diff --git a/.circleci/config.yml b/.circleci/config.yml index f1143f25b0..d1e36447d3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -802,7 +802,7 @@ commands: - store_artifacts: path: /tmp/testlogs - test-dynamo-models_torch_export: + test-dynamo-models_export: description: "Test the Dynamo models via torch_export path" steps: - run: @@ -818,6 +818,20 @@ commands: - store_artifacts: path: /tmp/testlogs + test-dynamo-export_serde: + description: "Test the export serialize/deserialize functionality for Dynamo models" + steps: + - run: + name: Run Dynamo models and test export serde with TRT compiled modules + command: | + cd tests/py/dynamo/models + pytest test_export_serde.py --junitxml=/tmp/artifacts/test_results/dynamo/backend/test_results.xml --ir dynamo + + - store_test_results: + path: /tmp/artifacts + - store_artifacts: + path: /tmp/testlogs + test-dynamo-converters: description: "Test the Dynamo aten converters" steps: @@ -1122,7 +1136,8 @@ jobs: - test-dynamo-backend - test-dynamo-shared_utilities - test-dynamo-models_torch_compile - - test-dynamo-models_torch_export + - test-dynamo-models_export + - test-dynamo-export_serde package-x86_64-linux: parameters: diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml index 80a9f15c94..bfc19cce45 100644 --- a/.github/workflows/build-test.yml +++ b/.github/workflows/build-test.yml @@ -141,6 +141,8 @@ jobs: cd tests/py/dynamo ${CONDA_RUN} python -m pip install --pre pytest timm transformers parameterized expecttest --use-deprecated=legacy-resolver ${CONDA_RUN} python -m pytest --junitxml=${RUNNER_TEST_RESULTS_DIR}/dynamo_fe_test_results.xml --ir dynamo models/test_models_export.py + ${CONDA_RUN} python -m pytest --junitxml=${RUNNER_TEST_RESULTS_DIR}/export_serde_test_results.xml --ir dynamo models/test_export_serde.py + ${CONDA_RUN} python -m pytest --junitxml=${RUNNER_TEST_RESULTS_DIR}/dyn_models_export.xml --ir dynamo models/test_dyn_models.py popd tests-py-torch-compile-be: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ac24623eef..4738ea80be 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -40,7 +40,7 @@ repos: rev: 'v1.4.1' hooks: - id: mypy - exclude: "^py/torch_tensorrt/fx|^examples|^tests|^tools|^docs|noxfile.py|setup.py|versions.py" + exclude: "^py/torch_tensorrt/fx|^examples|^tests|^py/torch_tensorrt/dynamo/_experimental|^tools|^docs|noxfile.py|setup.py|versions.py" - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. rev: v0.0.278 diff --git a/core/conversion/converters/impl/shuffle.cpp b/core/conversion/converters/impl/shuffle.cpp index 5a8e992d90..8d25525a6e 100644 --- a/core/conversion/converters/impl/shuffle.cpp +++ b/core/conversion/converters/impl/shuffle.cpp @@ -20,7 +20,12 @@ static auto shuffle_registrations TORCHTRT_UNUSED = auto in_shape = util::toVec(in->getDimensions()); std::vector out_shape; if (ctx->input_is_dynamic) { - end_dim = (end_dim == -1) ? in_shape.size() - 1 : end_dim; + if (start_dim < 0) { + start_dim = start_dim + in_shape.size(); + } + if (end_dim < 0) { + end_dim = end_dim + in_shape.size(); + } int nbDynamicFlattenedDims = 0; int nbDynamicUnflattenedDims = 0; for (int i = 0; i < (int)in_shape.size(); i++) { diff --git a/core/runtime/execute_engine.cpp b/core/runtime/execute_engine.cpp index c4b34cb218..2a7fe884da 100644 --- a/core/runtime/execute_engine.cpp +++ b/core/runtime/execute_engine.cpp @@ -43,8 +43,8 @@ bool is_switch_required(const RTDevice& curr_device, const RTDevice& engine_devi return false; } -RTDevice select_rt_device(const RTDevice& engine_device) { - auto new_target_device_opt = get_most_compatible_device(engine_device); +RTDevice select_rt_device(const RTDevice& engine_device, const RTDevice& curr_device) { + auto new_target_device_opt = get_most_compatible_device(engine_device, curr_device); // REVIEW: THIS DOES NOT LIST DLA PROBABLY, WHICH WE SHOULD // TODO: I think this logic could be way simpler at execution time since if the tensors arent on the right @@ -89,7 +89,7 @@ std::vector execute_engine(std::vector inputs, c10::intr if (is_switch_required(curr_device, compiled_engine->device_info)) { // Scan through available CUDA devices and set the CUDA device context correctly - RTDevice device = select_rt_device(compiled_engine->device_info); + RTDevice device = select_rt_device(compiled_engine->device_info, curr_device); set_rt_device(device); // Target device is new device diff --git a/core/runtime/runtime.cpp b/core/runtime/runtime.cpp index 0c054d8a3c..0372258919 100644 --- a/core/runtime/runtime.cpp +++ b/core/runtime/runtime.cpp @@ -7,9 +7,16 @@ namespace torch_tensorrt { namespace core { namespace runtime { -c10::optional get_most_compatible_device(const RTDevice& target_device) { +c10::optional get_most_compatible_device(const RTDevice& target_device, const RTDevice& curr_device) { LOG_DEBUG("Target Device: " << target_device); auto device_options = find_compatible_devices(target_device); + RTDevice current_device; + if (current_device.id == -1) { + current_device = get_current_device(); + } else { + current_device = curr_device; + } + if (device_options.size() == 0) { return {}; } else if (device_options.size() == 1) { @@ -21,10 +28,20 @@ c10::optional get_most_compatible_device(const RTDevice& target_device dev_list << "[" << std::endl; for (auto device : device_options) { dev_list << " " << device << ',' << std::endl; - if (device.device_name == target_device.device_name && best_match.device_name != target_device.device_name) { - best_match = device; - } else if (device.device_name == target_device.device_name && best_match.device_name == target_device.device_name) { - if (device.id == target_device.id && best_match.id != target_device.id) { + if (device.device_name == target_device.device_name) { + // First priority is selecting a candidate which agrees with the current device ID + // If such a device is found, we can select it and break out of the loop + if (device.id == current_device.id && best_match.id != current_device.id) { + best_match = device; + break; + } + // Second priority is selecting a candidate which agrees with the target device ID + // At deserialization time, the current device and target device may not agree + else if (device.id == target_device.id && best_match.id != target_device.id) { + best_match = device; + } + // If no such GPU ID is found, select the first available candidate GPU + else if (best_match.device_name != target_device.device_name) { best_match = device; } } diff --git a/core/runtime/runtime.h b/core/runtime/runtime.h index 4c7565c9fc..05d97a30b8 100644 --- a/core/runtime/runtime.h +++ b/core/runtime/runtime.h @@ -26,7 +26,9 @@ typedef enum { SERIALIZATION_LEN, // NEVER USED FOR DATA, USED TO DETERMINE LENGTH OF SERIALIZED INFO } SerializedInfoIndex; -c10::optional get_most_compatible_device(const RTDevice& target_device); +c10::optional get_most_compatible_device( + const RTDevice& target_device, + const RTDevice& curr_device = RTDevice()); std::vector find_compatible_devices(const RTDevice& target_device); std::vector execute_engine(std::vector inputs, c10::intrusive_ptr compiled_engine); diff --git a/core/util/trt_util.cpp b/core/util/trt_util.cpp index 77c88b465d..50b58a0bdb 100644 --- a/core/util/trt_util.cpp +++ b/core/util/trt_util.cpp @@ -216,7 +216,7 @@ nvinfer1::Dims squeezeDims(const nvinfer1::Dims& d, int pos, bool use_zeros, boo // 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) { + if (use_zeros && d.d[i] == -1 && i < pos) { dims.d[j] = 0; // If zeros already exist in the dimensions (empty tensor), // Replace all instances of 0, indicating empty dimension diff --git a/cpp/include/torch_tensorrt/torch_tensorrt.h b/cpp/include/torch_tensorrt/torch_tensorrt.h index 29f860c8b3..adac75d984 100644 --- a/cpp/include/torch_tensorrt/torch_tensorrt.h +++ b/cpp/include/torch_tensorrt/torch_tensorrt.h @@ -60,6 +60,8 @@ class DataType { enum Value : int8_t { /// INT64 kLong, + /// FP64 + kDouble, /// FP32 kFloat, /// FP16 diff --git a/cpp/src/types.cpp b/cpp/src/types.cpp index 2be7fea338..69b956a162 100644 --- a/cpp/src/types.cpp +++ b/cpp/src/types.cpp @@ -97,6 +97,8 @@ at::ScalarType toAtenDataType(DataType value) { return at::kInt; case DataType::kLong: return at::kLong; + case DataType::kDouble: + return at::kDouble; case DataType::kBool: return at::kBool; case DataType::kFloat: @@ -119,7 +121,8 @@ nvinfer1::TensorFormat toTRTTensorFormat(TensorFormat value) { DataType::DataType(c10::ScalarType t) { TORCHTRT_CHECK( - t == at::kHalf || t == at::kFloat || t == at::kChar || t == at::kLong || t == at::kInt || t == at::kBool, + t == at::kHalf || t == at::kFloat || t == at::kChar || t == at::kLong || t == at::kDouble || t == at::kInt || + t == at::kBool, "Data type is unsupported (" << t << ")"); switch (t) { case at::kHalf: @@ -134,6 +137,9 @@ DataType::DataType(c10::ScalarType t) { case at::kLong: value = DataType::kLong; break; + case at::kDouble: + value = DataType::kDouble; + break; case at::kBool: value = DataType::kBool; break; diff --git a/docker/WORKSPACE.ngc b/docker/WORKSPACE.ngc index a01bbed32b..c3d9bea0fc 100755 --- a/docker/WORKSPACE.ngc +++ b/docker/WORKSPACE.ngc @@ -9,24 +9,28 @@ http_archive( sha256 = "778197e26c5fbeb07ac2a2c5ae405b30f6cb7ad1f5510ea6fdac03bded96cc6f", ) -load("@rules_python//python:pip.bzl", "pip_install") +load("@rules_python//python:repositories.bzl", "py_repositories") + +py_repositories() http_archive( name = "rules_pkg", + sha256 = "8f9ee2dc10c1ae514ee599a8b42ed99fa262b757058f65ad3c384289ff70c4b8", urls = [ - "https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.4.0/rules_pkg-0.4.0.tar.gz", - "https://github.com/bazelbuild/rules_pkg/releases/download/0.4.0/rules_pkg-0.4.0.tar.gz", + "https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz", + "https://github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz", ], - sha256 = "038f1caa773a7e35b3663865ffb003169c6a71dc995e39bf4815792f385d837d", ) + load("@rules_pkg//:deps.bzl", "rules_pkg_dependencies") + rules_pkg_dependencies() -git_repository( +http_archive( name = "googletest", - remote = "https://github.com/google/googletest", - commit = "703bd9caab50b139428cea1aaff9974ebee5742e", - shallow_since = "1570114335 -0400" + sha256 = "755f9a39bc7205f5a0c428e920ddad092c33c8a1b46997def3f1d4a82aded6e1", + strip_prefix = "googletest-5ab508a01f9eb089207ee87fd547d290da39d015", + urls = ["https://github.com/google/googletest/archive/5ab508a01f9eb089207ee87fd547d290da39d015.zip"], ) # External dependency for torch_tensorrt if you already have precompiled binaries. @@ -80,17 +84,13 @@ new_local_repository( ######################################################################### # Testing Dependencies (optional - comment out on aarch64) ######################################################################### -pip_install( - name = "torch_tensorrt_py_deps", - requirements = "//py:requirements.txt", -) +load("@rules_python//python:pip.bzl", "pip_parse") -pip_install( - name = "py_test_deps", - requirements = "//tests/py:requirements.txt", +pip_parse( + name = "devtools_deps", + requirements_lock = "//:requirements-dev.txt", ) -pip_install( - name = "pylinter_deps", - requirements = "//tools/linter:requirements.txt", -) +load("@devtools_deps//:requirements.bzl", "install_deps") + +install_deps() diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html index 1a026d309c..276ad9db46 100644 --- a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html +++ b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html @@ -10,7 +10,7 @@ - Class DataType — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Class DataType — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
- v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
@@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    @@ -414,6 +417,12 @@

    Class Documentation +
    +enumerator kDouble
    +

    FP64.

