_pthreadpool_compute_1d(threadpool, (legacy_pthreadpool_function_1d_t) compute_1d_tiled, &context, tile_range);
}
}
struct compute_2d_context {
pthreadpool_function_2d_t function;
legacy_pthreadpool_function_2d_t function;
void* argument;
caffe2::FixedDivisor<int32_t> range_j;
};
@ -85,9 +85,9 @@ static void compute_2d(void* context_, size_t linear_index) {
context->function(context->argument, q, r);
}
void pthreadpool_compute_2d(
struct pthreadpool* threadpool,
pthreadpool_function_2d_t function,
void legacy_pthreadpool_compute_2d(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_2d_t function,
void* argument,
size_t range_i,
size_t range_j)
@ -106,12 +106,12 @@ void pthreadpool_compute_2d(
/*.function = */ function,
/*.argument = */ argument,
/*.range_j = */ caffe2::FixedDivisor<int32_t>(range_j)};
pthreadpool_compute_1d(threadpool, (pthreadpool_function_1d_t) compute_2d, &context, range_i * range_j);
legacy_pthreadpool_compute_1d(threadpool, (legacy_pthreadpool_function_1d_t) compute_2d, &context, range_i * range_j);
}
}
struct compute_2d_tiled_context {
pthreadpool_function_2d_tiled_t function;
legacy_pthreadpool_function_2d_tiled_t function;
void* argument;
caffe2::FixedDivisor<int32_t> tile_range_j;
size_t range_i;
@ -135,9 +135,9 @@ static void compute_2d_tiled(void* context_, size_t linear_index) {
context->function(context->argument, index_i, index_j, tile_i, tile_j);
}
void pthreadpool_compute_2d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_2d_tiled_t function,
void legacy_pthreadpool_compute_2d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_2d_tiled_t function,
void* argument,
size_t range_i,
size_t range_j,
@ -166,12 +166,12 @@ void pthreadpool_compute_2d_tiled(
/*.range_j = */ range_j,
/*.tile_i = */ tile_i,
/*.tile_j = */ tile_j};
pthreadpool_compute_1d(threadpool, (pthreadpool_function_1d_t) compute_2d_tiled, &context, tile_range_i * tile_range_j);
legacy_pthreadpool_compute_1d(threadpool, (legacy_pthreadpool_function_1d_t) compute_2d_tiled, &context, tile_range_i * tile_range_j);
}
}
struct compute_3d_tiled_context {
pthreadpool_function_3d_tiled_t function;
legacy_pthreadpool_function_3d_tiled_t function;
void* argument;
caffe2::FixedDivisor<int32_t> tile_range_j;
caffe2::FixedDivisor<int32_t> tile_range_k;
@ -205,9 +205,9 @@ static void compute_3d_tiled(
context->argument, index_i, index_j, index_k, tile_i, tile_j, tile_k);
}
void pthreadpool_compute_3d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_3d_tiled_t function,
void legacy_pthreadpool_compute_3d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_3d_tiled_t function,
void* argument,
size_t range_i,
size_t range_j,
@ -251,16 +251,16 @@ void pthreadpool_compute_3d_tiled(
/*.tile_i = */ tile_i,
/*.tile_j = */ tile_j,
/*.tile_k = */ tile_k};
pthreadpool_compute_1d(
legacy_pthreadpool_compute_1d(
threadpool,
(pthreadpool_function_1d_t)compute_3d_tiled,
(legacy_pthreadpool_function_1d_t)compute_3d_tiled,
&context,
tile_range_i * tile_range_j * tile_range_k);
}
}
struct compute_4d_tiled_context {
pthreadpool_function_4d_tiled_t function;
legacy_pthreadpool_function_4d_tiled_t function;
void* argument;
caffe2::FixedDivisor<int32_t> tile_range_kl;
caffe2::FixedDivisor<int32_t> tile_range_j;
@ -310,9 +310,9 @@ static void compute_4d_tiled(
tile_l);
}
void pthreadpool_compute_4d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_4d_tiled_t function,
void legacy_pthreadpool_compute_4d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_4d_tiled_t function,
void* argument,
size_t range_i,
size_t range_j,
@ -367,9 +367,9 @@ void pthreadpool_compute_4d_tiled(
/*.tile_j = */ tile_j,
/*.tile_k = */ tile_k,
/*.tile_l = */ tile_l};
pthreadpool_compute_1d(
legacy_pthreadpool_compute_1d(
threadpool,
(pthreadpool_function_1d_t)compute_4d_tiled,
(legacy_pthreadpool_function_1d_t)compute_4d_tiled,
&context,
tile_range_i * tile_range_j * tile_range_k * tile_range_l);
}

