Merge pull request #1320 from pytorch/master

Update release
pytorch · Aug 29, 2022 · 4ed2aac · 4ed2aac
2 parents 9e86efb + 81f2a5c
commit 4ed2aac
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diff --git a/.circleci/config.yml b/.circleci/config.yml
diff --git a/README.md b/README.md
@@ -113,10 +113,10 @@ torch.jit.save(trt_ts_module, "trt_torchscript_module.ts") # save the TRT embedd
 These are the following dependencies used to verify the testcases. Torch-TensorRT can work with other versions, but the tests are not guaranteed to pass.
 
 - Bazel 5.2.0
-- Libtorch 1.12.0 (built with CUDA 11.3)
-- CUDA 11.3
+- Libtorch 1.12.1 (built with CUDA 11.6)
+- CUDA 11.6
 - cuDNN 8.4.1
-- TensorRT 8.4.1.5
+- TensorRT 8.4.3.1
 
 ## Prebuilt Binaries and Wheel files
 

diff --git a/WORKSPACE b/WORKSPACE
@@ -56,17 +56,17 @@ new_local_repository(
 http_archive(
     name = "libtorch",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "80f089939de20e68e3fcad4dfa72a26c8bf91b5e77b11042f671f39ebac35865",
+    sha256 = "5a392132fbff9db1482eae72a30f74b09f53a47edf8305fe9688d4ce7ddb0b6b",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.12.0%2Bcu113.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu116/libtorch-cxx11-abi-shared-with-deps-1.12.1%2Bcu116.zip"],
 )
 
 http_archive(
     name = "libtorch_pre_cxx11_abi",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "8e35371403f7052d9e9b43bcff383980dbde4df028986dc1dab539953481d55f",
+    sha256 = "5e044cc56a29cd4f3a7198c0fe5b2f0fa8f4c38cd71a0491274b6a914e8f24a7",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-shared-with-deps-1.12.0%2Bcu113.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu116/libtorch-shared-with-deps-1.12.1%2Bcu116.zip"],
 )
 
 # Download these tarballs manually from the NVIDIA website
@@ -86,10 +86,10 @@ http_archive(
 http_archive(
     name = "tensorrt",
     build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "8107861af218694130f170e071f49814fa3e27f1386ce7cb6d807ac05a7fcf0e",
-    strip_prefix = "TensorRT-8.4.1.5",
+    sha256 = "8d7c2085c1639dcc73875048c23598a8526ce3089136876e31d90258e49e4f61",
+    strip_prefix = "TensorRT-8.4.3.1",
     urls = [
-        "https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.4.1/tars/tensorrt-8.4.1.5.linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz",
+        "https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.4.3/tars/tensorrt-8.4.3.1.linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz",
     ],
 )
 

diff --git a/core/conversion/converters/impl/expand.cpp b/core/conversion/converters/impl/expand.cpp
@@ -282,6 +282,116 @@ auto expand_registrations TORCHTRT_UNUSED =
                auto out = ctx->AssociateValueAndTensor(n->outputs()[0], in);
 
