mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
78 lines
2.4 KiB
C++
78 lines
2.4 KiB
C++
#include "caffe2/core/common.h"
|
|
#include "caffe2/opt/converter.h"
|
|
#include "caffe2/opt/device.h"
|
|
|
|
#include <gtest/gtest.h>
|
|
|
|
using namespace nom::repr;
|
|
|
|
#define ADD_ARG(_op, _name, _type, _val) \
|
|
{ \
|
|
caffe2::Argument* arg = _op->add_arg(); \
|
|
arg->set_name(_name); \
|
|
arg->set_##_type(_val); \
|
|
}
|
|
|
|
TEST(DeviceTest, InsertCopies) {
|
|
caffe2::NetDef net;
|
|
for (auto i = 0; i < 9; ++i) {
|
|
if (i % 3 == 0) {
|
|
caffe2::OperatorDef* def = net.add_op();
|
|
def->set_type("Conv");
|
|
def->add_input("X");
|
|
def->add_input("W" + c10::to_string(i));
|
|
def->add_input("b" + c10::to_string(i));
|
|
ADD_ARG(def, "kernel", i, 3);
|
|
ADD_ARG(def, "stride", i, 1);
|
|
ADD_ARG(def, "pad", i, 0);
|
|
ADD_ARG(def, "order", s, "NCHW");
|
|
def->add_output("X");
|
|
def->mutable_device_option()->set_device_type(caffe2::PROTO_CPU);
|
|
} else {
|
|
caffe2::OperatorDef* def = net.add_op();
|
|
def->set_type("Relu");
|
|
def->add_input("X");
|
|
def->add_output("X");
|
|
def->mutable_device_option()->set_device_type(caffe2::PROTO_CPU);
|
|
}
|
|
}
|
|
auto nn = caffe2::convertToNNModule(net);
|
|
|
|
for (auto node : nn.dataFlow.getMutableNodes()) {
|
|
if (nn::is<Relu>(node)) {
|
|
auto annot = nn::get<NeuralNetOperator>(node)->getMutableAnnotation();
|
|
auto c2_annot = dyn_cast<caffe2::Caffe2Annotation>(annot);
|
|
c2_annot->setDeviceType(caffe2::PROTO_OPENCL);
|
|
}
|
|
}
|
|
|
|
caffe2::opt::insertCopies(
|
|
&nn,
|
|
[](NNGraph::NodeRef node) {
|
|
// Ignore all tensors
|
|
if (!nn::is<NeuralNetOperator>(node)) {
|
|
return true;
|
|
}
|
|
auto annot = nn::get<NeuralNetOperator>(node)->getMutableAnnotation();
|
|
NOM_REQUIRE_OR_RET_FALSE(annot);
|
|
auto c2_annot = dyn_cast<caffe2::Caffe2Annotation>(annot);
|
|
NOM_REQUIRE_OR_RET_FALSE(c2_annot);
|
|
return c2_annot->getDeviceType() == caffe2::PROTO_OPENCL;
|
|
},
|
|
[](NNGraph& g) {
|
|
return g.createNode(std::make_unique<GenericOperator>());
|
|
},
|
|
[](NNGraph& g) {
|
|
return g.createNode(std::make_unique<GenericOperator>());
|
|
});
|
|
|
|
auto proto = caffe2::convertToCaffe2Proto(nn, net);
|
|
|
|
// Conv -> Relu -> Relu
|
|
// becomes
|
|
// Conv -> Generic -> Relu -> Relu -> Generic
|
|
// thus
|
|
// 9 ops of this pattern becomes 15
|
|
EXPECT_EQ(proto.op().size(), 15);
|
|
}
|