    +
    +
    enumerator kFloat
    diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html b/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html index 5768cdee0b..ac20190757 100644 --- a/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html +++ b/docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html @@ -10,7 +10,7 @@ - Class Device::DeviceType — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Class Device::DeviceType — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html b/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html index d41ac517bc..8667256a0b 100644 --- a/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html +++ b/docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html @@ -10,7 +10,7 @@ - Class TensorFormat — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Class TensorFormat — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html index 09bac026e1..e4a8679553 100644 --- a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html +++ b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html @@ -10,7 +10,7 @@ - Template Class Int8CacheCalibrator — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Template Class Int8CacheCalibrator — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html index 4b3ab97753..f3b4a29783 100644 --- a/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html +++ b/docs/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html @@ -10,7 +10,7 @@ - Template Class Int8Calibrator — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Template Class Int8Calibrator — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html b/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html index 8006c0b593..17ea150421 100644 --- a/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html +++ b/docs/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html @@ -10,7 +10,7 @@ - Define STR — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define STR — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html b/docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html index 0423d8e05e..e20cb286f7 100644 --- a/docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html +++ b/docs/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html @@ -10,7 +10,7 @@ - Define TORCH_TENSORRT_PATCH_VERSION — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define TORCH_TENSORRT_PATCH_VERSION — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html b/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html index 4166d97035..aecba4f392 100644 --- a/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html +++ b/docs/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html @@ -10,7 +10,7 @@ - Define TORCH_TENSORRT_MAJOR_VERSION — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define TORCH_TENSORRT_MAJOR_VERSION — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html b/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html index e6ec77ad26..3e2bf0b936 100644 --- a/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html +++ b/docs/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html @@ -10,7 +10,7 @@ - Define TORCH_TENSORRT_MINOR_VERSION — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define TORCH_TENSORRT_MINOR_VERSION — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html b/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html index 6d9d63b9ba..a10b3666d1 100644 --- a/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html +++ b/docs/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html @@ -10,7 +10,7 @@ - Define TORCHTRT_API — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define TORCHTRT_API — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html b/docs/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html index 66c6ad1dfe..ad5b066a3e 100644 --- a/docs/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html +++ b/docs/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html @@ -10,7 +10,7 @@ - Define XSTR — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define XSTR — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html b/docs/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html index bc551e2236..97af500c2e 100644 --- a/docs/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html +++ b/docs/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html @@ -10,7 +10,7 @@ - Define TORCHTRT_HIDDEN — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define TORCHTRT_HIDDEN — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html b/docs/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html index d69ca8298b..3d2094beba 100644 --- a/docs/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html +++ b/docs/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html @@ -10,7 +10,7 @@ - Define TORCH_TENSORRT_VERSION — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Define TORCH_TENSORRT_VERSION — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/dir_cpp.html b/docs/_cpp_api/dir_cpp.html index 70b176e648..e6135c0dae 100644 --- a/docs/_cpp_api/dir_cpp.html +++ b/docs/_cpp_api/dir_cpp.html @@ -10,7 +10,7 @@ - Directory cpp — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Directory cpp — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/dir_cpp_include.html b/docs/_cpp_api/dir_cpp_include.html index 60868375ff..083bef08a3 100644 --- a/docs/_cpp_api/dir_cpp_include.html +++ b/docs/_cpp_api/dir_cpp_include.html @@ -10,7 +10,7 @@ - Directory include — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Directory include — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/dir_cpp_include_torch_tensorrt.html b/docs/_cpp_api/dir_cpp_include_torch_tensorrt.html index 53b9722a0f..3f1def703f 100644 --- a/docs/_cpp_api/dir_cpp_include_torch_tensorrt.html +++ b/docs/_cpp_api/dir_cpp_include_torch_tensorrt.html @@ -10,7 +10,7 @@ - Directory torch_tensorrt — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Directory torch_tensorrt — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/enum_namespacetorch__tensorrt_1_1logging_1a130f65408ad8cbaee060f05e8db69558.html b/docs/_cpp_api/enum_namespacetorch__tensorrt_1_1logging_1a130f65408ad8cbaee060f05e8db69558.html index 35e6debe99..f2ece9f252 100644 --- a/docs/_cpp_api/enum_namespacetorch__tensorrt_1_1logging_1a130f65408ad8cbaee060f05e8db69558.html +++ b/docs/_cpp_api/enum_namespacetorch__tensorrt_1_1logging_1a130f65408ad8cbaee060f05e8db69558.html @@ -10,7 +10,7 @@ - Enum Level — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Enum Level — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/enum_namespacetorch__tensorrt_1a3fbe5d72e4fc624dbd038853079620eb.html b/docs/_cpp_api/enum_namespacetorch__tensorrt_1a3fbe5d72e4fc624dbd038853079620eb.html index b4fa402119..7b98df5715 100644 --- a/docs/_cpp_api/enum_namespacetorch__tensorrt_1a3fbe5d72e4fc624dbd038853079620eb.html +++ b/docs/_cpp_api/enum_namespacetorch__tensorrt_1a3fbe5d72e4fc624dbd038853079620eb.html @@ -10,7 +10,7 @@ - Enum EngineCapability — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Enum EngineCapability — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html b/docs/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html index 5f24705e0b..8bb0e11451 100644 --- a/docs/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html +++ b/docs/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html @@ -10,7 +10,7 @@ - File logging.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + File logging.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html b/docs/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html index a9aa30a929..313aea43dd 100644 --- a/docs/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html +++ b/docs/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html @@ -10,7 +10,7 @@ - File macros.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + File macros.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html b/docs/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html index 1378d7a911..ffdfad12f3 100644 --- a/docs/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html +++ b/docs/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html @@ -10,7 +10,7 @@ - File ptq.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + File ptq.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html b/docs/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html index cb8e187508..fb1a315054 100644 --- a/docs/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html +++ b/docs/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html @@ -10,7 +10,7 @@ - File torch_tensorrt.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + File torch_tensorrt.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0593f776f469c20469e2f729fc7861a3.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0593f776f469c20469e2f729fc7861a3.html index 278dc53b83..8bdecdafdf 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0593f776f469c20469e2f729fc7861a3.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0593f776f469c20469e2f729fc7861a3.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::get_logging_prefix — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::get_logging_prefix — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0c012cb374addd90eb1f42eaec570650.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0c012cb374addd90eb1f42eaec570650.html index fb2f09953c..a8dcb663ed 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0c012cb374addd90eb1f42eaec570650.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a0c012cb374addd90eb1f42eaec570650.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::get_reportable_log_level — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::get_reportable_log_level — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a56e110feaaba2c3fd44bd201fd21a76a.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a56e110feaaba2c3fd44bd201fd21a76a.html index 60f01af545..4baf7091f6 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a56e110feaaba2c3fd44bd201fd21a76a.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a56e110feaaba2c3fd44bd201fd21a76a.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::get_is_colored_output_on — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::get_is_colored_output_on — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a7cb50492421ea9de4e3db895819df6f2.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a7cb50492421ea9de4e3db895819df6f2.html index 35b54da87c..e25f5e3b83 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a7cb50492421ea9de4e3db895819df6f2.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1a7cb50492421ea9de4e3db895819df6f2.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::set_reportable_log_level — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::set_reportable_log_level — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ac46ac0901cb97e3ae6e93b45f24e90b8.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ac46ac0901cb97e3ae6e93b45f24e90b8.html index 73c2ab24cd..fcc621aef8 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ac46ac0901cb97e3ae6e93b45f24e90b8.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ac46ac0901cb97e3ae6e93b45f24e90b8.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::log — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::log — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ad2efd47b6c3689e58ccc595680579ae5.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ad2efd47b6c3689e58ccc595680579ae5.html index 0d6def9c05..56439d5706 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ad2efd47b6c3689e58ccc595680579ae5.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1ad2efd47b6c3689e58ccc595680579ae5.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::set_is_colored_output_on — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::set_is_colored_output_on — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1af8f3443813315af7901903d25dd495cc.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1af8f3443813315af7901903d25dd495cc.html index 2bfc67ba96..12e46faa39 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1af8f3443813315af7901903d25dd495cc.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1logging_1af8f3443813315af7901903d25dd495cc.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::logging::set_logging_prefix — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::logging::set_logging_prefix — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a226e3c83379d1012cde8578c1c86b16c.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a226e3c83379d1012cde8578c1c86b16c.html index b4a3d911ce..29a6699f6e 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a226e3c83379d1012cde8578c1c86b16c.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a226e3c83379d1012cde8578c1c86b16c.html @@ -10,7 +10,7 @@ - Template Function torch_tensorrt::ptq::make_int8_cache_calibrator — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Template Function torch_tensorrt::ptq::make_int8_cache_calibrator — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a6186e305f47c1d94b6130ef6c7f7e178.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a6186e305f47c1d94b6130ef6c7f7e178.html index cbfd8322a7..b726b38ad8 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a6186e305f47c1d94b6130ef6c7f7e178.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1ptq_1a6186e305f47c1d94b6130ef6c7f7e178.html @@ -10,7 +10,7 @@ - Template Function torch_tensorrt::ptq::make_int8_calibrator — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Template Function torch_tensorrt::ptq::make_int8_calibrator — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a5b405fd3bf3c8fc2e2a54cbbab979797.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a5b405fd3bf3c8fc2e2a54cbbab979797.html index 3d0ccc4348..1b56570166 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a5b405fd3bf3c8fc2e2a54cbbab979797.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a5b405fd3bf3c8fc2e2a54cbbab979797.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::torchscript::check_method_operator_support — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::torchscript::check_method_operator_support — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a6e19490a08fb1553c9dd347a5ae79db9.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a6e19490a08fb1553c9dd347a5ae79db9.html index a2d9087dbf..ef85bbd1d5 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a6e19490a08fb1553c9dd347a5ae79db9.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a6e19490a08fb1553c9dd347a5ae79db9.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::torchscript::compile — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::torchscript::compile — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a81f9783517335dda877d8cfcf38987c9.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a81f9783517335dda877d8cfcf38987c9.html index 50778085d4..77098e2a8e 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a81f9783517335dda877d8cfcf38987c9.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1a81f9783517335dda877d8cfcf38987c9.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::torchscript::embed_engine_in_new_module — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::torchscript::embed_engine_in_new_module — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1ae8d56472106eeef37fbe51ff7f40c9b2.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1ae8d56472106eeef37fbe51ff7f40c9b2.html index 1b34411038..98d42cf4e1 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1ae8d56472106eeef37fbe51ff7f40c9b2.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1_1torchscript_1ae8d56472106eeef37fbe51ff7f40c9b2.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::torchscript::convert_method_to_trt_engine — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::torchscript::convert_method_to_trt_engine — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1ac4ab8313ae72c2c899ea31548b528528.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1ac4ab8313ae72c2c899ea31548b528528.html index f4122b4955..f95adc571b 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1ac4ab8313ae72c2c899ea31548b528528.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1ac4ab8313ae72c2c899ea31548b528528.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::get_build_info — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::get_build_info — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1ad1acd06eaeaffbbcf6e7ebf426891384.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1ad1acd06eaeaffbbcf6e7ebf426891384.html index f202c1d3f0..dee1199ea1 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1ad1acd06eaeaffbbcf6e7ebf426891384.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1ad1acd06eaeaffbbcf6e7ebf426891384.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::set_device — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::set_device — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/function_namespacetorch__tensorrt_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html b/docs/_cpp_api/function_namespacetorch__tensorrt_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html index 00c6b45647..654d938896 100644 --- a/docs/_cpp_api/function_namespacetorch__tensorrt_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html +++ b/docs/_cpp_api/function_namespacetorch__tensorrt_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html @@ -10,7 +10,7 @@ - Function torch_tensorrt::dump_build_info — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Function torch_tensorrt::dump_build_info — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/namespace_torch_tensorrt.html b/docs/_cpp_api/namespace_torch_tensorrt.html index b52412267d..961405c7fc 100644 --- a/docs/_cpp_api/namespace_torch_tensorrt.html +++ b/docs/_cpp_api/namespace_torch_tensorrt.html @@ -10,7 +10,7 @@ - Namespace torch_tensorrt — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Namespace torch_tensorrt — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/namespace_torch_tensorrt__logging.html b/docs/_cpp_api/namespace_torch_tensorrt__logging.html index 903729acc2..8d89eb2466 100644 --- a/docs/_cpp_api/namespace_torch_tensorrt__logging.html +++ b/docs/_cpp_api/namespace_torch_tensorrt__logging.html @@ -10,7 +10,7 @@ - Namespace torch_tensorrt::logging — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Namespace torch_tensorrt::logging — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/namespace_torch_tensorrt__ptq.html b/docs/_cpp_api/namespace_torch_tensorrt__ptq.html index f714303c21..8278009851 100644 --- a/docs/_cpp_api/namespace_torch_tensorrt__ptq.html +++ b/docs/_cpp_api/namespace_torch_tensorrt__ptq.html @@ -10,7 +10,7 @@ - Namespace torch_tensorrt::ptq — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Namespace torch_tensorrt::ptq — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/namespace_torch_tensorrt__torchscript.html b/docs/_cpp_api/namespace_torch_tensorrt__torchscript.html index 53f3e2ae2f..f649092a7d 100644 --- a/docs/_cpp_api/namespace_torch_tensorrt__torchscript.html +++ b/docs/_cpp_api/namespace_torch_tensorrt__torchscript.html @@ -10,7 +10,7 @@ - Namespace torch_tensorrt::torchscript — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Namespace torch_tensorrt::torchscript — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html index 3448aa537f..b986f030d2 100644 --- a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html +++ b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html @@ -10,7 +10,7 @@ - Program Listing for File logging.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Program Listing for File logging.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html index 43d53e256f..7ac014854c 100644 --- a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html +++ b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html @@ -10,7 +10,7 @@ - Program Listing for File macros.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Program Listing for File macros.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html index da3a4065e4..0207543780 100644 --- a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html +++ b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html @@ -10,7 +10,7 @@ - Program Listing for File ptq.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Program Listing for File ptq.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html index 928491f78a..d876aae576 100644 --- a/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html +++ b/docs/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html @@ -10,7 +10,7 @@ - Program Listing for File torch_tensorrt.h — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Program Listing for File torch_tensorrt.h — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    @@ -432,6 +435,7 @@ public: enum Value : int8_t { kLong, + kDouble, kFloat, kHalf, kChar, diff --git a/docs/_cpp_api/structtorch__tensorrt_1_1Device.html b/docs/_cpp_api/structtorch__tensorrt_1_1Device.html index 4090407a7c..e3b09e7175 100644 --- a/docs/_cpp_api/structtorch__tensorrt_1_1Device.html +++ b/docs/_cpp_api/structtorch__tensorrt_1_1Device.html @@ -10,7 +10,7 @@ - Struct Device — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Struct Device — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html b/docs/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html index cfe853493e..cb5d86fe0c 100644 --- a/docs/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html +++ b/docs/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html @@ -10,7 +10,7 @@ - Struct GraphInputs — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Struct GraphInputs — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/structtorch__tensorrt_1_1Input.html b/docs/_cpp_api/structtorch__tensorrt_1_1Input.html index f065a85570..0bceddb4cb 100644 --- a/docs/_cpp_api/structtorch__tensorrt_1_1Input.html +++ b/docs/_cpp_api/structtorch__tensorrt_1_1Input.html @@ -10,7 +10,7 @@ - Struct Input — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Struct Input — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html b/docs/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html index 6ec1b3db77..4b2437ded3 100644 --- a/docs/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html +++ b/docs/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html @@ -10,7 +10,7 @@ - Struct CompileSpec — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Struct CompileSpec — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/torch_tensort_cpp.html b/docs/_cpp_api/torch_tensort_cpp.html index 71c9943908..003ad4f806 100644 --- a/docs/_cpp_api/torch_tensort_cpp.html +++ b/docs/_cpp_api/torch_tensort_cpp.html @@ -10,7 +10,7 @@ - Torch-TensorRT C++ API — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Torch-TensorRT C++ API — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -225,7 +225,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -269,6 +269,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -304,6 +306,7 @@