                LOG_DEBUG("Repeat layer output tensor shape: " << out->getDimensions());
+               return true;
+             }})
+        .pattern(
+            {"aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, *, int? output_size=None) -> (Tensor)",
+             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+               auto self = args[0].ITensorOrFreeze(ctx);
+               auto repeats = args[1].unwrapToScalar().to<int>();
+
+               auto input_shape = self->getDimensions();
+
+               int dim;
+               if (args[2].IValue()->isNone()) {
+                 dim = 0;
+
+                 // Flatten self tensor
+                 int size;
+                 if (ctx->input_is_dynamic) {
+                   // Set size to -1 if input is dynamic
+                   size = -1;
+                 } else {
+                   size = 1;
+                   for (int i = 0; i < input_shape.nbDims; i++) {
+                     size *= input_shape.d[i];
+                   }
+                 }
+                 auto flatten = ctx->net->addShuffle(*self);
+                 TORCHTRT_CHECK(flatten, "Unable to create shuffle layer from node: " << *n);
+                 flatten->setReshapeDimensions(util::toDims(std::vector<int64_t>({size})));
+                 self = flatten->getOutput(0);
+                 input_shape = self->getDimensions();
+               } else {
+                 dim = args[2].unwrapToScalar().to<int>();
+               }
+
+               if (ctx->input_is_dynamic) {
+                 int dynamic_dims = 0;
+                 for (int idx = 0; idx < input_shape.nbDims; idx++) {
+                   if (input_shape.d[idx] == -1) {
+                     dynamic_dims++;
+                   }
+                 }
+
+                 if (dynamic_dims > 1) {
+                   TORCHTRT_THROW_ERROR(
+                       "Repeat_interleave is currently not supported when target shape contains more than one dynamic dimension");
+                 }
+               }
+
+               // Insert singleton dimension after desired repeat dimension
+               std::vector<int64_t> repeat_shape_vec;
+               for (int j = 0; j < input_shape.nbDims; j++) {
+                 repeat_shape_vec.push_back(input_shape.d[j]);
+                 if (j == dim) {
+                   repeat_shape_vec.push_back(1);
+                 }
+               }
+               auto expand = ctx->net->addShuffle(*self);
+               TORCHTRT_CHECK(expand, "Unable to create shuffle layer from node: " << *n);
+               auto repeat_shape_dims = util::toDims(repeat_shape_vec);
+               expand->setReshapeDimensions(repeat_shape_dims);
+
+               // Expand on newly created singleton dimension
+               repeat_shape_dims.d[dim + 1] = repeats;
+               std::vector<int64_t> start_vec(repeat_shape_dims.nbDims, 0);
+               auto start_dims = util::toDims(start_vec);
+
+               std::vector<int64_t> strides_vec(repeat_shape_dims.nbDims, 1);
+               strides_vec[dim + 1] = 0;
+               auto strides_dims = util::toDims(strides_vec);
+
+               auto slice = ctx->net->addSlice(*expand->getOutput(0), start_dims, repeat_shape_dims, strides_dims);
+
+               if (ctx->input_is_dynamic) {
+                 auto start_tensor = tensor_to_const(ctx, torch::tensor(start_vec, torch::kInt32));
+
+                 auto expand_output_shape = ctx->net->addShape(*expand->getOutput(0))->getOutput(0);
+                 std::vector<int64_t> repeat_const_vec(repeat_shape_dims.nbDims, 1);
+                 repeat_const_vec[dim + 1] = repeats;
+                 auto repeat_const = tensor_to_const(ctx, torch::tensor(repeat_const_vec, torch::kInt32));
+                 auto repeat_shape_tensor =
+                     ctx->net
+                         ->addElementWise(*expand_output_shape, *repeat_const, nvinfer1::ElementWiseOperation::kPROD)
+                         ->getOutput(0);
+
+                 auto strides_tensor = tensor_to_const(ctx, torch::tensor(strides_vec, torch::kInt32));
+                 slice->setInput(1, *start_tensor);
+                 slice->setInput(2, *repeat_shape_tensor);
+                 slice->setInput(3, *strides_tensor);
+               }
+
+               // Collapse repeated dimension back into desired dimension
+               std::vector<int64_t> collapse_shape_vec;
+               for (int k = 0; k < repeat_shape_dims.nbDims; k++) {
+                 if (k == dim) {
+                   int64_t collapse_dim = repeat_shape_dims.d[k] * repeat_shape_dims.d[++k];
+                   // Set dim size to -1 if repeat is being done on dynamic dim
+                   collapse_dim = std::max(collapse_dim, (int64_t)-1);
+                   collapse_shape_vec.push_back(collapse_dim);
+                 } else {
+                   collapse_shape_vec.push_back(repeat_shape_dims.d[k]);
+                 }
+               }
+               auto collapse = ctx->net->addShuffle(*slice->getOutput(0));
+               TORCHTRT_CHECK(collapse, "Unable to create shuffle layer from node: " << *n);
+               collapse->setReshapeDimensions(util::toDims(collapse_shape_vec));
+
+               collapse->setName(util::node_info(n).c_str());
+               auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], collapse->getOutput(0));
+               LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+
                return true;
              }});