Merge pull request #299 from NVIDIA/leaky_relu

support leaky_relu converter/test_case

Author: narendasan

core/conversion/converters/impl/activation.cpp

@@ -124,7 +124,35 @@ auto acthardtanh TRTORCH_UNUSED =
                out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], out_tensor);
                LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
                return true;
-             }});
+             }})
+        .pattern({"aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> (Tensor)",
+                  [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+                    auto self = args[0].ITensorOrFreeze(ctx);
+                    auto negative_slopeScalar = args[1].unwrapToScalar().to<float>();
+
+                    auto new_layer = ctx->net->addActivation(*self, nvinfer1::ActivationType::kLEAKY_RELU);
+                    new_layer->setAlpha(negative_slopeScalar);
+
+                    new_layer->setName(util::node_info(n).c_str());
+                    auto out_tensor = new_layer->getOutput(0);
+                    out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], out_tensor);
+                    LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
+                    return true;
+                  }})
+        .pattern({"aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)",
+                  [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+                    auto self = args[0].ITensorOrFreeze(ctx);
+                    auto negative_slopeScalar = args[1].unwrapToScalar().to<float>();
+
+                    auto new_layer = ctx->net->addActivation(*self, nvinfer1::ActivationType::kLEAKY_RELU);
+                    new_layer->setAlpha(negative_slopeScalar);
+
+                    new_layer->setName(util::node_info(n).c_str());
+                    auto out_tensor = new_layer->getOutput(0);
+                    out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], out_tensor);
+                    LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
+                    return true;
+                  }});
 
 } // namespace
 } // namespace impl

tests/core/conversion/converters/test_activation.cpp

@@ -156,3 +156,24 @@ TEST(Converters, ATenPReLUMultiChannelConvertsCorrectly) {
 
   ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
 }
+
+TEST(Converters, ATenLeakyReluConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%0 : Tensor):
+        %1 : float = prim::Constant[value=0.15]()
+        %2 : Tensor = aten::leaky_relu(%0, %1)
+        return (%2))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, &*g);
+
+  auto in = at::randint(-5, 5, {5}, {at::kCUDA});
+  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], 2e-6));
+}