247
caffe2/utils/threadpool/pthreadpool.h
View File

@ -5,49 +5,16 @@
#include "ThreadPoolCommon.h"
#include <stddef.h> // for size_t
typedef struct pthreadpool* pthreadpool_t;
typedef void (*pthreadpool_function_1d_t)(void*, size_t);
typedef void (*pthreadpool_function_1d_tiled_t)(void*, size_t, size_t);
typedef void (*pthreadpool_function_2d_t)(void*, size_t, size_t);
typedef void (*pthreadpool_function_2d_tiled_t)(void*, size_t, size_t, size_t, size_t);
typedef void (*pthreadpool_function_3d_tiled_t)(
void*,
size_t,
size_t,
size_t,
size_t,
size_t,
size_t);
typedef void (*pthreadpool_function_4d_tiled_t)(
void*,
size_t,
size_t,
size_t,
size_t,
size_t,
size_t,
size_t,
size_t);
#include <stdint.h> // for uint32_t
typedef void (*pthreadpool_task_1d_t)(void*, size_t);
typedef void (*pthreadpool_task_1d_tile_1d_t)(void*, size_t, size_t);
typedef void (*pthreadpool_task_2d_t)(void*, size_t, size_t);
typedef void (*pthreadpool_task_2d_tile_1d_t)(void*, size_t, size_t, size_t);
typedef void (*pthreadpool_task_2d_tile_2d_t)(void*, size_t, size_t, size_t, size_t);
typedef void (*pthreadpool_task_3d_tile_2d_t)(
void*,
size_t,
size_t,
size_t,
size_t,
size_t);
typedef void (*pthreadpool_task_4d_tile_2d_t)(
typedef struct pthreadpool* legacy_pthreadpool_t;
typedef void (*legacy_pthreadpool_function_1d_t)(void*, size_t);
typedef void (*legacy_pthreadpool_function_1d_tiled_t)(void*, size_t, size_t);
typedef void (*legacy_pthreadpool_function_2d_t)(void*, size_t, size_t);
typedef void (*legacy_pthreadpool_function_2d_tiled_t)(void*, size_t, size_t, size_t, size_t);
typedef void (*legacy_pthreadpool_function_3d_tiled_t)(
void*,
size_t,
size_t,
@ -55,16 +22,7 @@ typedef void (*pthreadpool_task_4d_tile_2d_t)(
size_t,
size_t,
size_t);
typedef void (*pthreadpool_task_5d_tile_2d_t)(
void*,
size_t,
size_t,
size_t,
size_t,
size_t,
size_t,
size_t);
typedef void (*pthreadpool_task_6d_tile_2d_t)(
typedef void (*legacy_pthreadpool_function_4d_tiled_t)(
void*,
size_t,
size_t,
@ -90,8 +48,8 @@ extern "C" {
* On error the function returns NULL and sets errno accordingly.
*/
//Returns internal threadpool impl.
pthreadpool_t pthreadpool_create(size_t threads_count);
// Returns internal threadpool impl.
legacy_pthreadpool_t legacy_pthreadpool_create(size_t threads_count);
/**
* Queries the number of threads in a thread pool.
@ -100,7 +58,7 @@ pthreadpool_t pthreadpool_create(size_t threads_count);
*
* @returns The number of threads in the thread pool.
*/
size_t pthreadpool_get_threads_count(pthreadpool_t threadpool);
size_t legacy_pthreadpool_get_threads_count(legacy_pthreadpool_t threadpool);
/**
* Processes items in parallel using threads from a thread pool.
@ -117,38 +75,45 @@ size_t pthreadpool_get_threads_count(pthreadpool_t threadpool);
* @param[in] items The number of items to process. The @a function
* will be called once for each item.
*/
void pthreadpool_compute_1d(
pthreadpool_t threadpool,
pthreadpool_function_1d_t function,
void legacy_pthreadpool_compute_1d(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_1d_t function,
void* argument,
size_t range);
void pthreadpool_compute_1d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_1d_tiled_t function,
void legacy_pthreadpool_parallelize_1d(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_1d_t function,
void* argument,
size_t range,
uint32_t flags);
void legacy_pthreadpool_compute_1d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_1d_tiled_t function,
void* argument,
size_t range,
size_t tile);
void pthreadpool_compute_2d(
pthreadpool_t threadpool,
pthreadpool_function_2d_t function,
void legacy_pthreadpool_compute_2d(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_2d_t function,
void* argument,
size_t range_i,
size_t range_j);
void pthreadpool_compute_2d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_2d_tiled_t function,
void legacy_pthreadpool_compute_2d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_2d_tiled_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t tile_i,
size_t tile_j);
void pthreadpool_compute_3d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_3d_tiled_t function,
void legacy_pthreadpool_compute_3d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_3d_tiled_t function,
void* argument,
size_t range_i,
size_t range_j,
@ -157,9 +122,9 @@ void pthreadpool_compute_3d_tiled(
size_t tile_j,
size_t tile_k);
void pthreadpool_compute_4d_tiled(
pthreadpool_t threadpool,
pthreadpool_function_4d_tiled_t function,
