Checkpoint, initial test and implementation

Author: mfeliz-cruise

core/conversion/converters/impl/constant_pad.cpp

@@ -21,7 +21,8 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
        auto padding = args[1].unwrapToIntList().vec();
        int64_t padSize = padding.size();
        auto value = args[2].unwrapToScalar().to<float>();
-
+       at::Tensor value_tensor = torch::tensor(value, util::TRTDataTypeToScalarType(in->getType()));
+       auto valueTensor = tensor_to_const(ctx, value_tensor);
        TORCHTRT_CHECK(padSize % 2 == 0, "Length of pad must be even but instead it equals " << padSize);
 
        int64_t l_pad = padSize / 2;
@@ -55,8 +56,6 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
              auto fill_layer = ctx->net->addFill(nvinfer1::Dims{1, {1}}, nvinfer1::FillOperation::kLINSPACE);
              auto shape_gather_out = ctx->net->addShape(*left_gather_out)->getOutput(0);
              fill_layer->setInput(0, *shape_gather_out);
-             at::Tensor value_tensor = torch::tensor(value, torch::kFloat32);
-             auto valueTensor = tensor_to_const(ctx, value_tensor);
              fill_layer->setInput(1, *valueTensor);
              at::Tensor delta_tensor = torch::zeros(inRank);
              auto deltaTensor = tensor_to_const(ctx, delta_tensor);
@@ -69,8 +68,6 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
            } else {
              inDims.d[axis] = padding[padding_index];
              auto fill_layer = ctx->net->addFill(inDims, nvinfer1::FillOperation::kLINSPACE);
-             at::Tensor value_tensor = torch::tensor(value, torch::kFloat32);
-             auto valueTensor = tensor_to_const(ctx, value_tensor);
              fill_layer->setInput(1, *valueTensor);
              at::Tensor delta_tensor = torch::zeros(inRank);
              auto deltaTensor = tensor_to_const(ctx, delta_tensor);
@@ -111,9 +108,7 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
              // fill the right_gather_out with value
              auto fill_layer = ctx->net->addFill(nvinfer1::Dims{1, {1}}, nvinfer1::FillOperation::kLINSPACE);
              auto shape_gather_out = ctx->net->addShape(*right_gather_out)->getOutput(0);
-             fill_layer->setInput(0, *shape_gather_out);
-             at::Tensor value_tensor = torch::tensor(value, torch::kFloat32);
-             auto valueTensor = tensor_to_const(ctx, value_tensor);
+             fill_layer->setInput(0, *shape_gather_out);          
              fill_layer->setInput(1, *valueTensor);
              at::Tensor delta_tensor = torch::zeros(inRank);
              auto deltaTensor = tensor_to_const(ctx, delta_tensor);
@@ -126,8 +121,6 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
            } else {
              inDims.d[axis] = padding[padding_index + 1];
              auto fill_layer = ctx->net->addFill(inDims, nvinfer1::FillOperation::kLINSPACE);
-             at::Tensor value_tensor = torch::tensor(value, torch::kFloat32);
-             auto valueTensor = tensor_to_const(ctx, value_tensor);
              fill_layer->setInput(1, *valueTensor);
              at::Tensor delta_tensor = torch::zeros(inRank);
              auto deltaTensor = tensor_to_const(ctx, delta_tensor);

tests/core/conversion/converters/test_constant_pad.cpp

@@ -28,6 +28,29 @@ TEST(Converters, ATenConstantPad1dTensorConvertsCorrectly) {
       torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
 }
 
+TEST(Converters, ATenConstantPad1dIntTensorConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%0 : Tensor):
+        %1 : int[] = prim::Constant[value=[2, 3]]()
+        %2 : Scalar = prim::Constant[value=2]()
+        %3 : Tensor = aten::constant_pad_nd(%0, %1, %2)
+        return (%3))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in1 = at::randint(1, 10, {1, 3, 4}, {at::kCUDA}).toType(at::kInt);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in1});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in1});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
 TEST(Converters, ATenConstantPad1dRightZeroTensorConvertsCorrectly) {
   const auto graph = R"IR(
       graph(%0 : Tensor):