Merge branch 'pytorch:main' into main

.github/code-owners.yml

@@ -2,7 +2,8 @@
   - "narendasan"
 
 "component: api [Python]":
-  - "narendasan"
+  - "gs-olive"
+  - "peri044"
 
 "component: api":
   - "narendasan"
@@ -11,18 +12,23 @@
   - "narendasan"
 
 "component: conversion":
-  - "narendasan"
+  - "apbose"
   - "peri044"
 
 "component: converters":
-  - "peri044"
-  - "bowang007"
+  - "apbose"
+  - "zewenli98"
 
 "component: core":
   - "narendasan"
   - "peri044"
   - "bowang007"
 
+"component: dynamo":
+  - "narendasan"
+  - "gs-olive"
+  - "peri044"
+
 "component: evaluators":
   - "narendasan"
   - "peri044"
@@ -32,7 +38,7 @@
 
 "component: lowering":
   - "peri044"
-  - "narendasan"
+  - "gs-olive"
 
 "component: partitioning":
   - "bowang007"
@@ -43,13 +49,18 @@
 
 "component: quantization":
   - "peri044"
+  - "bowang007"
 
 "component: runtime":
   - "narendasan"
 
 "component: tests":
   - "narendasan"
 
+"component: torch_compile":
+  - "gs-olive"
+  - "narendasan"
+
 "component: torchtrtc":
   - "narendasan"
 

.github/pr-labels.yml

@@ -12,31 +12,43 @@
 
 "component: conversion":
   - core/conversion/**/*
-
+  - py/torch_tensorrt/dynamo/conversion/**/*
+
 "component: converters":
   - core/conversion/converters/**/*
+  - py/torch_tensorrt/dynamo/conversion/impl/**/*
 
 "component: evaluators":
   - core/conversion/evaluators/**/*
 
 "component: fx":
   - py/torch_tensorrt/fx/**/*
 
+"component: dynamo":
+  - py/torch_tensorrt/dynamo/**/*
+
+"component: torch_compile":
+  - py/torch_tensorrt/dynamo/backend/*
+
 "component: partitioning":
   - core/partitioning/**/*
 
 "component: runtime":
   - core/runtime/**/*
+  - py/torch_tensorrt/dynamo/runtime/**/*
 
 "component: lowering":
   - core/lowering/**/*
+  - py/torch_tensorrt/dynamo/lowering/**/*
 
 "component: tests":
   - tests/**/*
 
 "component: build system":
   - WORKSPACE
   - BUILD
+  - pyproject.toml
+  - setup.py
 
 "documentation":
   - docs/**/*

.github/workflows/docgen.yml

@@ -12,75 +12,30 @@ jobs:
   build-docs:
     runs-on: ubuntu-20.04
     container:
-      image: ghcr.io/pytorch/tensorrt/docgen:latest
-      credentials:
-        username: ${{ github.actor }}
-        password: ${{ secrets.GITHUB_TOKEN }}
+      image: docker.io/pytorch/manylinux-builder:cuda12.1
     steps:
-      - name: Reclaim space
-        run: |
-          rm -rf /usr/share/dotnet
-          rm -rf /opt/ghc
-          rm -rf "/usr/local/share/boost"
-          rm -rf /usr/local/cuda/cuda-*
-      - name: Install base deps
-        run: |
-          apt update
-          DEBIAN_FRONTEND=noninteractive apt install -y software-properties-common gcc git curl wget make zlib1g-dev bzip2 libbz2-dev lzma lzma-dev libreadline-dev libsqlite3-dev libssl-dev libffi-dev doxygen pandoc
-          git config --global --add safe.directory '*'
-      - name: Set up Python 3.10.12
-        uses: actions/setup-python@v4
-        with:
-          python-version: 3.10.12
       - uses: actions/checkout@v3
         with:
           ref: ${{github.head_ref}}
+      - name: Install base deps
+        run: |
+          ./packaging/pre_build_script.sh
       - name: Get HEAD SHA
         id: vars
         run: echo "sha=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT
       - name: Get Bazel version
         id: bazel_info
         run: echo "version=$(cat .bazelversion)" >> $GITHUB_OUTPUT
-      - name: Install Bazel
-        run: |
-          wget -q https://github.com/bazelbuild/bazel/releases/download/${{ steps.bazel_info.outputs.version }}/bazel-${{ steps.bazel_info.outputs.version }}-linux-x86_64 -O /usr/bin/bazel
-          chmod a+x /usr/bin/bazel
-      - name: Install cudnn + tensorrt
-        run: |
-          apt-get update
-          wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
-          mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub
-          apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 536F8F1DE80F6A35
-          apt-key adv --keyserver keyserver.ubuntu.com --recv-keys A4B469963BF863CC
-          add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
-          apt-get update
-          apt-get install -y libcudnn8 libcudnn8-dev
-
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
-          add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
-          apt-get update
-
-          apt-get install -y libnvinfer8 libnvinfer-plugin8 libnvinfer-dev libnvinfer-plugin-dev
-      - name: Install Torch
-        run: |
-          python3 -m pip install -r py/requirements.txt --user
       - name: Build Python Package
         run: |
-          cp toolchains/ci_workspaces/WORKSPACE.x86_64 WORKSPACE
-          cd py
-          python3 -m pip install pip==21.3.1
-          echo $(which python3)
-          echo $(python3 -c 'import site; print(site.getsitepackages()[0])')
-          mkdir -p /opt/circleci/.pyenv/versions/3.9.4/lib/python3.9/
-          ln -s $(python3 -c 'import site; print(site.getsitepackages()[0])') /opt/circleci/.pyenv/versions/3.9.4/lib/python3.9/site-packages
-          python3 setup.py install
-          cd ..
+          cp toolchains/ci_workspaces/WORKSPACE.x86_64.cu121.release.rhel WORKSPACE
+          python -m pip install pip<=23
+          python -m pip install --pre -e . --extra-index-url https://download.pytorch.org/whl/nightly/cu121
       - name: Generate New Docs
         run: |
           cd docsrc
-          python3 -m pip install -r requirements.txt
-          python3 -c "import torch_tensorrt; print(torch_tensorrt.__version__)"
+          python -m pip install -r requirements.txt
+          python -c "import torch_tensorrt; print(torch_tensorrt.__version__)"
           make html
           cd ..
       - uses: stefanzweifel/git-auto-commit-action@v4

