feat(aten::cat): Implements aten::cat and completes support for SSD

Signed-off-by: Naren Dasan <naren@narendasan.com>
Signed-off-by: Naren Dasan <narens@nvidia.com>

Author: narendasan

core/conversion/converters/BUILD

@@ -33,7 +33,8 @@ cc_library(
     deps = [
         "@tensorrt//:nvinfer",
         "//core/util:prelude",
-        "//core/conversion/arg",
+        "//core/conversion/var",
+        "//core/conversion/tensorcontainer",
         "//core/conversion/conversionctx",
     ] + select({
         ":use_pre_cxx11_abi":  ["@libtorch_pre_cxx11_abi//:libtorch"],

core/conversion/converters/impl/concat.cpp

@@ -0,0 +1,47 @@
+#include "core/util/prelude.h"
+#include "core/conversion/converters/converters.h"
+#include "core/conversion/tensorcontainer/TensorContainer.h"
+
+namespace trtorch {
+namespace core {
+namespace conversion {
+namespace converters {
+namespace impl {
+namespace {
+auto cat_registrations = RegisterNodeConversionPatterns()
+    .pattern({
+        "aten::cat(Tensor[] tensors, int dim=0) -> Tensor",
+        [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+            auto ts = args[0].IValue()->toListRef();
+            auto dim = args[1].unwrapToInt();
+
+            std::vector<nvinfer1::ITensor*> tensors;
+            for (auto t : ts) {
+                std::cout << t << std::endl;
+                if (t.isTensor()) {
+                    auto torch_tensor = t.toTensor();
+                    auto t_weights = Weights(ctx, torch_tensor);
+                    auto const_layer = ctx->net->addConstant(t_weights.shape, t_weights.data);
+                    tensors.push_back(const_layer->getOutput(0));
+                } else {
+                    auto cont = t.toCustomClass<TensorContainer>();
+                    tensors.push_back(cont->tensor());
+                }
+            }
+
+            auto cat_layer = ctx->net->addConcatenation(tensors.data(), tensors.size());
+            cat_layer->setAxis(static_cast<int>(dim));
+            auto cat_out = ctx->AssociateValueAndTensor(n->outputs()[0], cat_layer->getOutput(0));
+
+            LOG_DEBUG("Output tensor shape: " << cat_out->getDimensions());
+
+            return true;
+        }
+    });
+} // namespace
+} // namespace impl
+} // namespace converters
+} // namespace conversion
+} // namespace core
+} // namespace trtorch
+

core/conversion/evaluators/prim.cpp

@@ -74,8 +74,12 @@ auto prim_registrations = RegisterNodeEvaluators()
                 auto list = c10::impl::GenericList(elementType);
                 list.reserve(num_inputs);
                 for (auto in : n->inputs()) {
-                    auto x = torch::make_custom_class<TensorContainer>(reinterpret_cast<int64_t>(args.at(in).ITensor()));
-                    list.emplace_back(std::move(x));
+                    if (args.at(in).isITensor()) {
+                        auto x = torch::make_custom_class<TensorContainer>(reinterpret_cast<int64_t>(args.at(in).ITensor()));
+                        list.emplace_back(std::move(x));
+                    } else {
+                        list.emplace_back(std::move(args.at(in).unwrapToTensor()));
+                    }
                 }
                 return c10::optional<torch::jit::IValue>(std::move(torch::jit::IValue(list)));
             }

tests/core/converters/BUILD

@@ -15,6 +15,10 @@ converter_test(
   name = "test_batch_norm"
 )
 
+converter_test(
+  name = "test_concat"
+)
+
 converter_test(
   name = "test_conv_deconv"
 )

tests/core/converters/test_concat.cpp

@@ -0,0 +1,53 @@
+#include <string>
+#include "gtest/gtest.h"
+#include "torch/csrc/jit/ir/irparser.h"
+#include "tests/util/util.h"
+#include "core/compiler.h"
+
+TEST(Converters, ATenCatPureTensorConvertsCorrectly) {
+    const auto graph = R"IR(
+      graph(%0 : Tensor,
+            %1 : Tensor):
+        %2 : Tensor[] = prim::ListConstruct(%0, %1)
+        %3 : int = prim::Constant[value=0]()
+        %4 : Tensor = aten::cat(%2, %3)
+        return (%4))IR";
+
+    auto g = std::make_shared<torch::jit::Graph>();
+    torch::jit::parseIR(graph, &*g);
+
+    auto in1 = at::randint(1, 10, {5}, {at::kCUDA});
+    auto in2 = at::randint(1, 10, {5}, {at::kCUDA});
+
+    auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
+    auto jit_results = trtorch::tests::util::RunGraph(g, params, {in1, in2});
+
+    params = trtorch::core::conversion::get_named_params(g->inputs(), {});
+    auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in1, in2});
+
+    ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenCatDiffTensorConvertsCorrectly) {
+    const auto graph = R"IR(
+      graph(%0 : Tensor,
+            %1 : Float(5)):
+        %2 : Tensor[] = prim::ListConstruct(%0, %1)
+        %3 : int = prim::Constant[value=0]()
+        %4 : Tensor = aten::cat(%2, %3)
+        return (%4))IR";
+
+    auto g = std::make_shared<torch::jit::Graph>();
+    torch::jit::parseIR(graph, &*g);
+
+    auto in1 = at::randint(1, 10, {5}, {at::kCUDA});
+    auto in2 = at::randint(1, 10, {5}, {at::kCUDA});
+
+    auto params = trtorch::core::conversion::get_named_params(g->inputs(), {in2});
+    auto jit_results = trtorch::tests::util::RunGraph(g, params, {in1});
+
+    params = trtorch::core::conversion::get_named_params(g->inputs(), {in2});
+    auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {in1});
+
+    ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}