    Indices

    diff --git a/docs/_cpp_api/unabridged_orphan.html b/docs/_cpp_api/unabridged_orphan.html index a1daeceadf..bf7fa033b1 100644 --- a/docs/_cpp_api/unabridged_orphan.html +++ b/docs/_cpp_api/unabridged_orphan.html @@ -10,7 +10,7 @@ - Full API — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Full API — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -223,7 +223,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -267,6 +267,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -302,6 +304,7 @@

    Indices

    diff --git a/docs/_downloads/6a6052d9668b2cb8332d349d328e21c1/_rendered_examples_jupyter.zip b/docs/_downloads/6a6052d9668b2cb8332d349d328e21c1/_rendered_examples_jupyter.zip index 0c394886e8..43f8e2dea2 100644 Binary files a/docs/_downloads/6a6052d9668b2cb8332d349d328e21c1/_rendered_examples_jupyter.zip and b/docs/_downloads/6a6052d9668b2cb8332d349d328e21c1/_rendered_examples_jupyter.zip differ diff --git a/docs/_downloads/798cda8f83bd9f5e2cc93f329a04332c/_rendered_examples_python.zip b/docs/_downloads/798cda8f83bd9f5e2cc93f329a04332c/_rendered_examples_python.zip index 50adabe644..4d1d417f7e 100644 Binary files a/docs/_downloads/798cda8f83bd9f5e2cc93f329a04332c/_rendered_examples_python.zip and b/docs/_downloads/798cda8f83bd9f5e2cc93f329a04332c/_rendered_examples_python.zip differ diff --git a/docs/_modules/index.html b/docs/_modules/index.html index d0850279da..d72b2b8cf1 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -9,7 +9,7 @@ - Overview: module code — Torch-TensorRT v2.2.0.dev0+b50290d documentation + Overview: module code — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -222,7 +222,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -266,6 +266,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -301,6 +303,7 @@

    Indices

    diff --git a/docs/_modules/torch_tensorrt/_Device.html b/docs/_modules/torch_tensorrt/_Device.html index 07f34c814b..8db1c2f2fa 100644 --- a/docs/_modules/torch_tensorrt/_Device.html +++ b/docs/_modules/torch_tensorrt/_Device.html @@ -9,7 +9,7 @@ - torch_tensorrt._Device — Torch-TensorRT v2.2.0.dev0+b50290d documentation + torch_tensorrt._Device — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -222,7 +222,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -266,6 +266,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -301,6 +303,7 @@

    Indices

    diff --git a/docs/_modules/torch_tensorrt/_Input.html b/docs/_modules/torch_tensorrt/_Input.html index 3ea14d3d68..3bc62ff017 100644 --- a/docs/_modules/torch_tensorrt/_Input.html +++ b/docs/_modules/torch_tensorrt/_Input.html @@ -9,7 +9,7 @@ - torch_tensorrt._Input — Torch-TensorRT v2.2.0.dev0+b50290d documentation + torch_tensorrt._Input — Torch-TensorRT v2.2.0.dev0+d375d10 documentation @@ -222,7 +222,7 @@
    - v2.2.0.dev0+b50290d + v2.2.0.dev0+d375d10
    @@ -266,6 +266,8 @@
  • Torch-TensorRT (FX Frontend) User Guide
  • Post Training Quantization (PTQ)
  • Deploying Torch-TensorRT Programs
    • +
  • Saving models compiled with Torch-TensorRT
    • +
  • Dynamic shapes with Torch-TensorRT
  • Using Torch-TensorRT Directly From PyTorch
  • DLA
    • @@ -301,6 +303,7 @@

    Indices

    @@ -429,6 +432,7 @@

    Source code for torch_tensorrt._Input

         low_tensor_domain_incl: float = 0.0
     high_tensor_domain_excl: float = low_tensor_domain_incl + DOMAIN_OFFSET
     torch_dtype: torch.dtype = torch.float32
+    torch_tensor: torch.Tensor = None
 
 
    [docs]    def __init__(self, *args: Any, **kwargs: Any) -> None:
         """__init__ Method for torch_tensorrt.Input
@@ -552,7 +556,15 @@ 
    Source code for torch_tensorrt._Input
             else:
             domain = None
 
-        self.tensor_domain = Input._parse_tensor_domain(domain)
    
+        self.tensor_domain = Input._parse_tensor_domain(domain)
+
+        if "torch_tensor" in kwargs:
+            self.torch_tensor = kwargs["torch_tensor"]
+        else:
+            if self.shape_mode == Input._ShapeMode.DYNAMIC:
+                self.torch_tensor = self.example_tensor("opt_shape")
+            else:
+                self.torch_tensor = self.example_tensor()
    
 
     def __str__(self) -> str:
         if self.shape_mode == Input._ShapeMode.STATIC:
@@ -603,6 +615,8 @@ 
    Source code for torch_tensorrt._Input
                     return _enums.dtype.half
             elif dtype == torch.float:
                 return _enums.dtype.float
+            elif dtype == torch.float64:
+                return _enums.dtype.double
             elif dtype == torch.bool:
                 return _enums.dtype.bool
             else:
@@ -632,6 +646,8 @@ 
    Source code for torch_tensorrt._Input
                 return torch.float
         elif dtype == _enums.dtype.bool:
             return torch.bool
+        elif dtype == _enums.dtype.double:
+            return torch.float64
         else:
             # Default torch_dtype used in FX path
             return torch.float32
@@ -737,7 +753,7 @@ 
    Source code for torch_tensorrt._Input
                 )
             else torch.channels_last
         )
-        return cls(shape=t.shape, dtype=t.dtype, format=frmt)
    
+        return cls(shape=t.shape, dtype=t.dtype, format=frmt, torch_tensor=t)
    
 
 
    [docs]    @classmethod
     def from_tensors(
diff --git a/docs/_modules/torch_tensorrt/_compile.html b/docs/_modules/torch_tensorrt/_compile.html
index 48b3dd08d1..8983536777 100644
--- a/docs/_modules/torch_tensorrt/_compile.html
+++ b/docs/_modules/torch_tensorrt/_compile.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt._compile — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt._compile — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
@@ -597,23 +600,24 @@ 
    Source code for torch_tensorrt._compile
             )
         return compiled_fx_module
     elif target_ir == _IRType.dynamo:
+        # Prepare torch and torchtrt inputs
         import collections.abc
 
-        from torch_tensorrt import Device
-        from torch_tensorrt.dynamo.utils import prepare_inputs, to_torch_device
+        from torch_tensorrt.dynamo.utils import prepare_inputs
 
-        if not isinstance(inputs, collections.abc.Sequence):
-            inputs = [inputs]
-        device = kwargs.get("device", Device._current_device())
-        torchtrt_inputs, torch_inputs = prepare_inputs(inputs, to_torch_device(device))
-        module = torch_tensorrt.dynamo.trace(module, torch_inputs, **kwargs)
-        compiled_aten_module: torch.fx.GraphModule = dynamo_compile(
-            module,
-            inputs=input_list,
+        if not isinstance(input_list, collections.abc.Sequence):
+            input_list = [input_list]
+
+        # Export the module
+        torchtrt_inputs = prepare_inputs(input_list)
+        exp_program = torch_tensorrt.dynamo.trace(module, torchtrt_inputs, **kwargs)
+        trt_graph_module = dynamo_compile(
+            exp_program,
+            inputs=torchtrt_inputs,
             enabled_precisions=enabled_precisions_set,
             **kwargs,
         )
-        return compiled_aten_module
+        return trt_graph_module
     elif target_ir == _IRType.torch_compile:
         return torch_compile(
             module, enabled_precisions=enabled_precisions_set, **kwargs
diff --git a/docs/_modules/torch_tensorrt/_utils.html b/docs/_modules/torch_tensorrt/_utils.html
index 0fe35a6b2c..61821a5518 100644
--- a/docs/_modules/torch_tensorrt/_utils.html
+++ b/docs/_modules/torch_tensorrt/_utils.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt._utils — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt._utils — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/fx/fx2trt.html b/docs/_modules/torch_tensorrt/fx/fx2trt.html
index 56a753d7d8..efefd889fe 100644
--- a/docs/_modules/torch_tensorrt/fx/fx2trt.html
+++ b/docs/_modules/torch_tensorrt/fx/fx2trt.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.fx.fx2trt — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.fx.fx2trt — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html b/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html
index be65f5ad1f..84787375e5 100644
--- a/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html
+++ b/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.fx.input_tensor_spec — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.fx.input_tensor_spec — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/fx/lower.html b/docs/_modules/torch_tensorrt/fx/lower.html
index 3e555cfd6a..7af41b5788 100644
--- a/docs/_modules/torch_tensorrt/fx/lower.html
+++ b/docs/_modules/torch_tensorrt/fx/lower.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.fx.lower — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.fx.lower — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/fx/trt_module.html b/docs/_modules/torch_tensorrt/fx/trt_module.html
index 945e9a4436..954fdce0a0 100644
--- a/docs/_modules/torch_tensorrt/fx/trt_module.html
+++ b/docs/_modules/torch_tensorrt/fx/trt_module.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.fx.trt_module — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.fx.trt_module — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/logging.html b/docs/_modules/torch_tensorrt/logging.html
index 4d1531feb6..68f14206ef 100644
--- a/docs/_modules/torch_tensorrt/logging.html
+++ b/docs/_modules/torch_tensorrt/logging.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.logging — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.logging — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/ptq.html b/docs/_modules/torch_tensorrt/ptq.html
index d23f44dd09..4e99b2d200 100644
--- a/docs/_modules/torch_tensorrt/ptq.html
+++ b/docs/_modules/torch_tensorrt/ptq.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.ptq — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.ptq — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/ts/_compile_spec.html b/docs/_modules/torch_tensorrt/ts/_compile_spec.html
index 456d5958a6..2164bf55e6 100644
--- a/docs/_modules/torch_tensorrt/ts/_compile_spec.html
+++ b/docs/_modules/torch_tensorrt/ts/_compile_spec.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.ts._compile_spec — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.ts._compile_spec — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_modules/torch_tensorrt/ts/_compiler.html b/docs/_modules/torch_tensorrt/ts/_compiler.html
index 27424473b0..9a43ac4130 100644
--- a/docs/_modules/torch_tensorrt/ts/_compiler.html
+++ b/docs/_modules/torch_tensorrt/ts/_compiler.html
@@ -9,7 +9,7 @@
   
   
   