.github/workflows/docker_builder.yml

@@ -0,0 +1,62 @@
+name: 'Torch-TensorRT Docker Build'
+
+# Apply workflow only to main branch
+on:
+  push:
+    branches:
+      - main
+      - nightly
+
+# If pushes to main are made in rapid succession,
+# cancel existing docker builds and use newer commits
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref_name }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    runs-on: linux.2xlarge
+
+    # Define key environment variables
+    # Container name is of the form torch_tensorrt:<branch_name>
+    env:
+      DOCKER_REGISTRY: ghcr.io/pytorch/tensorrt
+      CONTAINER_NAME: torch_tensorrt:${{ github.ref_name }}
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v3
+
+    - name: Log in to the Container registry
+      uses: docker/login-action@v2
+      with:
+        registry: ${{ env.DOCKER_REGISTRY }}
+        username: ${{ github.actor }}
+        password: ${{ secrets.GITHUB_TOKEN }}
+
+    # Automatically detect TensorRT and cuDNN default versions for Torch-TRT build
+    - name: Build Docker image
+      env:
+        DOCKER_TAG: ${{ env.DOCKER_REGISTRY }}/${{ env.CONTAINER_NAME }}
+      run: |
+        python3 -m pip install pyyaml
+        TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
+        echo "TRT VERSION = ${TRT_VERSION}"
+        CUDNN_VERSION=$(python3 -c "import versions; versions.cudnn_version()")
+        echo "CUDNN VERSION = ${CUDNN_VERSION}"
+
+        DOCKER_BUILDKIT=1 docker build --build-arg TENSORRT_VERSION=$TRT_VERSION --build-arg CUDNN_VERSION=$CUDNN_VERSION -f docker/Dockerfile --tag $DOCKER_TAG .
+
+    - name: Push Docker image
+      env:
+        DOCKER_URL: ${{ env.DOCKER_REGISTRY }}/${{ env.CONTAINER_NAME }}
+      run: docker push $DOCKER_URL
+
+    # Clean up all untagged containers in registry
+    - name: Container Registry Cleanup
+      uses: actions/delete-package-versions@v4
+      with:
+        package-name: "tensorrt/torch_tensorrt"
+        package-type: container
+        min-versions-to-keep: 0
+        delete-only-untagged-versions: True

.gitignore

@@ -32,6 +32,7 @@ docsrc/_build
 docsrc/_notebooks
 docsrc/_cpp_api
 docsrc/_tmp
+docsrc/tutorials/_rendered_examples
 *.so
 __pycache__
 *.egg-info
@@ -67,4 +68,4 @@ bazel-tensorrt
 *cifar-10-batches-py*
 bazel-project
 build/
-wheelhouse/
+wheelhouse/

core/conversion/converters/impl/layer_norm.cpp

@@ -20,8 +20,8 @@ auto layer_norm_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
 
       /* Layer_Norm normalizes over last N dimensions.
          normalizaed_shape could be (C,H,W), (H,W), or (W). */
-      auto normalized_shape = args[1].unwrapToIntList();
-      auto normalized_shape_vec = util::toVec(util::toDims(normalized_shape));
+      // This could be an IntList or ITensorList. We only need the size of this list.
+      auto normalized_shape = args[1].IValue()->toList();
 
