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#include "caffe2/opt/mobile.h"
#include "caffe2/core/logging.h"
#include "caffe2/opt/converter.h"
#include "caffe2/opt/fusion.h"
#include "caffe2/opt/passes.h"
namespace caffe2 {
namespace opt {
using namespace nom;
void addNNPACK(repr::NNModule* nn, bool low_memory) {
for (auto node : nn->dataFlow.getMutableNodes()) {
// Skip blobs.
NOM_REQUIRE_OR_CONT(repr::nn::is<repr::NeuralNetOperator>(node));
// Check if it is a convolution.
auto nnOp = repr::nn::get<repr::NeuralNetOperator>(node);
NOM_REQUIRE_OR_CONT(isa<nom::repr::Conv>(nnOp));
// Requires X, W, b for NNPACK
NOM_REQUIRE_OR_CONT(node->getInEdges().size() >= 3);
std::string engine = "NNPACK";
// Now do some specific checks to see if an NNPACK engine is correct.
bool validTransformCandidate = true;
auto conv = dyn_cast<nom::repr::Conv>(nnOp);
NOM_REQUIRE_OR_CONT(conv->getLayout() == nom::repr::Conv::NNLayout::NCHW);
// NNPACK only supports stride == 1
for (auto stride : conv->getStrides()) {
if (stride != 1) {
validTransformCandidate = false;
break;
}
}
NOM_REQUIRE_OR_CONT(validTransformCandidate);
// NNPACK only supports 2DConv.
const auto& kernelShape = conv->getKernelShape();
NOM_REQUIRE_OR_CONT(kernelShape.size() == 2);
// Kx1 and 1xK convs are inefficient in NNPACK.
if (kernelShape[0] != kernelShape[1]) {
NOM_REQUIRE_OR_CONT(kernelShape[0] != 1 && kernelShape[1] != 1);
}
// We're good to use our engine.
auto annotation = conv->getMutableAnnotation();
NOM_REQUIRE_OR_CONT(annotation && isa<Caffe2Annotation>(annotation));
auto* op = dyn_cast<Caffe2Annotation>(annotation)->getMutableOperatorDef();
op->set_engine(engine);
if (!low_memory) {
auto* precompute_argument = op->add_arg();
precompute_argument->set_name("convolution_transform_strategy");
precompute_argument->set_s("PRECOMPUTE");
}
}
}
namespace {
inline bool isNNPACKConvReluEfficient(
const std::string& algo,
const repr::Conv& conv) {
if (algo == "AUTO" || algo == "") {
for (auto stride : conv.getStrides()) {
if (stride > 1) {
return false;
}
}
for (auto kernel : conv.getKernelShape()) {
if (kernel < 2) {
return false;
}
}
} else if (!(algo == "WINOGRAD" || algo == "WINOGRAD_FP16" ||
algo == "FT8x8" || algo == "FT16x16")) {
return false;
}
return true;
}
} // namespace
void fuseNNPACKConvRelu(repr::NNModule* nn) {
auto should_fuse = [](const repr::Conv& conv) {
const auto annotation = conv.getAnnotation();
if (!annotation || !isa<Caffe2Annotation>(annotation)) {
return false;
}
const auto& op = dyn_cast<Caffe2Annotation>(annotation)->getOperatorDef();
// We only want to fuse for fast NNPACK convs
if (op.engine() != "NNPACK") {
return false;
}
caffe2::string algo = "AUTO";
for (const auto &arg : op.arg()) {
if (arg.name() == "algo") {
algo = arg.s();
}
}
if (!isNNPACKConvReluEfficient(algo, conv)) {
return false;
}
return true;
};
auto postprocess = [](repr::NNGraph::NodeRef conv_node) {
auto conv = repr::nn::get<repr::Conv>(conv_node);
auto annotation = conv->getMutableAnnotation();
if (!annotation || !isa<Caffe2Annotation>(annotation)) {
return;
}
auto* op = dyn_cast<Caffe2Annotation>(annotation)->getMutableOperatorDef();
auto* arg = op->add_arg();
arg->set_name("activation");
arg->set_s("Relu");
};
fuseActivation<repr::Conv, repr::Relu>(nn, should_fuse, postprocess);
}
REGISTER_OPT_PASS_FROM_FUNC(FuseNNPACKConvRelu, fuseNNPACKConvRelu);
REGISTER_OPT_PASS_FROM_FUNC(AddNNPACK, addNNPACK);
} // namespace opt
} // namespace caffe2
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