-  torch_tensorrt.ts._compiler — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.ts._compiler — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
diff --git a/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt b/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
index 07718fc152..67848a40a0 100644
--- a/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
+++ b/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
@@ -56,6 +56,7 @@ Program Listing for File torch_tensorrt.h
     public:
      enum Value : int8_t {
        kLong,
+       kDouble,
        kFloat,
        kHalf,
        kChar,
diff --git a/docs/_sources/contributors/fx_converters.rst.txt b/docs/_sources/contributors/fx_converters.rst.txt
new file mode 100644
index 0000000000..75ee1c6341
--- /dev/null
+++ b/docs/_sources/contributors/fx_converters.rst.txt
@@ -0,0 +1,211 @@
+.. _dynamo_conversion:
+
+Dynamo Converters
+==================
+The dynamo converter library in Torch-TensorRT is located in ``TensorRT/py/torch_tensorrt/dynamo/conversion``.
+
+
+
+Steps
+==================
+
+Operation Set
+-------------------
+The converters in dynamo are produced by ``aten_trace`` and falls under ``aten_ops_converters`` ( FX earlier had ``acc_ops_converters``, ``aten_ops_converters`` or ``nn_ops_converters`` depending on the trace through which it was produced).  The converters are registered using  ``dynamo_tensorrt_converter`` for dynamo. The function decorated
+has the arguments - ``network, target, args, kwargs, name``,  which is common across all the operators schema.
+These functions are mapped in the ``aten`` converter registry dictionary (at present a compilation of FX and dynamo converters, FX will be deprecated soon), with key as the function target name.
+    
+    * aten_trace is produced by ``torch_tensorrt.dynamo.trace(..)`` for the export path and ``torch_tensorrt.compile(ir=dynamo)`` for the compile path.
+    The export path makes use of ``aten_tracer`` whereas the alternate trace in compile is produced by the AOT Autograd library.
+    Both these simplify the torch operators to reduced set of Aten operations.
+        
+
+As mentioned above, if you would like to add a new converter, its implementation will be included in ``TensorRT/py/torch_tensorrt/dynamo/conversion/impl``
+Although there is a corresponding implementation of the converters  included in the common implementation library present in ``TensorRT/py/torch_tensorrt/fx/impl`` for FX converters, this documentation focuses on the implementation of the ``aten_ops`` converters in dynamo.
+
+
+Converter implementation
+------------------------
+In this section, we illustrate the steps to be implemented for writing a converter. We divide them according to activation, operator, lowering pass implementation or evaluator.
+Each of them is detailed with the help of an example
+
+    * Registration
+        
+        The converter needs to be registered with the appropriate op code in the ``dynamo_tensorrt_converter``. 
+        
+        * Activation type
+
+            Example: ``leaky_relu``
+            
+            
+            * aten_ops_converters: Dynamo_converters
+
+                    Define in ``py/torch_tensorrt/dynamo/conversion/aten_ops_converters``. One needs to register the opcode generated in the trace with ``dynamo_tensorrt_converter`` decorator. Op code to be used for the registration or the converter registry key in this case is ``torch.ops.aten.leaky_relu.default``
+
+                        .. code-block:: python
+            
+                            @dynamo_tensorrt_converter(torch.ops.aten.leaky_relu.default)
+                                def aten_ops_leaky_relu(
+                                    network: TRTNetwork,
+                                    target: Target,
+                                    args: Tuple[Argument, ...],
+                                    kwargs: Dict[str, Argument],
+                                    name: str,
+                                ) -> Union[TRTTensor, Sequence[TRTTensor]]:
+                                        return activation.leaky_relu(network, target, SourceIR.ATEN, name, args[0], args[1])
+
+            The ``tensorrt_converter`` (used for FX registration) and ``dynamo_tensorrt_converter`` are similar decorator functions with some differences. 
+            
+            #. Both register the converters in the registeries (python dictionaries) - ``CONVERTERS`` and ``DYNAMO_CONVERTERS`` respectively. These are two dictioneries which are concatenated to form the overall converter registry 
+            #. The dictionary is keyed on the ``OpOverLoad`` which is mentioned in more detail below with examples 
+            #. Both return the decorated converter implementation
+            #. The ``CONVERTERS`` directly registers the decorated ``converter_implementation`` function, while ``DYNAMO_CONVERTERS`` has additionational arguments and registers the ``ConverterSupport`` object
+            #. The additional arguments are:
+
+                .. code-block:: python
+                    def dynamo_tensorrt_converter(
+                        key: Target,
+                        enabled: bool = True,
+                        capability_validator: Optional[Callable[[Node], bool]] = None,
+                        priority: ConverterPriority = ConverterPriority.STANDARD,
+                    ) -> Callable[[Any], Union[TRTTensor, Sequence[TRTTensor]]]:
+
+                #. key: Node target for which the converter is implemented for (for example, torch.ops.aten.leaky_relu.Tensor)
+                #. enabled: Whether the converter should be enabled/cached or not
+                #. capability_validator: Function which evaluates whether a node is valid for conversion by the decorated converter. It defaults to None, implying the capability_validator function is always true. This means all nodes of "key" kind can be supported by this converter by default. See ``embedding`` example for more details
+                #. priority: Converter's level of priority relative to other converters with the same target
+
+            #. The ``ConverterSupport`` is a compilation of ``converter_implementation`` and ``capability_validator``.
+
+
+            The function decorated by ``tensorrt_converter`` and ``dynamo_tensorrt_converter`` has the following arguments which are automatically generated by the trace functions mentioned above.
+            
+            #. network : Node in the form of ``call_module`` or ``call_function`` having the target as the key
+            #. target: Target key in the ``call_module`` or ``call_function`` above. eg: ``torch.ops.aten_.leaky_relu.default``. Note that ``torch.ops.aten._leaky_relu`` is the ``OpOverloadPacket`` while ``torch.ops.aten_.leaky_relu.default`` is ``OpOverload``.
+            #. args: The arguments passed in the ``call_module`` or ``call_function`` above
+            #. kwargs: The kwargs passed in the ``call_module`` or ``call_function`` above
+            #. name: String containing the name of the target
+
+            As a user writing new converters, one just needs to take care that the approriate arguments are extracted from the trace generated to the implementation function in the implementation lib function ``activation.leaky_relu`` (which we will discuss below in detail).
+
+        * Operation type
+
+            Example: ``fmod``
+
+            It follows the same steps as the above converter. In this case the opcode is ``torch.ops.aten.fmod.Scalar`` or ``torch.ops.aten.fmod.Tensor``. 
+            Hence both the opcodes are registered in ``py/torch_tensorrt/dynamo/conversion/aten_ops_converters``.
+            Note that ``torch.ops.aten.fmod`` is the ``OpOverLoadPacket`` while the registry is keyed on ``torch.ops.aten.fmod.Scalar`` or ``torch.ops.aten.fmod.Tensor``, which is ``OpOverLoad``
+
+            Example: ``embedding``
+
+            It follows the same steps as the above converter. In this case the opcode is ``torch.ops.aten.embedding.default``. 
+            There are some converters which have special cases to be accounted for. In those cases, one should use ``capability_validators`` to register the converter using ``@dynamo_tensorrt_converter``
+            We illustrate this through ``torch.ops.aten.embedding.default``. It has parameters - ``scale_grad_by_freq`` and ``sparse`` which are not currently supported by the implementation.
+            In such cases we can write validator ``embedding_param_validator`` which implements that given those paramters the converter is not supported and register the converter by 
+                
+                .. code-block:: python
+                    @dynamo_tensorrt_converter(
+                        torch.ops.aten.embedding.default, capability_validator=embedding_param_validator
+                    )
+
+            So if there is a new converter in which certain special cases are not to be supported then they can be specified in the ``capability_validator``.
+        
+        * Evaluator type
+
+            Example: ``operator.getitem``
+
+            Evaluators are categorized as so since they do not make any modification to the graph. This is implemented in ``py/torch_tensorrt/dynamo/conversion/op_evaluators.py``, with the corresponding ``capbility_validator``.
+            The opcode is ``operator.getitem``.
+
+
+    * Implementation Library
+
+        The dynamo converters would be located in ``py/torch_tensorrt/dynamo/conversion/impl``
+        
+        * Activation
+
+            Example: ``leaky_relu``
+        
+            The implementation is to be placed in present in ``py/torch_tensorrt/dynamo/conversion/impl/activation.py``. This is where all the activation functions are defined and implemented.
+            
+            .. code-block:: python
+
+                def leaky_relu(
+                    network: TRTNetwork,
+                    target: Target,
+                    source_ir: Optional[SourceIR],
+                    name: str,
+                    input_val: TRTTensor,
+                    alpha: Optional[Any],
+                ):
+                    #implementation
+
+            The implementation function has the following arguments.
+
+            #. network : ``network`` passed from the decorated function registration
+            #. target: ``target`` passed from the decorated function registration
+            #. source_ir: Enum attribute. ``SourceIR`` enum is defined in ``py/torch_tensorrt/dynamo/conversion/impl/converter_utils``
+            #. name: ``name`` passed from the decorated function registration
+            #. input_val: Approriate arguments extracted from the decorated function registration from args or kwargs
+            #. alpha: Approriate arguments extracted from the decorated function registration from args or kwargs. If not None, it will set the alpha attribute of the created TensorRT activation layer eg: Used in leaky_relu, elu, hardtanh           
+            #. beta: Approriate arguments extracted from the decorated function registration from args or kwargs. If not None, it will set the beta attribute of the created TensorRT activation layer eg: Used in hardtanh
+            #. dyn_range_fn: A optional function which takes the dynamic range of a TensorRT Tensor and returns the output dynamic range
+
+            The implementation functions call the ``convert_activation`` function in ``py/torch_tensorrt/dynamo/conversion/impl/activation.py``. This function will add the approriate activation layer via ``network.add_activation``.
+        
+        * Operator
+        
+            The implementation is to be placed in ``py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py`` for dynamo. This is where all the elementwise functions are defined and implemented.
+            For a new operator, one should identify the category to which it belongs. Following are some examples
+
+            #. Elementwise operators like ``fmod`` is present in ``py/torch_tensorrt/dynamo/conversion/impl/elementwise``. The ``py/torch_tensorrt/dynamo/conversion/impl/elementwise/base`` contains base functions for elementwise operator.
+            #. Unary operators like ``sqrt`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/unary``. The ``py/torch_tensorrt/dynamo/conversion/impl/unary/base`` contains base functions for unary operator.
+            #. Normalization operators like ``softmax``, ``layer_norm``, ``batch_norm`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/normalization``. Since there are no base operations common to all, there is no base file. But one can choose to implement a base file, if there are common functions across all normalization operations
+            #. Individual operators like ``slice``, ``select``, ``where``, ``embedding`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/*.py``. They will have individual operator implementation with the same API structure as above but with different individual arguments
+            
+            Please note that the above operators would have common functions to be implemented which should be placed in 
+            ``py/torch_tensorrt/dynamo/conversion/impl/converter_utils.py``
+
+
+    * Lowering type
+
+        There are some converters which can be decomposed into suboperations and need not have seperate converter registration.
+        Such converters can be implemented via ``lowering passes``
+
+        Example: ``addmm``
+        
+        The decompositions are registered via ``register_decomposition`` in ``py/torch_tensorrt/dynamo/backend/lowering/_decompositions.py``
+        We define ``addmm_replacement`` and replace it with the torch ops, which will have their corresponding converters called.
+
+        .. code-block:: python
+
+            @register_decomposition(torch.ops.aten.addmm, registry=DECOMPOSITIONS)
+            def addmm_replacement(
+                input_: torch.Tensor, mat1: torch.Tensor, mat2: torch.Tensor, *, beta=1, alpha=1
+            ) -> torch.Tensor:
+                return torch.add(
+                    torch.mul(input_, beta), torch.mul(torch.matmul(mat1, mat2), alpha)
+                )
+        
+        Note that there are some pre-existing dynamo decompositions in torch directory, in which case they should be used,
+        In that case please enable the decompositions in ``py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py`` in ``torch_enabled_decompositions``.
+        Similarly you can choose to disable any in ``torch_disabled_decompositions``. Please note that the ones already defined in the lowering will take precedence over torch lowering ops.
+
+
+
+       
+Tests
+-----
+
+* Dynamo testing: 
+    
+    Dynamo tests are present for the lowering ops in ``tests/py/dynamo/lowering/test_decompositions.py``. The above converters will soon be ported to dynamo tests
+    
+    #. Compare the results for ``fx.symbolic_trace`` and ``torch_tensorrt.dynamo.compile``.
+    #. Test for the ``expected_op`` and the ``unexpected_op``. 
+        
+        #. ``expected_op``: Operations the operations are lowered to. eg: ``mul`` and ``add`` for ``addmm``
+        #. Note that specify that ``disable_passes= True`` for cases where you would not want lowering passes (which should be the default when testing converters)
+        #. ``unexpected_op``: Original operation. eg: ``addmm`` for ``addmm``
+        
+The tests should fail if any of the above two conditions fail
diff --git a/docs/_sources/contributors/writing_dynamo_aten_lowering_passes.rst.txt b/docs/_sources/contributors/writing_dynamo_aten_lowering_passes.rst.txt
new file mode 100644
index 0000000000..4c29bc9b75
--- /dev/null
+++ b/docs/_sources/contributors/writing_dynamo_aten_lowering_passes.rst.txt
@@ -0,0 +1,109 @@
+.. _writing_dynamo_aten_lowering_passes:
+
+Writing Dynamo ATen Lowering Passes
+===================
+
+Basics of a Lowering Pass
+------------
+
+ATen lowering passes are Python functions which take as input a graph of ATen operators, apply some desired modification such as operator coalescing/fusion, operator replacement, subgraph rewriting, custom operator insertion, or other operation on a `torch.fx.GraphModule`, then return the modified graph to the caller. These lowering passes generally modify the graph in-place and return the same input object.
+
+Lowering Pass Requirements
+------------
+
+An ATen lowering pass function in Torch-TRT must satisfy two requirements:
+- The function must take as input a `torch.fx.GraphModule` and a sequence of torch Tensors, `Sequence[torch.Tensor]`, and return the lowered `torch.fx.GraphModule`
+- The function must leave the graph in a valid and invoke-able state, including performing any necessary linting and recompilation
+
+See this link for information on `Graph Manipulations `_ in FX. See below for an example of a lowering pass which repairs graphs that have inputs which are also outputs, a disallowed configuration for TRT Engines.
+
+Example Lowering Pass
+------------
+
+.. code-block:: python
+
+    def repair_input_as_output(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+        """Repair scenarios where inputs are also outputs of the graph
+
+        TRT does not allow such cases, so we insert a clone (identity) layer
+        """
+        modified_graph = False
+
+        # Extract graph placeholder Tensors
+        placeholders = [
+            node
+            for node in gm.graph.nodes
+            if (
+                node.op == "placeholder"
+                and isinstance(node.type, type)
+                and issubclass(node.type, torch.Tensor)
+            )
+        ]
+
+        for placeholder in placeholders:
+            # If any placeholder has any users which are direct graph outputs
+            if len(placeholder.users) >= 1 and any(
+                user.op == "output" for user in placeholder.users
+            ):
+                modified_graph = True
+
+                # Get direct graph outputs which are direct uses of placeholders
+                direct_outputs = [user for user in placeholder.users if user.op == "output"]
+
+                # Insert clone node for placeholder to ensure
+                # placeholder is not a direct output
+                with gm.graph.inserting_after(placeholder):
+                    cloned_placeholder = gm.graph.call_function(
+                        torch.ops.aten.clone.default,
+                        args=(placeholder,),
+                    )
+
+                # Replace placeholder as output with cloned version
+                for output in direct_outputs:
+                    output.replace_input_with(placeholder, cloned_placeholder)
+
+        # If the graph was modified, clean up the graph and ensure it is up-to-date
+        if modified_graph:
+            gm.graph.eliminate_dead_code()
+            gm.graph.lint()
+            gm.recompile()
+            logger.debug(f"Graph after repair_input_as_output:\n{gm.graph}")
+
+        return gm
+
+
+Registering Lowering Passes
+----------------------
+
+Lowering passes are currently registered in `py/torch_tensorrt/dynamo/lowering/passes/__init__.py`, using the `torch.fx.passes.pass_manager.PassManager` utility to assemble the list of passes in a desired order. New passes added directly to that list will be applied to graphs in the Torch-TensorRT `torch.compile` backend. Currently, we offer an ATen lowering pass registration decorator for convenience, which can be invoked either directly, or with the optional `index` keyword argument which controls where in the pass list the lowering pass will be inserted.
+
+For instance, to insert the pass at the default location (end of the list), the following code can be used:
+
+.. code-block:: python
+
+    @_aten_lowering_pass
+    def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+        ...
+
+Alternatively, to insert the pass at a custom index (such as the front of the list) in the passlist, the following code can be used:
+
+.. code-block:: python
+
+    @_aten_lowering_pass(index=0)
+    def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+        ...
+
+There are also provided utilities in `torch_tensorrt.dynamo.lowering.passes` for displaying the currently-available lowering pass list, applying those passes to an arbitrary `torch.fx.GraphModule`, and removing the lowering pass at a specific index.
+
+.. code-block:: python
+
+    # Print all lowering passes in the list
+    print(dump_lowering_passes())
+
+    # Apply lowering passes to a GraphModule
+    apply_lowering_passes(graph_module, sample_inputs)
+
+    # Remove the lowering pass at index 1
+    _remove_lowering_pass(index=1)
+
+**Note:** The above APIs are subject to change, as the lowering pass system evolves.
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index eee62bc2f7..9e98c7a63d 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -42,6 +42,8 @@ User Guide
 * :ref:`getting_started_with_fx`
 * :ref:`ptq`
 * :ref:`runtime`
+* :ref:`saving_models`
+* :ref:`dynamic_shapes`
 * :ref:`use_from_pytorch`
 * :ref:`using_dla`
 