void legacy_pthreadpool_compute_4d_tiled(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_4d_tiled_t function,
void* argument,
size_t range_i,
size_t range_j,
@ -178,129 +143,29 @@ void pthreadpool_compute_4d_tiled(
*
* @param[in,out] threadpool The thread pool to destroy.
*/
void pthreadpool_destroy(pthreadpool_t threadpool);
void legacy_pthreadpool_destroy(legacy_pthreadpool_t threadpool);
// New interface copy/pasted from pthreadpool.
// We will merge the internal and third-party/pthreadpool eventually.
// For now copy-paste to get past build issues.
#ifdef USE_INTERNAL_PTHREADPOOL_IMPL
#define PTHREADPOOL_FLAG_DISABLE_DENORMALS 0x00000001
#define pthreadpool_t legacy_pthreadpool_t
#define pthreadpool_function_1d_t legacy_pthreadpool_function_1d_t
#define pthreadpool_function_1d_tiled_t legacy_pthreadpool_function_1d_tiled_t
#define pthreadpool_function_2d_t legacy_pthreadpool_function_2d_t
#define pthreadpool_function_2d_tiled_t legacy_pthreadpool_function_2d_tiled_t
#define pthreadpool_function_3d_tiled_t legacy_pthreadpool_function_3d_tiled_t
#define pthreadpool_function_4d_tiled_t legacy_pthreadpool_function_4d_tiled_t
#define pthreadpool_create legacy_pthreadpool_create
#define pthreadpool_destroy legacy_pthreadpool_destroy
#define pthreadpool_get_threads_count legacy_pthreadpool_get_threads_count
#define pthreadpool_compute_1d legacy_pthreadpool_compute_1d
#define pthreadpool_parallelize_1d legacy_pthreadpool_parallelize_1d
#define pthreadpool_compute_1d_tiled legacy_pthreadpool_compute_1d_tiled
#define pthreadpool_compute_2d legacy_pthreadpool_compute_2d
#define pthreadpool_compute_2d_tiled legacy_pthreadpool_compute_2d_tiled
#define pthreadpool_compute_3d_tiled legacy_pthreadpool_compute_3d_tiled
#define pthreadpool_compute_4d_tiled legacy_pthreadpool_compute_4d_tiled
// Returns the copied threadpool impl of third-party/pthreadpool
pthreadpool_t pthreadpool_create_xnnpack(size_t threads_count);
// Copied third-party impl.
size_t pthreadpool_get_threads_count_xnnpack(pthreadpool_t threadpool);
// Copied third-party impl.
void pthreadpool_destroy_xnnpack(pthreadpool_t threadpool);
/**
* Processes items in parallel using threads from a thread pool.
*
* When the call returns, all items have been processed and the thread pool is
* ready for a new task.
*
* @note If multiple threads call this function with the same thread pool, the
* calls are serialized.
*
* @param[in] threadpool The thread pool to use for parallelisation.
* @param[in] function The function to call for each item.
* @param[in] argument The first argument passed to the @a function.
* @param[in] items The number of items to process. The @a function
* will be called once for each item.
*/
void pthreadpool_parallelize_1d(
pthreadpool_t threadpool,
pthreadpool_task_1d_t function,
void* argument,
size_t range,
uint32_t flags);
void pthreadpool_parallelize_1d_tile_1d(
pthreadpool_t threadpool,
pthreadpool_task_1d_tile_1d_t function,
void* argument,
size_t range,
size_t tile,
uint32_t flags);
void pthreadpool_parallelize_2d(
pthreadpool_t threadpool,
pthreadpool_task_2d_t function,
void* argument,
size_t range_i,
size_t range_j,
uint32_t flags);
void pthreadpool_parallelize_2d_tile_1d(
pthreadpool_t threadpool,
pthreadpool_task_2d_tile_1d_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t tile_j,
uint32_t flags);
void pthreadpool_parallelize_2d_tile_2d(
pthreadpool_t threadpool,
pthreadpool_task_2d_tile_2d_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t tile_i,
size_t tile_j,
uint32_t flags);
void pthreadpool_parallelize_3d_tile_2d(
pthreadpool_t threadpool,
pthreadpool_task_3d_tile_2d_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t range_k,
size_t tile_j,
size_t tile_k,
uint32_t flags);
void pthreadpool_parallelize_4d_tile_2d(
pthreadpool_t threadpool,
pthreadpool_task_4d_tile_2d_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t range_k,
size_t range_l,
size_t tile_k,
size_t tile_l,
uint32_t flags);
void pthreadpool_parallelize_5d_tile_2d(
pthreadpool_t threadpool,
pthreadpool_task_5d_tile_2d_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t range_k,
size_t range_l,
size_t range_m,
size_t tile_l,
size_t tile_m,
uint32_t flags);
void pthreadpool_parallelize_6d_tile_2d(
pthreadpool_t threadpool,
pthreadpool_task_6d_tile_2d_t function,
void* argument,
size_t range_i,
size_t range_j,
size_t range_k,
size_t range_l,
size_t range_m,
size_t range_n,
size_t tile_m,
size_t tile_n,
uint32_t flags);
#endif /* USE_INTERNAL_PTHREADPOOL_IMPL */
#ifdef __cplusplus
} /* extern "C" */

