fix: Resolve compilation bug for empty tensors in aten::select

core/conversion/converters/impl/select.cpp

@@ -149,8 +149,26 @@ auto select_registrations TORCHTRT_UNUSED =
                  // IShuffleLayer removes redundant dimensions
                  auto shuffle_layer = ctx->net->addShuffle(*out);
                  TORCHTRT_CHECK(shuffle_layer, "Unable to create shuffle layer from node: " << *n);
-                 shuffle_layer->setReshapeDimensions(
-                     util::squeezeDims(out->getDimensions(), dim, !ctx->input_is_dynamic));
+
+                 auto num_zero_dimensions =
+                     util::validateInputDimsForShuffle(out->getDimensions(), ctx->input_is_dynamic);
+                 TORCHTRT_CHECK(
+                     num_zero_dimensions >= 0,
+                     "Detected multiple zero dimensions and dynamic shape in aten::select, "
+                         << "which is not currently supported in TensorRT");
+
+                 // If the input is not dynamic, and the tensor is empty (has some dimension 0)
+                 // Then 0 is no longer a placeholder for inherited dimensions
+                 if (!ctx->input_is_dynamic && (num_zero_dimensions > 0)) {
+                   LOG_DEBUG("Setting zero as a true dimension (not placeholder) in aten::select");
+                   shuffle_layer->setZeroIsPlaceholder(false);
+                 }
+
+                 shuffle_layer->setReshapeDimensions(util::squeezeDims(
+                     out->getDimensions(),
+                     dim,
+                     ctx->input_is_dynamic,
+                     ctx->input_is_dynamic && (num_zero_dimensions > 0)));
                  shuffle_layer->setName(util::node_info(n).c_str());
                  out = shuffle_layer->getOutput(0);
                }

core/util/trt_util.cpp

@@ -180,15 +180,50 @@ nvinfer1::Dims unsqueezeDims(const nvinfer1::Dims& d, int pos, int val, bool use
   return dims;
 }
 
-nvinfer1::Dims squeezeDims(const nvinfer1::Dims& d, int pos, bool use_zeros) {
+int validateInputDimsForShuffle(const nvinfer1::Dims& d, bool input_is_dynamic) {
+  int num_zeros_detected = 0;
+
+  // For each dimension, increment counter if that dimension has value 0
+  for (int i = 0; i < d.nbDims; i++) {
+    if (d.d[i] == 0) {
+      num_zeros_detected++;
+    }
+  }
+
+  // If the tensor from which the dimensions originate has dynamic shape and more than 1
+  // zero dimension is detected, this constitutes an invalid shape to the TRT Shuffle Layer,
+  // since dynamic dimensions to Shuffle Layers are generally represented with a 0
+  // denoting to inherit the dimension from the input tensor, thus causing an
+  // overload of the "0" dimension
+  return (input_is_dynamic && num_zeros_detected > 1) ? -1 : num_zeros_detected;
+}
+
+nvinfer1::Dims squeezeDims(const nvinfer1::Dims& d, int pos, bool use_zeros, bool swap_existing_zeros) {
   // acceptable range for pos is [0, d.nbDims]
   TORCHTRT_ASSERT(pos >= 0 && pos <= d.nbDims, "ERROR: Index to squeeze is out of bounds.");
 
   nvinfer1::Dims dims;
   int j = 0;
   for (int i = 0; i < d.nbDims; i++) {
     if (i != pos) {
-      dims.d[j++] = (use_zeros && d.d[i] == -1) ? 0 : d.d[i];
+      // If zeros are replacing dynamic/existing dimensions,
+      // Replace all instances of -1, indicating dynamic dimension
+      // with 0, indicating copy the dimension from another tensor
+      // (Generally used for reshape operations)
+      if (use_zeros && d.d[i] == -1) {
+        dims.d[j] = 0;
+        // If zeros already exist in the dimensions (empty tensor),
+        // Replace all instances of 0, indicating empty dimension
+        // with -1, indicating inherit the dimension from reshape
+        // (Generally used for reshape operations)
+      } else if (swap_existing_zeros && d.d[i] == 0) {
+        dims.d[j] = -1;
+        // Otherwise, replace the dimension with the same value from the input
+      } else {
+        dims.d[j] = d.d[i];
+      }
+
+      j++;
     }
   }
   dims.nbDims = j;

core/util/trt_util.h

@@ -135,8 +135,9 @@ nvinfer1::Dims toDimsPad(c10::List<int64_t> l, uint64_t pad_to);
 nvinfer1::Dims toDimsTailPad(c10::IntArrayRef l, uint64_t pad_to);
 nvinfer1::Dims toDimsTailPad(c10::List<int64_t> l, uint64_t pad_to);
 nvinfer1::Dims unpadDims(const nvinfer1::Dims& d);
+int validateInputDimsForShuffle(const nvinfer1::Dims& d, bool input_is_dynamic);
 nvinfer1::Dims unsqueezeDims(const nvinfer1::Dims& d, int pos, int val = 1, bool use_zeros = true);
-nvinfer1::Dims squeezeDims(const nvinfer1::Dims& d, int pos, bool use_zeros = true);
+nvinfer1::Dims squeezeDims(const nvinfer1::Dims& d, int pos, bool use_zeros = true, bool swap_existing_zeros = false);
 nvinfer1::Dims squeezeAllDims(const nvinfer1::Dims& d, bool use_zeros_for_unknown_dims = true);
 nvinfer1::Dims toDims(c10::IntArrayRef l);
 nvinfer1::Dims toDims(c10::List<int64_t> l);

tests/core/conversion/converters/test_select.cpp

@@ -140,6 +140,31 @@ TEST(Converters, ATenSelectIntTwiceConvertsCorrectly) {
   ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
 }
 
+TEST(Converters, ATenSelectEmptyTensorConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%0 : Tensor):
+        %2 : int = prim::Constant[value=1]()
+        %3 : int = prim::Constant[value=0]()
+        %4 : Tensor = aten::select(%0, %3, %2)
+        return (%4))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = torch::ones({2, 20, 0, 768}).to(at::kCUDA);
+
+  auto jit_in = at::clone(in);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {jit_in});
+
+  auto trt_in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {trt_in});
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::sameShape(jit_results[0], trt_results[0]));
+}
+
 TEST(Converters, ATenNarrowStartScalarConvertsCorrectly) {
   const auto graph = R"IR(
       graph(%x.1 : Tensor):

tests/util/util.cpp

@@ -28,6 +28,20 @@ bool almostEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor,
   return result <= threshold;
 }
 
+bool sameShape(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor) {
+  if (computed_tensor.sizes().size() != gt_tensor.sizes().size()) {
+    return false;
+  }
+
+  for (size_t i = 0; i < computed_tensor.sizes().size(); i++) {
+    if (computed_tensor.sizes()[i] != gt_tensor.sizes()[i]) {
+      return false;
+    }
+  }
+
+  return true;
+}
+
 bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold) {
   torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(
       computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));

tests/util/util.h

@@ -21,6 +21,8 @@ bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tens
 
 bool almostEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float atol = ATOL, float rtol = RTOL);
 
+bool sameShape(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor);
+
 bool exactlyEqual(const at::Tensor& a, const at::Tensor& b);
 
 void pointwise_test_helper(