@@ -54,6 +56,8 @@ User Guide
    user_guide/getting_started_with_fx_path
    user_guide/ptq
    user_guide/runtime
+   user_guide/saving_models
+   user_guide/dynamic_shapes
    user_guide/use_from_pytorch
    user_guide/using_dla
 
@@ -128,6 +132,7 @@ Contributor Documentation
 --------------------------------
 * :ref:`system_overview`
 * :ref:`writing_converters`
+* :ref:`writing_dynamo_aten_lowering_passes`
 * :ref:`useful_links`
 
 .. toctree::
@@ -137,6 +142,7 @@ Contributor Documentation
 
    contributors/system_overview
    contributors/writing_converters
+   contributors/writing_dynamo_aten_lowering_passes
    contributors/useful_links
 
 Indices
diff --git a/docs/_sources/user_guide/dynamic_shapes.rst.txt b/docs/_sources/user_guide/dynamic_shapes.rst.txt
new file mode 100644
index 0000000000..28320956c4
--- /dev/null
+++ b/docs/_sources/user_guide/dynamic_shapes.rst.txt
@@ -0,0 +1,218 @@
+.. _runtime:
+
+Dynamic shapes with Torch-TensorRT
+====================================
+
+By default, you can run a pytorch model with varied input shapes and the output shapes are determined eagerly. 
+However, Torch-TensorRT is an AOT compiler which requires some prior information about the input shapes to compile and optimize the model.
+In the case of dynamic input shapes, we must provide the (min_shape, opt_shape, max_shape) arguments so that the model can be optimized for
+these range of input shapes. An example usage of static and dynamic shapes is as follows.
+
+NOTE: The following code uses dynamo IR. Incase of Torchscript IR, please swap out ``ir=dynamo`` with ``ir=ts`` and the behavior is exactly the same.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    # Compile with static shapes
+    inputs = torch_tensorrt.Input(shape=[1, 3, 224, 224], dtype=torch.float32)
+    # or compile with dynamic shapes
+    inputs = torch_tensorrt.Input(min_shape=[1, 3, 224, 224], 
+                                  opt_shape=[4, 3, 224, 224],
+                                  max_shape=[8, 3, 224, 224],
+                                  dtype=torch.float32)
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+
+Under the hood
+--------------
+
+There are two phases of compilation when we use ``torch_tensorrt.compile`` API with ``ir=dynamo`` (default).
+
+- aten_tracer.trace (which uses torch.export to trace the graph with the given inputs)
+
+In the tracing phase, we use torch.export along with the constraints. In the case of 
+dynamic shaped inputs, the range can be provided to the tracing via constraints. Please 
+refer to this `docstring `_
+for detailed information on how to set constraints. In short, we create new inputs for 
+torch.export tracing and provide constraints on the min and max values(provided by the user), a particular dimension can take. 
+Please take a look at ``aten_tracer.py`` file to understand how this works under the hood. 
+
+- dynamo.compile (which compiles a torch.fx.GraphModule object using TensorRT)
+
+In the conversion to TensorRT, we use the user provided dynamic shape inputs. 
+We perform shape analysis using dummy inputs (across min, opt and max shapes) and store the 
+intermediate output shapes which can be used in case the graph has a mix of Pytorch 
+and TensorRT submodules.
+
+Custom Constraints
+------------------
+
+Given an input ``x = torch_tensorrt.Input(min_shape, opt_shape, max_shape, dtype)``, 
+Torch-TensorRT automatically sets the constraints during ``torch.export`` tracing as follows 
+
+.. code-block:: python
+
+    for dim in constraint_dims:
+        if min_shape[dim] > 1:
+            constraints.append(min_shape[dim] <= dynamic_dim(trace_input, dim))
+        if max_shape[dim] > 1:
+            constraints.append(dynamic_dim(trace_input, dim) <= max_shape[dim])
+
+Sometimes, we might need to set additional constraints and Torchdynamo errors out if we don't specify them.
+For example, in the case of BERT model compilation, there are two inputs and a constraint has to be set involving the sequence length size of these two inputs.
+
+.. code-block:: python
+
+    constraints.append(dynamic_dim(trace_inputs[0], 0) == dynamic_dim(trace_inputs[1], 0))
+
+
+If you have to provide any custom constraints to your model, the overall workflow for model compilation using ``ir=dynamo`` would involve a few steps.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+    from torch_tensorrt.dynamo.lowering import apply_lowering_passes, get_decompositions
+    # Assume the model has two inputs
+    model = MyModel()
+    torch_input_1 = torch.randn((1, 14), dtype=torch.int32).cuda()
+    torch_input_2 = torch.randn((1, 14), dtype=torch.int32).cuda()
+
+    dynamic_inputs = [torch_tensorrt.Input(min_shape=[1, 14], 
+                        opt_shape=[4, 14],
+                        max_shape=[8, 14],
+                        dtype=torch.int32), 
+                      torch_tensorrt.Input(min_shape=[1, 14], 
+                        opt_shape=[4, 14],
+                        max_shape=[8, 14],
+                        dtype=torch.int32)]
+
+    # Export the model with additional constraints
+    constraints = []
+    # The following constraints are automatically added by Torch-TensorRT in the 
+    # general case when you call torch_tensorrt.compile directly on MyModel()
+    constraints.append(dynamic_dim(torch_input_1, 0) < 8)
+    constraints.append(dynamic_dim(torch_input_2, 0) < 8)
+    # This is an additional constraint as instructed by Torchdynamo
+    constraints.append(dynamic_dim(torch_input_1, 0) == dynamic_dim(torch_input_2, 0))
+    with unittest.mock.patch(
+        "torch._export.DECOMP_TABLE", get_decompositions(experimental_decompositions)
+    ):
+        graph_module = export(
+            model, (torch_input_1, torch_input_2), constraints=constraints
+        ).module()
+
+    # Use the dynamo.compile API
+    trt_mod = torch_tensorrt.dynamo.compile(graph_module, inputs=dynamic_inputs, **compile_spec)
+
+Limitations
+-----------
+
+If there are operations in the graph that use the dynamic dimension of the input, Pytorch 
+introduces ``torch.ops.aten.sym_size.int`` ops in the graph. Currently, we cannot handle these operators and 
+the compilation results in undefined behavior. We plan to add support for these operators and implement 
+robust support for shape tensors in the next release. Here is an example of the limitation described above
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.avgpool = torch.nn.AdaptiveAvgPool2d((1, 1))
+
+        def forward(self, x):
+            x = self.avgpool(x)
+            out = torch.flatten(x, 1)
+            return out
+
+    model = MyModel().eval().cuda()
+    # Compile with dynamic shapes
+    inputs = torch_tensorrt.Input(min_shape=(1, 512, 1, 1), 
+                         opt_shape=(4, 512, 1, 1),
+                         max_shape=(8, 512, 1, 1),
+                         dtype=torch.float32)
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+
+
+The traced graph of `MyModule()` looks as follows
+
+.. code-block:: python
+
+    Post export graph: graph():
+    %arg0_1 : [num_users=2] = placeholder[target=arg0_1]
+    %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [-1, -2], True), kwargs = {})
+    %sym_size : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%arg0_1, 0), kwargs = {})
+    %view : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%mean, [%sym_size, 512]), kwargs = {})
+    return (view,)
+
+
+Here the ``%sym_size`` node captures the dynamic batch and uses it in the ``aten.view`` layer. This requires shape tensors support 
+which would be a part of our next release.
+
+Workaround (BERT static compilation example)
+------------------------------------------
+
+In the case where you encounter the issues mentioned in the **Limitations** section, 
+you can compile the model (static mode) with max input size that can be provided. In the cases of smaller inputs, 
+we can pad them accordingly. This is only a workaround until we address the limitations.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+    from transformers.utils.fx import symbolic_trace as transformers_trace
+
+    model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
+
+    # Input sequence length is 20.
+    input1 = torch.randint(0, 5, (1, 20), dtype=torch.int32).to("cuda")
+    input2 = torch.randint(0, 5, (1, 20), dtype=torch.int32).to("cuda")
+    
+    model = transformers_trace(model, input_names=["input_ids", "attention_mask"]).eval().cuda()
+    trt_mod = torch_tensorrt.compile(model, inputs=[input1, input2], **compile_spec)
+    model_outputs = model(input, input2)
+    
+    # If you have a sequence of length 14, pad 6 zero tokens and run inference
+    # or recompile for sequence length of 14.
+    input1 = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+    input2 = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+    trt_mod = torch_tensorrt.compile(model, inputs=[input1, input2], **compile_spec)
+    model_outputs = model(input, input2)
+
+
+Dynamic shapes with ir=torch_compile
+------------------------------------
+
+``torch_tensorrt.compile(model, inputs, ir="torch_compile")`` returns a torch.compile boxed function with the backend 
+configured to Tensorrt. In the case of ``ir=torch_compile``, users have to recompile for different input shapes. 
+In the future, we plan to explore the option of compiling with dynamic shapes in the first execution of the model.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = torch.randn((1, 3, 224, 224), dtype=float32)
+    trt_gm = torch_tensorrt.compile(model, ir="torch_compile", inputs)
+    # Compilation happens when you call the model
+    trt_gm(inputs)
+
+    # Recompilation happens with modified batch size
+    inputs_bs2 = torch.randn((2, 3, 224, 224), dtype=torch.float32)
+    trt_gm = torch_tensorrt.compile(model, ir="torch_compile", inputs_bs2)
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/_sources/user_guide/saving_models.rst.txt b/docs/_sources/user_guide/saving_models.rst.txt
new file mode 100644
index 0000000000..46fadcb905
--- /dev/null
+++ b/docs/_sources/user_guide/saving_models.rst.txt
@@ -0,0 +1,77 @@
+.. _runtime:
+
+Saving models compiled with Torch-TensorRT
+====================================
+
+Saving models compiled with Torch-TensorRT varies slightly with the `ir` that has been used for compilation.
+
+1) Dynamo IR
+
+Starting with 2.1 release of Torch-TensorRT, we are switching the default compilation to be dynamo based.
+The output of `ir=dynamo` compilation is a `torch.fx.GraphModule` object. There are two ways to save these objects
+
+a) Converting to Torchscript
+`torch.fx.GraphModule` objects cannot be serialized directly. Hence we use `torch.jit.trace` to convert this into a `ScriptModule` object which can be saved to disk. 
+The following code illustrates this approach. 
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = torch.randn((1, 3, 224, 224)).cuda()
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
+    trt_script_model = torch.jit.trace(trt_gm, inputs)
+    torch.jit.save(trt_script_model, "trt_model.ts")
+
+    # Later, you can load it and run inference
+    model = torch.jit.load("trt_model.ts").cuda()
+    model(inputs)
+
+b) ExportedProgram
+`torch.export.ExportedProgram` is a new format introduced in Pytorch 2.1. After we compile a Pytorch module using Torch-TensorRT, the resultant 
+`torch.fx.GraphModule` along with additional metadata can be used to create `ExportedProgram` which can be saved and loaded from disk.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+    from torch_tensorrt.dynamo.export import transform, create_exported_program
+
+    model = MyModel().eval().cuda()
+    inputs = torch.randn((1, 3, 224, 224)).cuda()
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
+    # Transform and create an exported program
+    trt_gm = transform(trt_gm, inputs)
+    trt_exp_program = create_exported_program(trt_gm, call_spec, trt_gm.state_dict())
+    torch._export.save(trt_exp_program, "trt_model.ep")
+
+    # Later, you can load it and run inference 
+    model = torch._export.load("trt_model.ep")
+    model(inputs)
+
+`torch_tensorrt.dynamo.export.transform` inlines the submodules within a GraphModule to their corresponding nodes and stiches all the nodes together. 
+This is needed as `torch._export` serialization cannot handle serializing and deserializing of submodules (`call_module` nodes). 
+
+NOTE: This way of saving the models using `ExportedProgram` is experimental. Here is a known issue : https://github.com/pytorch/TensorRT/issues/2341
+
+2) Torchscript IR
+
+  In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
+  This behavior stays the same in 2.X versions as well. 
+
+  .. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = torch.randn((1, 3, 224, 224)).cuda()
+    trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+    torch.jit.save(trt_ts, "trt_model.ts")
+
+    # Later, you can load it and run inference
+    model = torch.jit.load("trt_model.ts").cuda()
+    model(inputs)
+  
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index 43cdeb6f9c..c10c4b61c9 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: 'v2.2.0.dev0+b50290d',
+    VERSION: 'v2.2.0.dev0+d375d10',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/cli/torchtrtc.html b/docs/cli/torchtrtc.html
index 20b090b6d0..dcc5baf340 100644
--- a/docs/cli/torchtrtc.html
+++ b/docs/cli/torchtrtc.html
@@ -10,7 +10,7 @@
 