33
caffe2/utils/threadpool/pthreadpool_impl.cc
View File

@ -6,9 +6,9 @@
// External API
//
void pthreadpool_compute_1d(
pthreadpool_t threadpool,
pthreadpool_function_1d_t function,
void legacy_pthreadpool_compute_1d(
legacy_pthreadpool_t threadpool,
legacy_pthreadpool_function_1d_t function,
void* argument,
size_t range) {
if (threadpool == nullptr) {
@ -27,30 +27,31 @@ void pthreadpool_compute_1d(
range);
}
size_t pthreadpool_get_threads_count(pthreadpool_t threadpool) {
// The current fix only useful when XNNPACK calls pthreadpool_get_threads_count with nullptr.
void legacy_pthreadpool_parallelize_1d(
const legacy_pthreadpool_t threadpool,
const legacy_pthreadpool_function_1d_t function,
void* const argument,
const size_t range,
uint32_t) {
legacy_pthreadpool_compute_1d(threadpool, function, argument, range);
}
size_t legacy_pthreadpool_get_threads_count(legacy_pthreadpool_t threadpool) {
// The current fix only useful when XNNPACK calls legacy_pthreadpool_get_threads_count with nullptr.
if (threadpool == nullptr) {
return 1;
}
return reinterpret_cast<caffe2::ThreadPool*>(threadpool)->getNumThreads();
// TODO: Future fix: If we keep maintaining two different threadpools.
// Old C2 and new one for XNNPACK, then the we have two different pthreadpool pointer
// types. One is caffe2::Thredpool*, the other is pthreadpool* (pthreadpool_new_if_impl.c)
// XNNPACK calls pthreadpool_get_threads_count during op setup using pthreadpool*, and
// uses _parallelize_ interface for for actual work.
// While NNPACK uses caffe2::Threadpool*.
// Thus if pthreadpool_get_threads_count is getting called from XNNPACK we cannot
// reinterpret_cast it to ThreadPool. It will seg fault or worse will have unedfined behavior.
}
pthreadpool_t pthreadpool_create(size_t threads_count) {
legacy_pthreadpool_t legacy_pthreadpool_create(size_t threads_count) {
std::mutex thread_pool_creation_mutex_;
std::lock_guard<std::mutex> guard(thread_pool_creation_mutex_);
return reinterpret_cast<pthreadpool_t>(new caffe2::ThreadPool(threads_count));
return reinterpret_cast<legacy_pthreadpool_t>(new caffe2::ThreadPool(threads_count));
}
void pthreadpool_destroy(pthreadpool_t pthreadpool) {
void legacy_pthreadpool_destroy(legacy_pthreadpool_t pthreadpool) {
if (pthreadpool) {
caffe2::ThreadPool* threadpool =
reinterpret_cast<caffe2::ThreadPool*>(pthreadpool);

