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Expand coverage of unary ops #24

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Merged
merged 3 commits into from
Mar 23, 2020
Merged

Expand coverage of unary ops #24

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181082b
Expand coverage of unary ops
bddppq Mar 22, 2020
53d8df5
remove conversion for sqrt that uses trt Scale
bddppq Mar 22, 2020
92a98b7
Remove test_scale
bddppq Mar 22, 2020
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1 change: 0 additions & 1 deletion core/conversion/converters/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@ cc_library(
"impl/element_wise.cpp",
"impl/linear.cpp",
"impl/pooling.cpp",
"impl/scale.cpp",
"impl/softmax.cpp",
"impl/unary.cpp",
],
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81 changes: 0 additions & 81 deletions core/conversion/converters/impl/scale.cpp
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61 changes: 43 additions & 18 deletions core/conversion/converters/impl/unary.cpp
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Expand Up @@ -8,28 +8,53 @@ namespace converters {
namespace impl {
namespace {

auto unary_registrations = RegisterNodeConversionPatterns()
.pattern({
"aten::log(Tensor self) -> Tensor",
[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
auto in = args[0].ITensor();
auto log = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kLOG);
#define convert(unary, trt_type) \
auto unary##_registrations TRTORCH_UNUSED = \
RegisterNodeConversionPatterns().pattern( \
{"aten::" #unary "(Tensor self) -> Tensor", \
[](ConversionCtx *ctx, const torch::jit::Node *n, \
args &args) -> bool { \
auto in = args[0].ITensor(); \
auto unary = \
ctx->net->addUnary(*in, nvinfer1::UnaryOperation::trt_type); \
\
TRTORCH_CHECK( \
unary, \
"Unable to create " #unary " layer from node: " << *n); \
\
unary->setName(util::node_info(n).c_str()); \
auto out_value = n->outputs()[0]; \
auto out_tensor = unary->getOutput(0); \
out_tensor->setName(out_value->debugName().c_str()); \
ctx->value_tensor_map[out_value] = out_tensor; \
LOG_DEBUG( \
"Output tensor shape: " << out_tensor->getDimensions()); \
\
return true; \
}});

TRTORCH_CHECK(log, "Unable to create log layer from node: " << *n);
convert(cos, kCOS);
convert(acos, kACOS);
convert(cosh, kCOSH);
convert(sin, kSIN);
convert(asin, kASIN);
convert(sinh, kSINH);
convert(tan, kTAN);
convert(atan, kATAN);
convert(abs, kABS);
convert(floor, kFLOOR);
convert(reciprocal, kRECIP);
convert(log, kLOG);
convert(ceil, kCEIL);
convert(sqrt, kSQRT);
convert(exp, kEXP);
convert(neg, kNEG);

#undef convert

log->setName(util::node_info(n).c_str());
auto out_value = n->outputs()[0];
auto out_tensor = log->getOutput(0);
out_tensor->setName(out_value->debugName().c_str());
ctx->value_tensor_map[out_value] = out_tensor;
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());

return true;
}
});
} // namespace
} // namespace impl
} // namespace converters
} // namespace conversion
} // namespace core
} // namespace trtorch
} // namespace trtorch
5 changes: 0 additions & 5 deletions tests/core/converters/BUILD
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Expand Up @@ -28,10 +28,6 @@ converter_test(
name = "test_conv"
)

converter_test(
name = "test_scale"
)

test_suite(
name = "test_converters",
tests = [
Expand All @@ -42,7 +38,6 @@ test_suite(
":test_linear",
":test_element_wise",
":test_conv",
":test_scale"
]
)

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25 changes: 0 additions & 25 deletions tests/core/converters/test_scale.cpp
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57 changes: 43 additions & 14 deletions tests/core/converters/test_unary.cpp
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Expand Up @@ -4,22 +4,51 @@
#include "tests/util/util.h"
#include "core/compiler.h"

TEST(Converters, ATenLogConvertsCorrectly) {
const auto graph = R"IR(
namespace {
std::string gen_test_graph(const std::string &unary) {
return R"IR(
graph(%0 : Tensor):
%3 : Tensor = aten::log(%0)
%3 : Tensor = aten::)IR" +
unary + R"IR((%0)
return (%3))IR";
}
} // namespace

auto g = std::make_shared<torch::jit::Graph>();
torch::jit::script::parseIR(graph, &*g);

auto in = at::randint(1, 5, {5}, {at::kCUDA});
auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto jit_results = trtorch::tests::util::RunGraph(g, params, {in});
#define test_unary(unary, name) \
TEST(Converters, ATen##name##ConvertsCorrectly) { \
const auto graph = gen_test_graph(#unary); \
\
auto g = std::make_shared<torch::jit::Graph>(); \
torch::jit::script::parseIR(graph, &*g); \
\
auto in = at::empty({10}, {at::kCUDA}).uniform_(0, 0.5); \
auto params = \
trtorch::core::conversion::get_named_params(g->inputs(), {}); \
auto jit_results = trtorch::tests::util::RunGraph(g, params, {in}); \
\
in = at::clone(in); \
params = trtorch::core::conversion::get_named_params(g->inputs(), {}); \
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in}); \
\
ASSERT_TRUE( \
trtorch::tests::util::almostEqual(jit_results[0], trt_results[0])); \
}

in = at::clone(in);
params = trtorch::core::conversion::get_named_params(g->inputs(), {});
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
test_unary(cos, Cos);
test_unary(acos, Acos);
test_unary(cosh, Cosh);
test_unary(sin, Sin);
test_unary(asin, Asin);
test_unary(sinh, Sinh);
test_unary(tan, Tan);
test_unary(atan, Atan);
test_unary(abs, Abs);
test_unary(floor, Floor);
test_unary(reciprocal, Reciprocal);
test_unary(log, Log);
test_unary(ceil, Ceil);
test_unary(sqrt, Sqrt);
test_unary(exp, Exp);
test_unary(neg, Neg);

ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0]));
}
#undef test_unary