   
   
-  torchtrtc — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torchtrtc — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/contributors/conversion.html b/docs/contributors/conversion.html
index da3d031975..bfd44e88c7 100644
--- a/docs/contributors/conversion.html
+++ b/docs/contributors/conversion.html
@@ -10,7 +10,7 @@
 
   
   
-  Conversion Phase — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Conversion Phase — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/contributors/fx_converters.html b/docs/contributors/fx_converters.html
new file mode 100644
index 0000000000..d1863081c2
--- /dev/null
+++ b/docs/contributors/fx_converters.html
@@ -0,0 +1,928 @@
+
+
+
+
+
+  
+
+  
+  
+
+  
+  
+  Dynamo Converters — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
+  
+
+  
+  
+  
+  
+
+  
+
+  
+  
+    
+
+  
+
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+    
+    
+  
+    
+    
+  
+
+  
+  
+
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+
+
+
+
+
+   
+
+    
+
+    
    
+      Table of Contents
+      
+    
    
+
+    
+
+    
    
+      
    
+        
    
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
+
+  
      
+    
+      
    • 
+        
+          
+            Docs
+          
+         >
+      
      • 
+
+        
+      
    • Dynamo Converters
      • 
+    
+    
+      
    • 
+        
+            
+            
+          
+        
+      
      • 
+    
+  
    
+
+  
+
    
+        
    
+
+        
    
+          Shortcuts
+        
    
+      
    
+
+      
    
+        
    
+
+        
+          
+          
+          
+          
+          
    
+          
+            
    
+             
    
+              
+  
    
+
    Dynamo Converters
    
+
    The dynamo converter library in Torch-TensorRT is located in TensorRT/py/torch_tensorrt/dynamo/conversion.
    
+
    
+
    
+
    Steps
    
+
    
+
    Operation Set
    
+
    The converters in dynamo are produced by aten_trace and falls under aten_ops_converters ( FX earlier had acc_ops_converters, aten_ops_converters or nn_ops_converters depending on the trace through which it was produced).  The converters are registered using  dynamo_tensorrt_converter for dynamo. The function decorated
+has the arguments - network, target, args, kwargs, name,  which is common across all the operators schema.
+These functions are mapped in the aten converter registry dictionary (at present a compilation of FX and dynamo converters, FX will be deprecated soon), with key as the function target name.
    
+
    
+
      
+
    • aten_trace is produced by torch_tensorrt.dynamo.trace(..) for the export path and torch_tensorrt.compile(ir=dynamo) for the compile path.
      • 
+
    
+
    The export path makes use of aten_tracer whereas the alternate trace in compile is produced by the AOT Autograd library.
+Both these simplify the torch operators to reduced set of Aten operations.
    
+
    
+
    As mentioned above, if you would like to add a new converter, its implementation will be included in TensorRT/py/torch_tensorrt/dynamo/conversion/impl
+Although there is a corresponding implementation of the converters  included in the common implementation library present in TensorRT/py/torch_tensorrt/fx/impl for FX converters, this documentation focuses on the implementation of the aten_ops converters in dynamo.
    
+
    
+
    
+
    Converter implementation
    
+
    In this section, we illustrate the steps to be implemented for writing a converter. We divide them according to activation, operator, lowering pass implementation or evaluator.
+Each of them is detailed with the help of an example
    
+
    
+
      
+
    • Registration
      
+
      
+
      The converter needs to be registered with the appropriate op code in the dynamo_tensorrt_converter.
      
+
        
+
      • Activation type
        
+
        
+
        Example: leaky_relu
        
+
          
+
        • aten_ops_converters: Dynamo_converters
          
+
          
+
          Define in py/torch_tensorrt/dynamo/conversion/aten_ops_converters. One needs to register the opcode generated in the trace with dynamo_tensorrt_converter decorator. Op code to be used for the registration or the converter registry key in this case is torch.ops.aten.leaky_relu.default
          
+
          
+
          @dynamo_tensorrt_converter(torch.ops.aten.leaky_relu.default)
+    def aten_ops_leaky_relu(
+        network: TRTNetwork,
+        target: Target,
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        name: str,
+    ) -> Union[TRTTensor, Sequence[TRTTensor]]:
+            return activation.leaky_relu(network, target, SourceIR.ATEN, name, args[0], args[1])
+
          
+
          
+
          
+
          
+
          • 
+
        
+
        The tensorrt_converter (used for FX registration) and dynamo_tensorrt_converter are similar decorator functions with some differences.
        
+
          
+
        1. Both register the converters in the registeries (python dictionaries) - CONVERTERS and DYNAMO_CONVERTERS respectively. These are two dictioneries which are concatenated to form the overall converter registry
          2. 
+
        2. The dictionary is keyed on the OpOverLoad which is mentioned in more detail below with examples
          3. 
+
        3. Both return the decorated converter implementation
          4. 
+
        4. The CONVERTERS directly registers the decorated converter_implementation function, while DYNAMO_CONVERTERS has additionational arguments and registers the ConverterSupport object
          5. 
+
        5. The additional arguments are:
          
+
          
+
            
+
          1. key: Node target for which the converter is implemented for (for example, torch.ops.aten.leaky_relu.Tensor)
            2. 
+
          2. enabled: Whether the converter should be enabled/cached or not
            3. 
+
          3. capability_validator: Function which evaluates whether a node is valid for conversion by the decorated converter. It defaults to None, implying the capability_validator function is always true. This means all nodes of “key” kind can be supported by this converter by default. See embedding example for more details
            4. 
+
          4. priority: Converter’s level of priority relative to other converters with the same target
            5. 
+
          
+
          
+
          6. 
+
        6. The ConverterSupport is a compilation of converter_implementation and capability_validator.
          7. 
+
        
+
        The function decorated by tensorrt_converter and dynamo_tensorrt_converter has the following arguments which are automatically generated by the trace functions mentioned above.
        
+
          
+
        1. network : Node in the form of call_module or call_function having the target as the key
          2. 
+
        2. target: Target key in the call_module or call_function above. eg: torch.ops.aten_.leaky_relu.default. Note that torch.ops.aten._leaky_relu is the OpOverloadPacket while torch.ops.aten_.leaky_relu.default is OpOverload.
          3. 
+
        3. args: The arguments passed in the call_module or call_function above
          4. 
+
        4. kwargs: The kwargs passed in the call_module or call_function above
          5. 
+
        5. name: String containing the name of the target
          6. 
+
        
+
        As a user writing new converters, one just needs to take care that the approriate arguments are extracted from the trace generated to the implementation function in the implementation lib function activation.leaky_relu (which we will discuss below in detail).
        
+
        
+
        • 
+
      • Operation type
        
+
        
+
        Example: fmod
        
+
        It follows the same steps as the above converter. In this case the opcode is torch.ops.aten.fmod.Scalar or torch.ops.aten.fmod.Tensor.
+Hence both the opcodes are registered in py/torch_tensorrt/dynamo/conversion/aten_ops_converters.
+Note that torch.ops.aten.fmod is the OpOverLoadPacket while the registry is keyed on torch.ops.aten.fmod.Scalar or torch.ops.aten.fmod.Tensor, which is OpOverLoad
        
+
        Example: embedding
        
+
        It follows the same steps as the above converter. In this case the opcode is torch.ops.aten.embedding.default.
+There are some converters which have special cases to be accounted for. In those cases, one should use capability_validators to register the converter using @dynamo_tensorrt_converter
+We illustrate this through torch.ops.aten.embedding.default. It has parameters - scale_grad_by_freq and sparse which are not currently supported by the implementation.
+In such cases we can write validator embedding_param_validator which implements that given those paramters the converter is not supported and register the converter by
        
+
        
+
        
+
        So if there is a new converter in which certain special cases are not to be supported then they can be specified in the capability_validator.
        
+
        
+
        • 
+
      • Evaluator type
        
+
        
+
        Example: operator.getitem
        
+
        Evaluators are categorized as so since they do not make any modification to the graph. This is implemented in py/torch_tensorrt/dynamo/conversion/op_evaluators.py, with the corresponding capbility_validator.
+The opcode is operator.getitem.
        
+
        
+
        • 
+
      
+
      
+
      • 
+
    • Implementation Library
      
+
      
+
      The dynamo converters would be located in py/torch_tensorrt/dynamo/conversion/impl
      
+
        
+
      • Activation
        
+
        
+
        Example: leaky_relu
        
+
        The implementation is to be placed in present in py/torch_tensorrt/dynamo/conversion/impl/activation.py. This is where all the activation functions are defined and implemented.
        
+
        def leaky_relu(
+    network: TRTNetwork,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input_val: TRTTensor,
+    alpha: Optional[Any],
+):
+    #implementation
+
        
+
        
+
        The implementation function has the following arguments.
        
+
          
+
        1. network : network passed from the decorated function registration
          2. 
+
        2. target: target passed from the decorated function registration
          3. 
+
        3. source_ir: Enum attribute. SourceIR enum is defined in py/torch_tensorrt/dynamo/conversion/impl/converter_utils
          4. 
+
        4. name: name passed from the decorated function registration
          5. 
+
        5. input_val: Approriate arguments extracted from the decorated function registration from args or kwargs
          6. 
+
        6. alpha: Approriate arguments extracted from the decorated function registration from args or kwargs. If not None, it will set the alpha attribute of the created TensorRT activation layer eg: Used in leaky_relu, elu, hardtanh
          7. 
+
        7. beta: Approriate arguments extracted from the decorated function registration from args or kwargs. If not None, it will set the beta attribute of the created TensorRT activation layer eg: Used in hardtanh
          8. 
+
        8. dyn_range_fn: A optional function which takes the dynamic range of a TensorRT Tensor and returns the output dynamic range
          9. 
+
        
+
        The implementation functions call the convert_activation function in py/torch_tensorrt/dynamo/conversion/impl/activation.py. This function will add the approriate activation layer via network.add_activation.
        