1209
caffe2/utils/threadpool/pthreadpool_new_if_impl.c
View File

File diff suppressed because it is too large Load Diff

62
caffe2/utils/threadpool/pthreadpool_utils_new_if.h
View File

@ -1,62 +0,0 @@
#pragma once
#include <stdint.h>
#if defined(__SSE__) || defined(__x86_64__)
#include <xmmintrin.h>
#endif
struct fpu_state {
#if defined(__SSE__) || defined(__x86_64__)
uint32_t mxcsr;
#elif defined(__arm__) && defined(__ARM_FP) && (__ARM_FP != 0)
uint32_t fpscr;
#elif defined(__aarch64__)
uint64_t fpcr;
#else
char unused;
#endif
};
static inline struct fpu_state get_fpu_state() {
struct fpu_state state = { 0 };
#if defined(__SSE__) || defined(__x86_64__)
state.mxcsr = (uint32_t) _mm_getcsr();
#elif defined(__arm__) && defined(__ARM_FP) && (__ARM_FP != 0)
__asm__ __volatile__("VMRS %[fpscr], fpscr" : [fpscr] "=r" (state.fpscr));
#elif defined(__aarch64__)
__asm__ __volatile__("MRS %[fpcr], fpcr" : [fpcr] "=r" (state.fpcr));
#endif
return state;
}
static inline void set_fpu_state(const struct fpu_state state) {
#if defined(__SSE__) || defined(__x86_64__)
_mm_setcsr((unsigned int) state.mxcsr);
#elif defined(__arm__) && defined(__ARM_FP) && (__ARM_FP != 0)
__asm__ __volatile__("VMSR fpscr, %[fpscr]" : : [fpscr] "r" (state.fpscr));
#elif defined(__aarch64__)
__asm__ __volatile__("MSR fpcr, %[fpcr]" : : [fpcr] "r" (state.fpcr));
#endif
}
static inline void disable_fpu_denormals() {
#if defined(__SSE__) || defined(__x86_64__)
_mm_setcsr(_mm_getcsr() | 0x8040);
#elif defined(__arm__) && defined(__ARM_FP) && (__ARM_FP != 0)
uint32_t fpscr;
__asm__ __volatile__(
"VMRS %[fpscr], fpscr\n"
"ORR %[fpscr], #0x1000000\n"
"VMSR fpscr, %[fpscr]\n"
: [fpscr] "=r" (fpscr));
#elif defined(__aarch64__)
uint64_t fpcr;
__asm__ __volatile__(
"MRS %[fpcr], fpcr\n"
"ORR %w[fpcr], %w[fpcr], 0x1000000\n"
"ORR %w[fpcr], %w[fpcr], 0x80000\n"
"MSR fpcr, %[fpcr]\n"
: [fpcr] "=r" (fpcr));
#endif
}

