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[feat] Add support for integers in aten::abs converter (#35) #1232

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merged 3 commits into from
Aug 7, 2022
Merged

[feat] Add support for integers in aten::abs converter (#35) #1232

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474e58c
Add support for integers in aten::abs converter (#35)
mfeliz-cruise Aug 2, 2022
74a43d0
Use quotes instead of angle brackets for non stdlib includes
mfeliz-cruise Aug 4, 2022
1775558
replace angle bracket include with quotes
mfeliz-cruise Aug 5, 2022
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43 changes: 42 additions & 1 deletion core/conversion/converters/impl/unary.cpp
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Original file line number Diff line number Diff line change
@@ -1,13 +1,55 @@
#include "core/conversion/converters/converters.h"
#include "core/util/prelude.h"

#include "torch/torch.h"

namespace torch_tensorrt {
namespace core {
namespace conversion {
namespace converters {
namespace impl {
namespace {


auto abs_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
{"aten::abs (Tensor self) -> Tensor",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
auto in = args[0].ITensor();
bool unary_supported_input = in->getType() == nvinfer1::DataType::kFLOAT
|| in->getType() == nvinfer1::DataType::kHALF
|| in->getType() == nvinfer1::DataType::kINT8;
if(unary_supported_input){
auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kABS);
TORCHTRT_CHECK(unary_layer, "Unable to create abs layer from node: " << *n);
unary_layer->setName(util::node_info(n).c_str());
auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
return true;
}
else{
//For types not supported by kABS, use an elementwise implementation abs(x) = max(x, -1 * x)
at::Tensor neg_one = torch::full({1}, -1).to(util::TRTDataTypeToScalarType(in->getType()));
auto neg_one_const = tensor_to_const(ctx, neg_one);
auto neg_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kPROD,
in,
neg_one_const,
util::node_info(n) + std::string("_Negation"));
TORCHTRT_CHECK(neg_layer, "Unable to create prod layer from node: " << *n);
auto max_layer = add_elementwise(
ctx,
nvinfer1::ElementWiseOperation::kMAX,
in,
neg_layer->getOutput(0),
util::node_info(n) + std::string("_Max"));
TORCHTRT_CHECK(max_layer, "Unable to create max layer from node: " << *n);
auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], max_layer->getOutput(0));
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
return true;
}
}});

#define convert(unary, trt_type) \
auto unary##_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( \
{"aten::" #unary "(Tensor self) -> Tensor", \
Expand All @@ -32,7 +74,6 @@ convert(asin, kASIN);
convert(sinh, kSINH);
convert(tan, kTAN);
convert(atan, kATAN);
convert(abs, kABS);
convert(floor, kFLOOR);
convert(reciprocal, kRECIP);
convert(log, kLOG);
Expand Down
17 changes: 17 additions & 0 deletions tests/core/conversion/converters/test_unary.cpp
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
#include <string>
#include "torch/torch.h"
#include "core/compiler.h"
#include "gtest/gtest.h"
#include "tests/util/util.h"
Expand All @@ -14,6 +15,22 @@ std::string gen_test_graph(const std::string& unary) {
}
} // namespace

TEST(Converters, ATenAbsIntConvertsCorrectly) {
const auto graph = gen_test_graph("abs");
auto g = std::make_shared<torch::jit::Graph>();
torch::jit::parseIR(graph, g.get());

auto in = at::tensor({-1, 1, -2, 2, -3, 3}, {at::kCUDA}).to(torch::kInt32);
auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});

in = at::clone(in);
params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});

ASSERT_TRUE(torch_tensorrt::tests::util::exactlyEqual(jit_results[0], trt_results[0]));
}

#define test_unary(unary, name) \
TEST(Converters, ATen##name##ConvertsCorrectly) { \
const auto graph = gen_test_graph(#unary); \
Expand Down