+
        
+
        • 
+
      • Operator
        
+
        
+
        The implementation is to be placed in py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py for dynamo. This is where all the elementwise functions are defined and implemented.
+For a new operator, one should identify the category to which it belongs. Following are some examples
        
+
          
+
        1. Elementwise operators like fmod is present in py/torch_tensorrt/dynamo/conversion/impl/elementwise. The py/torch_tensorrt/dynamo/conversion/impl/elementwise/base contains base functions for elementwise operator.
          2. 
+
        2. Unary operators like sqrt will be present in py/torch_tensorrt/dynamo/conversion/impl/unary. The py/torch_tensorrt/dynamo/conversion/impl/unary/base contains base functions for unary operator.
          3. 
+
        3. Normalization operators like softmax, layer_norm, batch_norm will be present in py/torch_tensorrt/dynamo/conversion/impl/normalization. Since there are no base operations common to all, there is no base file. But one can choose to implement a base file, if there are common functions across all normalization operations
          4. 
+
        4. Individual operators like slice, select, where, embedding will be present in py/torch_tensorrt/dynamo/conversion/impl/*.py. They will have individual operator implementation with the same API structure as above but with different individual arguments
          5. 
+
        
+
        Please note that the above operators would have common functions to be implemented which should be placed in
+py/torch_tensorrt/dynamo/conversion/impl/converter_utils.py
        
+
        
+
        • 
+
      
+
      
+
      • 
+
    • Lowering type
      
+
      
+
      There are some converters which can be decomposed into suboperations and need not have seperate converter registration.
+Such converters can be implemented via lowering passes
      
+
      Example: addmm
      
+
      The decompositions are registered via register_decomposition in py/torch_tensorrt/dynamo/backend/lowering/_decompositions.py
+We define addmm_replacement and replace it with the torch ops, which will have their corresponding converters called.
      
+
      @register_decomposition(torch.ops.aten.addmm, registry=DECOMPOSITIONS)
+def addmm_replacement(
+    input_: torch.Tensor, mat1: torch.Tensor, mat2: torch.Tensor, *, beta=1, alpha=1
+) -> torch.Tensor:
+    return torch.add(
+        torch.mul(input_, beta), torch.mul(torch.matmul(mat1, mat2), alpha)
+    )
+
      
+
      
+
      Note that there are some pre-existing dynamo decompositions in torch directory, in which case they should be used,
+In that case please enable the decompositions in py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py in torch_enabled_decompositions.
+Similarly you can choose to disable any in torch_disabled_decompositions. Please note that the ones already defined in the lowering will take precedence over torch lowering ops.
      
+
      
+
      • 
+
    
+
    
+
    
+
    
+
    Tests
    
+
      
+
    • Dynamo testing:
      
+
      
+
      Dynamo tests are present for the lowering ops in tests/py/dynamo/lowering/test_decompositions.py. The above converters will soon be ported to dynamo tests
      
+
        
+
      1. Compare the results for fx.symbolic_trace and torch_tensorrt.dynamo.compile.
        2. 
+
      2. Test for the expected_op and the unexpected_op.
        
+
        
+
          
+
        1. expected_op: Operations the operations are lowered to. eg: mul and add for addmm
          2. 
+
        2. Note that specify that disable_passes= True for cases where you would not want lowering passes (which should be the default when testing converters)
          3. 
+
        3. unexpected_op: Original operation. eg: addmm for addmm
          4. 
+
        
+
        
+
        3. 
+
      
+
      
+
      • 
+
    
+
    The tests should fail if any of the above two conditions fail
    
+
    
+
    
+
+
+             
    
+             
+            
    
+            
    
+  
+
+  
+
+    
    
+
+  
+
+  
    
+    
    
+        © Copyright 2022, NVIDIA Corporation.
+
+    
    
+  
    
+    
+      
    
+        Built with Sphinx using a theme provided by Read the Docs.
+      
    
+     
+
+
    
+
+          
    
+        
    
+
+        
    
+          
+        
    
+      
    
+    
    
+
+  
+
+
+  
+
+     
+       
+         
+         
+         
+         
+         
+         
+         
+     
+
+  
+
+  
+  
+  
+  
+
+   
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
    Docs
    
+          
    Access comprehensive developer documentation for PyTorch
    
+          View Docs
+        
    
+
+        
    
+          
    Tutorials
    
+          
    Get in-depth tutorials for beginners and advanced developers
    
+          View Tutorials
+        
    
+
+        
    
+          
    Resources
    
+          
    Find development resources and get your questions answered
    
+          View Resources
+        
    
+      
    
+    
    
+  
    
+
+  
+
+  
    
+  
    
+    
    To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.
    
+    
+  
    
+
    
+
+  
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
+          
+        
    
+      
    
+    
    
+
+    
    
+      
    
+        
+      
    
+    
    
+  
    
+
+  
+
+  
+
+  
+
+
\ No newline at end of file
diff --git a/docs/contributors/lowering.html b/docs/contributors/lowering.html
index 7e2c0ff3da..3420c01f53 100644
--- a/docs/contributors/lowering.html
+++ b/docs/contributors/lowering.html
@@ -10,7 +10,7 @@
 
   
   
-  Lowering Phase — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Lowering Phase — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/contributors/partitioning.html b/docs/contributors/partitioning.html
index fe77c8a83e..893a7de544 100644
--- a/docs/contributors/partitioning.html
+++ b/docs/contributors/partitioning.html
@@ -10,7 +10,7 @@
 
   
   
-  Partitioning Phase — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Partitioning Phase — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/contributors/phases.html b/docs/contributors/phases.html
index 43bff496b0..b549627938 100644
--- a/docs/contributors/phases.html
+++ b/docs/contributors/phases.html
@@ -10,7 +10,7 @@
 
   
   
-  Compiler Phases — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Compiler Phases — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -223,7 +223,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -267,6 +267,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -302,6 +304,7 @@
 
 
    Indices
    
@@ -411,7 +414,7 @@ 
    Conversion
 
    Compilation and Runtime
    
-
    Deploying Torch-TensorRT Programs
    
+
    Saving models compiled with Torch-TensorRT
    
 
    The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
 takes a serialized engine and instantiates it within a engine manager, then the compiler will
 build out a JIT graph that references this engine and wraps it in a module to return to the user.
diff --git a/docs/contributors/runtime.html b/docs/contributors/runtime.html
index 764bb45325..3af5ed66d6 100644
--- a/docs/contributors/runtime.html
+++ b/docs/contributors/runtime.html
@@ -10,7 +10,7 @@
 
   
   
-  Runtime Phase — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Runtime Phase — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/contributors/system_overview.html b/docs/contributors/system_overview.html
index 900775df73..517f5fba58 100644
--- a/docs/contributors/system_overview.html
+++ b/docs/contributors/system_overview.html
@@ -10,7 +10,7 @@
 
   
   
-  System Overview — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  System Overview — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
@@ -434,7 +437,7 @@ 
    Conversion
 
    Compilation and Runtime
    
-
    Deploying Torch-TensorRT Programs
    
+
    Saving models compiled with Torch-TensorRT
    
 
    The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
 takes a serialized engine and instantiates it within a engine manager, then the compiler will
 build out a JIT graph that references this engine and wraps it in a module to return to the user.
diff --git a/docs/contributors/useful_links.html b/docs/contributors/useful_links.html
index 292958afae..6b581e921e 100644
--- a/docs/contributors/useful_links.html
+++ b/docs/contributors/useful_links.html
@@ -10,7 +10,7 @@
 
   
   
-  Useful Links for Torch-TensorRT Development — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Useful Links for Torch-TensorRT Development — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -40,7 +40,7 @@
     
     
     
-    
+    
   
     
+    
+  
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+
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+  
+
+
+
    
+
+
+
+   
+
+    
+
+    
    
+      Table of Contents
+      
+    
    
+
+    
+
+    
    
+      
    
+        
    
+          
+
+
+
+
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+
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+
+
+
    
+
+  
      
+    
+      
    • 
+        
+          
+            Docs
+          
+         >
+      
      • 
+
+        
+      
    • Writing Dynamo ATen Lowering Passes
      • 
+    
+    
+      
    • 
+        
+            
+            
+          
+        
+      
      • 
+    
+  
    
+
+  
+
    
+        
    
+
+        
    
+          Shortcuts
+        
    
+      
    
+
+      
    
+        
    
+
+        
+          
+          
+          
+          
+          
    
+          
+            
    
+             
    
+              
+  
    
+
    Writing Dynamo ATen Lowering Passes
    
+
    
+
    Basics of a Lowering Pass
    
+
    ATen lowering passes are Python functions which take as input a graph of ATen operators, apply some desired modification such as operator coalescing/fusion, operator replacement, subgraph rewriting, custom operator insertion, or other operation on a torch.fx.GraphModule, then return the modified graph to the caller. These lowering passes generally modify the graph in-place and return the same input object.
    
+
    
+
    
+
    Lowering Pass Requirements
    
+
    An ATen lowering pass function in Torch-TRT must satisfy two requirements:
+- The function must take as input a torch.fx.GraphModule and a sequence of torch Tensors, Sequence[torch.Tensor], and return the lowered torch.fx.GraphModule
+- The function must leave the graph in a valid and invoke-able state, including performing any necessary linting and recompilation
    
+
    See this link for information on Graph Manipulations in FX. See below for an example of a lowering pass which repairs graphs that have inputs which are also outputs, a disallowed configuration for TRT Engines.
    
+
    
+
    
+
    Example Lowering Pass
    
+
    def repair_input_as_output(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+    """Repair scenarios where inputs are also outputs of the graph
+
+    TRT does not allow such cases, so we insert a clone (identity) layer
+    """
+    modified_graph = False
+
+    # Extract graph placeholder Tensors
+    placeholders = [
+        node
+        for node in gm.graph.nodes
+        if (
+            node.op == "placeholder"
+            and isinstance(node.type, type)
+            and issubclass(node.type, torch.Tensor)
+        )
+    ]
+
+    for placeholder in placeholders:
+        # If any placeholder has any users which are direct graph outputs
+        if len(placeholder.users) >= 1 and any(
+            user.op == "output" for user in placeholder.users
+        ):
+            modified_graph = True
+
+            # Get direct graph outputs which are direct uses of placeholders
+            direct_outputs = [user for user in placeholder.users if user.op == "output"]
+
+            # Insert clone node for placeholder to ensure
+            # placeholder is not a direct output
+            with gm.graph.inserting_after(placeholder):
+                cloned_placeholder = gm.graph.call_function(
+                    torch.ops.aten.clone.default,
+                    args=(placeholder,),
+                )
+
+            # Replace placeholder as output with cloned version
+            for output in direct_outputs:
+                output.replace_input_with(placeholder, cloned_placeholder)
+
+    # If the graph was modified, clean up the graph and ensure it is up-to-date
+    if modified_graph:
+        gm.graph.eliminate_dead_code()
+        gm.graph.lint()
+        gm.recompile()
+        logger.debug(f"Graph after repair_input_as_output:\n{gm.graph}")
+
+    return gm
+
    
+
    
+
    
+
    
+
    Registering Lowering Passes
    
+
    Lowering passes are currently registered in py/torch_tensorrt/dynamo/lowering/passes/__init__.py, using the torch.fx.passes.pass_manager.PassManager utility to assemble the list of passes in a desired order. New passes added directly to that list will be applied to graphs in the Torch-TensorRT torch.compile backend. Currently, we offer an ATen lowering pass registration decorator for convenience, which can be invoked either directly, or with the optional index keyword argument which controls where in the pass list the lowering pass will be inserted.
    
+
    For instance, to insert the pass at the default location (end of the list), the following code can be used:
    
+
    @_aten_lowering_pass
+def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+    ...
+
    
+
    
+
    Alternatively, to insert the pass at a custom index (such as the front of the list) in the passlist, the following code can be used:
    
+
    @_aten_lowering_pass(index=0)
+def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+    ...
+
    
+
    
+
    There are also provided utilities in torch_tensorrt.dynamo.lowering.passes for displaying the currently-available lowering pass list, applying those passes to an arbitrary torch.fx.GraphModule, and removing the lowering pass at a specific index.
    
+
    # Print all lowering passes in the list
+print(dump_lowering_passes())
+
+# Apply lowering passes to a GraphModule
+apply_lowering_passes(graph_module, sample_inputs)
+
+# Remove the lowering pass at index 1
+_remove_lowering_pass(index=1)
+
    
+
    
+
    Note: The above APIs are subject to change, as the lowering pass system evolves.
    