78
cmake/Dependencies.cmake
View File

@ -266,6 +266,8 @@ if(USE_NNPACK OR USE_QNNPACK OR USE_PYTORCH_QNNPACK OR USE_XNNPACK)
set(CPUINFO_LOG_LEVEL "error" CACHE STRING "")
set(PTHREADPOOL_LIBRARY_TYPE "static" CACHE STRING "")
endif()
else()
set(DISABLE_NNPACK_AND_FAMILY ON)
endif()
set(CONFU_DEPENDENCIES_SOURCE_DIR ${PROJECT_BINARY_DIR}/confu-srcs
@ -281,12 +283,23 @@ if(INTERN_BUILD_MOBILE AND INTERN_USE_EIGEN_BLAS)
endif()
# ---[ pthreadpool
# QNNPACK and NNPACK both depend on pthreadpool, but when building with libtorch
# they should use the pthreadpool implementation under caffe2/utils/threadpool
# instead of the default implementation. To avoid confusion, add pthreadpool
# subdirectory explicitly with EXCLUDE_FROM_ALL property prior to QNNPACK/NNPACK
# does so, which will prevent it from installing the default pthreadpool library.
if(INTERN_BUILD_MOBILE AND NOT BUILD_CAFFE2_MOBILE AND (USE_QNNPACK OR USE_NNPACK OR USE_XNNPACK))
if(NOT USE_SYSTEM_PTHREADPOOL AND (INTERN_BUILD_MOBILE OR NOT DISABLE_NNPACK_AND_FAMILY))
set(USE_PTHREADPOOL ON CACHE BOOL "" FORCE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_PTHREADPOOL")
# Opt for custom Caffe2 implementation on MSVC. Windows support seems to have
# been added to pthreadpool recently but the current third party revision we are
# using right now does not suppor it. Should unify later after updating pthreadpool.
if(MSVC)
set(USE_INTERNAL_PTHREADPOOL_IMPL ON CACHE BOOL "" FORCE)
# XNNPACK cannot link against a custom implementation of pthreadpool
caffe2_update_option(USE_XNNPACK OFF)
else()
# We would like to maintain the ability to build against the internal C2
# pthreadpool implementation for now, hence this flag. This flag is not
# exposed as a build option to the user and is purly internal.
set(USE_INTERNAL_PTHREADPOOL_IMPL OFF CACHE BOOL "" FORCE)
if(NOT DEFINED PTHREADPOOL_SOURCE_DIR)
set(CAFFE2_THIRD_PARTY_ROOT "${PROJECT_SOURCE_DIR}/third_party")
set(PTHREADPOOL_SOURCE_DIR "${CAFFE2_THIRD_PARTY_ROOT}/pthreadpool" CACHE STRING "pthreadpool source directory")
@ -297,29 +310,18 @@ if(INTERN_BUILD_MOBILE AND NOT BUILD_CAFFE2_MOBILE AND (USE_QNNPACK OR USE_NNPAC
set(PTHREADPOOL_BUILD_BENCHMARKS OFF CACHE BOOL "")
add_subdirectory(
"${PTHREADPOOL_SOURCE_DIR}"
"${CONFU_DEPENDENCIES_BINARY_DIR}/pthreadpool"
EXCLUDE_FROM_ALL)
endif()
endif()
# XNNPACK has not option of like QNNPACK_CUSTOM_THREADPOOL
# that allows us to hijack pthreadpool interface.
# Thus not doing this ends up building pthreadpool as well as
# the internal implemenation of pthreadpool which results in symbol conflicts.
if(USE_XNNPACK AND NOT USE_SYSTEM_XNNPACK)
if(NOT DEFINED PTHREADPOOL_SOURCE_DIR)
set(CAFFE2_THIRD_PARTY_ROOT "${PROJECT_SOURCE_DIR}/third_party")
set(PTHREADPOOL_SOURCE_DIR "${CAFFE2_THIRD_PARTY_ROOT}/pthreadpool" CACHE STRING "pthreadpool source directory")
"${CONFU_DEPENDENCIES_BINARY_DIR}/pthreadpool")
set_property(TARGET pthreadpool PROPERTY POSITION_INDEPENDENT_CODE ON)
endif()
if(NOT TARGET pthreadpool)
set(PTHREADPOOL_BUILD_TESTS OFF CACHE BOOL "")
set(PTHREADPOOL_BUILD_BENCHMARKS OFF CACHE BOOL "")
add_subdirectory(
"${PTHREADPOOL_SOURCE_DIR}"
"${CONFU_DEPENDENCIES_BINARY_DIR}/pthreadpool"
EXCLUDE_FROM_ALL)
list(APPEND Caffe2_DEPENDENCY_LIBS pthreadpool)
endif()