       // Unwrap eps.
       auto eps = args[4].unwrapToDouble();
@@ -30,7 +30,7 @@ auto layer_norm_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
 
       // Set up  axis_ask for E[x].
       uint32_t axis_mask = 0;
-      for (size_t i = 0; i < normalized_shape_vec.size(); i++) {
+      for (size_t i = 0; i < normalized_shape.size(); i++) {
         axis_mask |= 1 << (shape.size() - i - 1);
       }
       LOG_DEBUG("Axis Mask for E[x]" << std::bitset<32>(axis_mask));

core/conversion/converters/impl/quantization.cpp

@@ -11,6 +11,22 @@ namespace {
 
 #if NV_TENSORRT_MAJOR > 7
 // clang-format off
+
+bool add_qdq(ConversionCtx *ctx, const torch::jit::Node* n, nvinfer1::ITensor* input, nvinfer1::ITensor* scale, std::string& opName) {
+  nvinfer1::IQuantizeLayer* quantize_layer = ctx->net->addQuantize(*input, *scale);
+  TORCHTRT_CHECK(quantize_layer, "Unable to create QuantizeLayer from node: " << *n);
+  quantize_layer->setAxis(0);
+
+  nvinfer1::IDequantizeLayer* dequantize_layer = ctx->net->addDequantize(*quantize_layer->getOutput(0), *scale);
+  TORCHTRT_CHECK(dequantize_layer, "Unable to create DequantizeLayer from node: " << *n);
+  dequantize_layer->setAxis(0);
+
+  auto qdq_out = ctx->AssociateValueAndTensor(n->outputs()[0], dequantize_layer->getOutput(0));
+  LOG_DEBUG("[" << opName << "]"<< " Output tensor shape: " << qdq_out->getDimensions());
+
+  return true;
+}
+
 auto quantization_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
   .pattern({"aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor)",
             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -20,18 +36,16 @@ auto quantization_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
               auto scale = args[1].unwrapToScalar().to<float>();
               auto scaleTensor = tensor_to_const(ctx, torch::tensor({scale}));
               // Add and configure a QuantizeLayer.
-              nvinfer1::IQuantizeLayer* quantize_layer = ctx->net->addQuantize(*input, *scaleTensor);
-              quantize_layer->setAxis(0);
-
-              // Add and configure DequantizeLayer following a QuantizeLayer
-              nvinfer1::IDequantizeLayer* dequantize_layer = ctx->net->addDequantize(*quantize_layer->getOutput(0), *scaleTensor);
-              dequantize_layer->setAxis(0);
-
-              auto qdq_out = ctx->AssociateValueAndTensor(n->outputs()[0], dequantize_layer->getOutput(0));
-              LOG_DEBUG("[fake_quantize_per_tensor_affine] Output tensor shape: " << qdq_out->getDimensions());
-
-              return true;
+              std::string opName("aten::fake_quantize_per_tensor_affine");
+              return add_qdq(ctx, n, input, scaleTensor, opName);
             }})
+  .pattern({"aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> (Tensor)",
+             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+              auto input = args[0].ITensorOrFreeze(ctx);
+              auto scale = args[1].ITensorOrFreeze(ctx);
+              std::string opName("aten::fake_quantize_per_tensor_affine.tensor_qparams");
+              return add_qdq(ctx, n, input, scale, opName);
+           }})
   .pattern({"aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor)",
             [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
               // This aten operator is generated from torch.fake_quantize_per_channel_affine op in Pytorch python API.

core/conversion/converters/impl/select.cpp

@@ -165,10 +165,7 @@ auto select_registrations TORCHTRT_UNUSED =
                  }
 
                  shuffle_layer->setReshapeDimensions(util::squeezeDims(
-                     out->getDimensions(),
-                     dim,
-                     ctx->input_is_dynamic,
-                     ctx->input_is_dynamic && (num_zero_dimensions > 0)));
+                     out->getDimensions(), dim, false, ctx->input_is_dynamic && (num_zero_dimensions > 0)));
                  shuffle_layer->setName(util::node_info(n).c_str());
                  out = shuffle_layer->getOutput(0);
                }
@@ -710,9 +707,8 @@ auto select_registrations TORCHTRT_UNUSED =
                auto val_t_dtype = util::TRTDataTypeToScalarType(self->getType());
 
                // Initialize contant tensor for fill with the inherited data type
-               auto val_t = tensor_to_const(
-                   ctx, torch::full(util::toVec(self->getDimensions()), val, {torch::dtype(val_t_dtype)}));
-
+               std::vector<int64_t> singleton_dims(self->getDimensions().nbDims, 1);
+               auto val_t = tensor_to_const(ctx, torch::full(singleton_dims, val, {torch::dtype(val_t_dtype)}));
                TORCHTRT_CHECK(
                    util::broadcastable(self->getDimensions(), mask->getDimensions(), /*multidirectional=*/false),
                    "Self and mask tensors are not broadcastable");