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narendasannarendasan
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feat(hardtanh): Adds support for the the hard tanh operator
Signed-off-by: Naren Dasan <naren@narendasan.com> Signed-off-by: Naren Dasan <narens@nvidia.com>
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2 files changed

+98
-7
lines changed

2 files changed

+98
-7
lines changed

‎core/conversion/converters/impl/activation.cpp‎

Lines changed: 49 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -28,11 +28,11 @@ namespace {
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auto act##_registrations TRTORCH_UNUSED = \
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RegisterNodeConversionPatterns() \
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.pattern({"aten::" #act "(Tensor input) -> (Tensor)", \
31-
[](ConversionCtx *ctx, const torch::jit::Node *n, \
32-
args &args) -> bool { return act(ctx, n, args); }}) \
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[](ConversionCtx* ctx, const torch::jit::Node* n, \
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args& args) -> bool { return act(ctx, n, args); }}) \
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.pattern({"aten::" #act "_(Tensor(a!) self) -> (Tensor(a!))", \
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[](ConversionCtx *ctx, const torch::jit::Node *n, \
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args &args) -> bool { return act(ctx, n, args); }});
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[](ConversionCtx* ctx, const torch::jit::Node* n, \
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args& args) -> bool { return act(ctx, n, args); }});
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//TODO: remove support for conversion of implace operators and move to the functionalization pass
3838

@@ -41,6 +41,51 @@ convert(sigmoid, kSIGMOID);
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convert(tanh, kTANH);
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#undef convert
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auto acthardtanh TRTORCH_UNUSED = RegisterNodeConversionPatterns()
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.pattern({
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"aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor)",
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[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
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auto in = args[0].ITensor();
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auto min = args[1].unwrapToDouble();
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auto max = args[2].unwrapToDouble();
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auto new_layer = ctx->net->addActivation(*in, nvinfer1::ActivationType::kCLIP);
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TRTORCH_CHECK(new_layer, "Unable to create layer for aten::hardtanh");
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new_layer->setAlpha(min);
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new_layer->setBeta(max);
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new_layer->setName(util::node_info(n).c_str());
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auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
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LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
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return true;
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}
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}).pattern({
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//TODO: Remove after functionalization
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"aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor(a!))",
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[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
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auto in = args[0].ITensor();
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auto min = args[1].unwrapToDouble();
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auto max = args[2].unwrapToDouble();
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auto new_layer = ctx->net->addActivation(*in, nvinfer1::ActivationType::kCLIP);
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TRTORCH_CHECK(new_layer, "Unable to create layer for aten::hardtanh");
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new_layer->setAlpha(min);
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new_layer->setBeta(max);
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new_layer->setName(util::node_info(n).c_str());
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auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
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LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
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return true;
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}
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});
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} // namespace
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} // namespace impl
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} // namespace converters

‎tests/core/converters/test_activation.cpp‎

Lines changed: 49 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,7 @@ TEST(Converters, ATenReLUConvertsCorrectly) {
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0]));
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
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}
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TEST(Converters, ATenSigmoidConvertsCorrectly) {
@@ -41,7 +41,7 @@ TEST(Converters, ATenSigmoidConvertsCorrectly) {
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
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44-
ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0]));
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
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}
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TEST(Converters, ATenTanhConvertsCorrectly) {
@@ -61,5 +61,51 @@ TEST(Converters, ATenTanhConvertsCorrectly) {
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
6363

64-
ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0]));
64+
ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
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}
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//TODO: Seems like the IR parser is not handling negative numbers well, need to follow up with the PyTorch Team
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// TEST(Converters, ATenHardTanhConvertsCorrectly) {
69+
// const auto graph = R"IR(
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// graph(%0 : Tensor):
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// %1 : float = prim::Constant[value=-1.0]()
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// %2 : float = prim::Constant[value=1.0]()
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// %3 : Tensor = aten::hardtanh(%0, %1, %2)
74+
// return (%3))IR";
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// auto g = std::make_shared<torch::jit::Graph>();
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// torch::jit::script::parseIR(graph, &*g);
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// auto in = at::randint(-5, 5, {5}, {at::kCUDA});
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// auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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// auto jit_results = trtorch::tests::util::RunGraph(g, params, {in});
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// in = at::clone(in);
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// params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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// auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
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// ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
88+
// }
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TEST(Converters, ATenHardTanhCustomRangeConvertsCorrectly) {
91+
const auto graph = R"IR(
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graph(%0 : Tensor):
93+
%1 : float = prim::Constant[value=0.0]()
94+
%2 : float = prim::Constant[value=6.0]()
95+
%3 : Tensor = aten::hardtanh(%0, %1, %2)
96+
return (%3))IR";
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auto g = std::make_shared<torch::jit::Graph>();
99+
torch::jit::script::parseIR(graph, &*g);
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auto in = at::randint(-5, 5, {5}, {at::kCUDA});
102+
auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto jit_results = trtorch::tests::util::RunGraph(g, params, {in});
104+
105+
in = at::clone(in);
106+
params = trtorch::core::conversion::get_named_params(g->inputs(), {});
107+
auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in});
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
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}
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