if(USE_INTERNAL_PTHREADPOOL_IMPL)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_INTERNAL_PTHREADPOOL_IMPL")
endif()
else()
set(USE_PTHREADPOOL OFF CACHE BOOL "" FORCE)
endif()
# ---[ Caffe2 uses cpuinfo library in the thread pool
@ -369,9 +371,12 @@ if(USE_QNNPACK)
endif()
if(NOT TARGET qnnpack)
if(NOT USE_SYSTEM_PTHREADPOOL AND USE_INTERNAL_PTHREADPOOL_IMPL)
set(QNNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
endif()
set(QNNPACK_BUILD_TESTS OFF CACHE BOOL "")
set(QNNPACK_BUILD_BENCHMARKS OFF CACHE BOOL "")
set(QNNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
set(QNNPACK_LIBRARY_TYPE "static" CACHE STRING "")
add_subdirectory(
"${QNNPACK_SOURCE_DIR}"
@ -379,7 +384,6 @@ if(USE_QNNPACK)
# We build static versions of QNNPACK and pthreadpool but link
# them into a shared library for Caffe2, so they need PIC.
set_property(TARGET qnnpack PROPERTY POSITION_INDEPENDENT_CODE ON)
set_property(TARGET pthreadpool PROPERTY POSITION_INDEPENDENT_CODE ON)
set_property(TARGET cpuinfo PROPERTY POSITION_INDEPENDENT_CODE ON)
endif()
@ -400,9 +404,12 @@ if(USE_PYTORCH_QNNPACK)
endif()
if(NOT TARGET pytorch_qnnpack)
if(NOT USE_SYSTEM_PTHREADPOOL AND USE_INTERNAL_PTHREADPOOL_IMPL)
set(PYTORCH_QNNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
endif()
set(PYTORCH_QNNPACK_BUILD_TESTS OFF CACHE BOOL "")
set(PYTORCH_QNNPACK_BUILD_BENCHMARKS OFF CACHE BOOL "")
set(PYTORCH_QNNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
set(PYTORCH_QNNPACK_LIBRARY_TYPE "static" CACHE STRING "")
add_subdirectory(
"${PYTORCH_QNNPACK_SOURCE_DIR}"
@ -410,9 +417,6 @@ if(USE_PYTORCH_QNNPACK)
# We build static versions of QNNPACK and pthreadpool but link
# them into a shared library for Caffe2, so they need PIC.
set_property(TARGET pytorch_qnnpack PROPERTY POSITION_INDEPENDENT_CODE ON)
if(NOT USE_SYSTEM_PTHREADPOOL)
set_property(TARGET pthreadpool PROPERTY POSITION_INDEPENDENT_CODE ON)
endif()
set_property(TARGET cpuinfo PROPERTY POSITION_INDEPENDENT_CODE ON)
endif()
@ -447,7 +451,6 @@ if(USE_XNNPACK AND NOT USE_SYSTEM_XNNPACK)
endif()
if(NOT TARGET XNNPACK)
set(XNNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
set(XNNPACK_LIBRARY_TYPE "static" CACHE STRING "")
set(XNNPACK_BUILD_BENCHMARKS OFF CACHE BOOL "")
set(XNNPACK_BUILD_TESTS OFF CACHE BOOL "")
@ -457,15 +460,6 @@ if(USE_XNNPACK AND NOT USE_SYSTEM_XNNPACK)
"${CONFU_DEPENDENCIES_BINARY_DIR}/XNNPACK")
set_property(TARGET XNNPACK PROPERTY POSITION_INDEPENDENT_CODE ON)
# Context: pthreadpool_get_threads_count implementation that is built in pytorch, uses
# implementation defined in caffe2/utils/threadpool/pthreadpool_impl.cc. This implementation
# assumes the the pthreadpool* passed is of type caffe2::ThradPool and thus does reinterpret cast.
# This is not valid when we create pthreadpool via caffe2::xnnpack_threadpool, which is of type
# compatible with new pthreadpool interface and is used in PT's XNNPACK integration.
# Thus all the calls for pthreadpool_get_threads_count originating from XNNPACK must be routed
# appropriately to pthreadpool_get_threads_count_xnnpack, which does not do the aforementioned
# casting to caffe2::ThradPool. Once the threadpools are unified, we will not need this.
target_compile_definitions(XNNPACK PRIVATE -Dpthreadpool_get_threads_count=pthreadpool_get_threads_count_xnnpack)
endif()
include_directories(SYSTEM ${XNNPACK_INCLUDE_DIR})

5
cmake/External/nnpack.cmake vendored
View File

@ -59,9 +59,12 @@ if(ANDROID OR IOS OR ${CMAKE_SYSTEM_NAME} STREQUAL "Linux" OR ${CMAKE_SYSTEM_NAM
set(GOOGLETEST_SOURCE_DIR "${CAFFE2_THIRD_PARTY_ROOT}/googletest" CACHE STRING "Google Test source directory")
if(NOT TARGET nnpack)
if(NOT USE_SYSTEM_PTHREADPOOL AND USE_INTERNAL_PTHREADPOOL_IMPL)
set(NNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
endif()
set(NNPACK_BUILD_TESTS OFF CACHE BOOL "")
set(NNPACK_BUILD_BENCHMARKS OFF CACHE BOOL "")
set(NNPACK_CUSTOM_THREADPOOL ON CACHE BOOL "")
set(NNPACK_LIBRARY_TYPE "static" CACHE STRING "")
set(PTHREADPOOL_LIBRARY_TYPE "static" CACHE STRING "")
set(CPUINFO_LIBRARY_TYPE "static" CACHE STRING "")

5
cmake/TorchConfig.cmake.in
View File

@ -69,6 +69,11 @@ if(NOT @BUILD_SHARED_LIBS@)
list(APPEND TORCH_LIBRARIES ${XNNPACK_LIBRARY})
endif()
if(NOT @USE_INTERNAL_PTHREADPOOL_IMPL@)
find_library(PTHREADPOOL_LIBRARY pthreadpool PATHS "${TORCH_INSTALL_PREFIX}/lib")
list(APPEND TORCH_LIBRARIES ${PTHREADPOOL_LIBRARY})
endif()
if(@INTERN_USE_EIGEN_BLAS@)
find_library(EIGEN_BLAS_LIBRARY eigen_blas PATHS "${TORCH_INSTALL_PREFIX}/lib")
list(APPEND TORCH_LIBRARIES ${EIGEN_BLAS_LIBRARY})

2
ios/TestApp/benchmark/setup.rb
View File

@ -63,7 +63,7 @@ targets.each do |target|
target.resources_build_phase.add_file_reference(config_file_ref, true)
end
puts "Linking static libraries..."
libs = ['libc10.a', 'libclog.a', 'libXNNPACK.a', 'libeigen_blas.a', 'libcpuinfo.a', 'libpytorch_qnnpack.a', 'libtorch_cpu.a', 'libtorch.a']
libs = ['libc10.a', 'libclog.a', 'libpthreadpool.a', 'libXNNPACK.a', 'libeigen_blas.a', 'libcpuinfo.a', 'libpytorch_qnnpack.a', 'libtorch_cpu.a', 'libtorch.a']
targets.each do |target|
target.frameworks_build_phases.clear
for lib in libs do

2
scripts/xcode_build.rb
View File

@ -51,7 +51,7 @@ end
# link static libraries
target.frameworks_build_phases.clear
libs = ['libc10.a', 'libclog.a', 'libXNNPACK.a', 'libeigen_blas.a', 'libcpuinfo.a', 'libpytorch_qnnpack.a', 'libtorch_cpu.a', 'libtorch.a']
libs = ['libc10.a', 'libclog.a', 'libpthreadpool.a', 'libXNNPACK.a', 'libeigen_blas.a', 'libcpuinfo.a', 'libpytorch_qnnpack.a', 'libtorch_cpu.a', 'libtorch.a']
for lib in libs do
path = "#{install_path}/lib/#{lib}"
if File.exist?(path)