+
    
+
    
+
+
+             
    
+             
+            
    
+            
+
+          
    
+        
    
+
+        
    
+          
+        
    
+      
    
+    
    
+
+  
+
+
+  
+
+     
+       
+         
+         
+         
+         
+         
+         
+         
+     
+
+  
+
+  
+  
+  
+  
+
+   
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
    Docs
    
+          
    Access comprehensive developer documentation for PyTorch
    
+          View Docs
+        
    
+
+        
    
+          
    Tutorials
    
+          
    Get in-depth tutorials for beginners and advanced developers
    
+          View Tutorials
+        
    
+
+        
    
+          
    Resources
    
+          
    Find development resources and get your questions answered
    
+          View Resources
+        
    
+      
    
+    
    
+  
    
+
+  
+
+  
    
+  
    
+    
    To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.
    
+    
+  
    
+
    
+
+  
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
+          
+        
    
+      
    
+    
    
+
+    
    
+      
    
+        
+      
    
+    
    
+  
    
+
+  
+
+  
+
+  
+
+
\ No newline at end of file
diff --git a/docs/genindex.html b/docs/genindex.html
index c0ccc64b71..b5b55e2899 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -9,7 +9,7 @@
   
   
   
-  Index — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Index — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -222,7 +222,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -266,6 +266,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
@@ -720,6 +723,8 @@ 
    T
    
       
  • torch_tensorrt::DataType::Value::kBool (C++ enumerator)
 
    • 
       
  • torch_tensorrt::DataType::Value::kChar (C++ enumerator)
+
    • 
+      
  • torch_tensorrt::DataType::Value::kDouble (C++ enumerator)
 
    • 
       
  • torch_tensorrt::DataType::Value::kFloat (C++ enumerator)
 
    • 
diff --git a/docs/getting_started/getting_started_with_cpp_api.html b/docs/getting_started/getting_started_with_cpp_api.html
index bef6245f9c..09bbc604e5 100644
--- a/docs/getting_started/getting_started_with_cpp_api.html
+++ b/docs/getting_started/getting_started_with_cpp_api.html
@@ -10,7 +10,7 @@
 
   
   
-  Using Torch-TensorRT in C++ — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Using Torch-TensorRT in C++ — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/getting_started/getting_started_with_python_api.html b/docs/getting_started/getting_started_with_python_api.html
index 967bd74fa5..fdd68ad424 100644
--- a/docs/getting_started/getting_started_with_python_api.html
+++ b/docs/getting_started/getting_started_with_python_api.html
@@ -10,7 +10,7 @@
 
   
   
-  Using Torch-TensorRT in Python — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Using Torch-TensorRT in Python — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/getting_started/getting_started_with_windows.html b/docs/getting_started/getting_started_with_windows.html
index 4b4615d160..0d6d98cc07 100644
--- a/docs/getting_started/getting_started_with_windows.html
+++ b/docs/getting_started/getting_started_with_windows.html
@@ -10,7 +10,7 @@
 
   
   
-  Building Torch-TensorRT on Windows — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Building Torch-TensorRT on Windows — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/getting_started/installation.html b/docs/getting_started/installation.html
index 7e1620c033..0dc9d5194c 100644
--- a/docs/getting_started/installation.html
+++ b/docs/getting_started/installation.html
@@ -10,7 +10,7 @@
 
   
   
-  Installation — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Installation — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/index.html b/docs/index.html
index 673c245a1d..200da10ec7 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -10,7 +10,7 @@
 
   
   
-  Torch-TensorRT — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Torch-TensorRT — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -224,7 +224,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -268,6 +268,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -303,6 +305,7 @@
 
 
    Indices
    
@@ -416,7 +419,9 @@ 
    User GuideCreating a TorchScript Module
    
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
-
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • saving_models
    • 
+
  • dynamic_shapes
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -469,6 +474,7 @@ 
    Contributor Documentation
 
  • System Overview
    • 
 
  • Writing Converters
    • 
+
  • Writing Dynamo ATen Lowering Passes
    • 
 
  • Useful Links for Torch-TensorRT Development
    • 
 
 
    
diff --git a/docs/indices/supported_ops.html b/docs/indices/supported_ops.html
index 1ab704dfed..b79952f615 100644
--- a/docs/indices/supported_ops.html
+++ b/docs/indices/supported_ops.html
@@ -10,7 +10,7 @@
 
   
   
-  Operators Supported — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Operators Supported — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -224,7 +224,7 @@
               
               
                 
    
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@@ -268,6 +268,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
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  • Post Training Quantization (PTQ)
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  • Deploying Torch-TensorRT Programs
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+
  • Saving models compiled with Torch-TensorRT
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+
  • Dynamic shapes with Torch-TensorRT
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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@@ -303,6 +305,7 @@
 
 
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diff --git a/docs/objects.inv b/docs/objects.inv
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Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 923a1db42d..d0bcaf8927 100644
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@@ -9,7 +9,7 @@
   
   
   
-  Python Module Index — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Python Module Index — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
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@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
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+
  • Dynamic shapes with Torch-TensorRT
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  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/py_api/fx.html b/docs/py_api/fx.html
index b386826388..a342e7d857 100644
--- a/docs/py_api/fx.html
+++ b/docs/py_api/fx.html
@@ -10,7 +10,7 @@
 
   
   
-  torch_tensorrt.fx — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.fx — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
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  • Torch-TensorRT (FX Frontend) User Guide
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  • Post Training Quantization (PTQ)
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  • Deploying Torch-TensorRT Programs
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+
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  • DLA
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@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/py_api/logging.html b/docs/py_api/logging.html
index f8ee811f48..4b11e569dc 100644
--- a/docs/py_api/logging.html
+++ b/docs/py_api/logging.html
@@ -10,7 +10,7 @@
 
   
   
-  torch_tensorrt.logging — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.logging — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
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@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
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@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/py_api/ptq.html b/docs/py_api/ptq.html
index 01cf608ce9..a548be7048 100644
--- a/docs/py_api/ptq.html
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@@ -10,7 +10,7 @@
 
   
   
-  torch_tensorrt.ptq — Torch-TensorRT v2.2.0.dev0+b50290d documentation
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@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
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@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
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  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
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+
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+
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@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/py_api/torch_tensorrt.html b/docs/py_api/torch_tensorrt.html
index ef0d5dee2c..a6e4aa7815 100644
--- a/docs/py_api/torch_tensorrt.html
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@@ -10,7 +10,7 @@
 
   
   
-  torch_tensorrt — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
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@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
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  • Deploying Torch-TensorRT Programs
    • 
+
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+
  • Dynamic shapes with Torch-TensorRT
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  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
@@ -562,7 +565,7 @@ 
    Classes
 
    
-dtype: _enums.dtype = <dtype.unknown: 6>
    
+dtype: _enums.dtype = <dtype.unknown: 7>
 
    torch_tensorrt.dtype.float32)
    
 
    
 
    Type
    
@@ -730,6 +733,8 @@ 
    Enums
 
    int32 : 32 bit integer number
    
 
    long : 64 bit integer number
    
 
    int64 : 64 bit integer number
    
+
    double : 64 bit floating point number
    
+
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    bool : Boolean value
    
 
    unknown : Unknown data type
    
 
    
diff --git a/docs/py_api/ts.html b/docs/py_api/ts.html
index b93cc2671f..20b0d7fb67 100644
--- a/docs/py_api/ts.html
+++ b/docs/py_api/ts.html
@@ -10,7 +10,7 @@
 
   
   
-  torch_tensorrt.ts — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  torch_tensorrt.ts — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
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@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
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+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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@@ -304,6 +306,7 @@
 
 
    Indices
    
@@ -609,7 +612,7 @@ 
    Functions
 
    
-torch_tensorrt.ts.TensorRTCompileSpec(inputs: typing.Optional[typing.List[torch.Tensor | torch_tensorrt._Input.Input]] = None, input_signature: typing.Optional[typing.Any] = None, device: torch.device | torch_tensorrt._Device.Device = Device(type=DeviceType.GPU, gpu_id=0), disable_tf32: bool = False, sparse_weights: bool = False, enabled_precisions: typing.Optional[typing.Set[torch.dtype | torch_tensorrt._C.dtype]] = None, refit: bool = False, debug: bool = False, capability: torch_tensorrt._C.EngineCapability = <EngineCapability.default: 0>, num_avg_timing_iters: int = 1, workspace_size: int = 0, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, truncate_long_and_double: bool = False, calibrator: object = None, allow_shape_tensors: bool = False) → <torch.ScriptClass object at 0x7f132f8f8030>[source]
    
+torch_tensorrt.ts.TensorRTCompileSpec(inputs: typing.Optional[typing.List[torch.Tensor | torch_tensorrt._Input.Input]] = None, input_signature: typing.Optional[typing.Any] = None, device: torch.device | torch_tensorrt._Device.Device = Device(type=DeviceType.GPU, gpu_id=0), disable_tf32: bool = False, sparse_weights: bool = False, enabled_precisions: typing.Optional[typing.Set[torch.dtype | torch_tensorrt._C.dtype]] = None, refit: bool = False, debug: bool = False, capability: torch_tensorrt._C.EngineCapability = <EngineCapability.default: 0>, num_avg_timing_iters: int = 1, workspace_size: int = 0, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, truncate_long_and_double: bool = False, calibrator: object = None, allow_shape_tensors: bool = False) → <torch.ScriptClass object at 0x7fbed790f4b0>[source]
 
    Utility to create a formated spec dictionary for using the PyTorch TensorRT backend
    
 
    
 
    Keyword Arguments
    
diff --git a/docs/search.html b/docs/search.html
index f4ea6db822..1d7341c4e4 100644
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-  Search — Torch-TensorRT v2.2.0.dev0+b50290d documentation
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  • Torch-TensorRT (FX Frontend) User Guide
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  • Post Training Quantization (PTQ)
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  • Deploying Torch-TensorRT Programs
    • 
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  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -301,6 +303,7 @@
 
 
    Indices
    
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\ No newline at end of file
diff --git a/docs/src/pytorch-sphinx-theme/docs/changelog.html b/docs/src/pytorch-sphinx-theme/docs/changelog.html
index e280086f2d..e6034fefc8 100644
--- a/docs/src/pytorch-sphinx-theme/docs/changelog.html
+++ b/docs/src/pytorch-sphinx-theme/docs/changelog.html
@@ -10,7 +10,7 @@
 
   
   
-  Changelog — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Changelog — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -223,7 +223,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -267,6 +267,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -302,6 +304,7 @@
 
 
    Indices
    
diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html
index 1c03ce006d..5acd65ff97 100644
--- a/docs/src/pytorch-sphinx-theme/docs/configuring.html
+++ b/docs/src/pytorch-sphinx-theme/docs/configuring.html
@@ -10,7 +10,7 @@
 
   
   
-  Configuration — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Configuration — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -223,7 +223,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -267,6 +267,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -302,6 +304,7 @@
 
 
    Indices
    
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
index c569774dec..82e3a59abf 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/api.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
@@ -10,7 +10,7 @@
 
   
   
-  5. :mod:`test_py_module` — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  5. :mod:`test_py_module` — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -223,7 +223,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -267,6 +267,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -302,6 +304,7 @@
 
 
    Indices
    
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
index c5bb04a5f3..a69822106b 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
@@ -12,7 +12,7 @@
 
   
   
-  3. Paragraph Level Markup — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  3. Paragraph Level Markup — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
@@ -582,7 +585,7 @@ 
    3.4.4. 
 
    3.4.5. Code Blocks
    
 
    # parsed-literal test
-curl -O http://someurl/release-v2.2.0.dev0+b50290d.tar-gz
    
+curl -O http://someurl/release-v2.2.0.dev0+d375d10.tar-gz
    
 
    
 
    Code Blocks can have captions.
    
 
    {
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
index 7aafe5496f..ecca6d5fbc 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
@@ -10,7 +10,7 @@
 
   
   
-  4. Lists & Tables — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  4. Lists & Tables — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
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+
  • Saving models compiled with Torch-TensorRT
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+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
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index 59f808985e..81ccd49844 100644
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+++ b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
@@ -10,7 +10,7 @@
 
   
   
-  1. Long Sticky Nav — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  1. Long Sticky Nav — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
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+
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+
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
index 92c5466376..9fa31c0236 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
@@ -10,7 +10,7 @@
 
   
   
-  1. Structural Elements — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  1. Structural Elements — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
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+
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+
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  • DLA
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diff --git a/docs/src/pytorch-sphinx-theme/docs/index.html b/docs/src/pytorch-sphinx-theme/docs/index.html
index a7d2e9a58e..d3d61edf8b 100644
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+  <no title> — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
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+
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+
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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diff --git a/docs/src/pytorch-sphinx-theme/docs/installing.html b/docs/src/pytorch-sphinx-theme/docs/installing.html
index e6ea516166..9639169812 100644
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+++ b/docs/src/pytorch-sphinx-theme/docs/installing.html
@@ -10,7 +10,7 @@
 
   
   
-  Installation — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Installation — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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    • 
 
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    • 
+
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+
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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    Indices
    
diff --git a/docs/tutorials/_rendered_examples/dynamo/index.html b/docs/tutorials/_rendered_examples/dynamo/index.html
index 6c8317cef4..c1e942cc61 100644
--- a/docs/tutorials/_rendered_examples/dynamo/index.html
+++ b/docs/tutorials/_rendered_examples/dynamo/index.html
@@ -10,7 +10,7 @@
 
   
   
-  Dynamo / torch.compile — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Dynamo / torch.compile — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
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    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
index 46c06feb0b..5a74b37c20 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
@@ -10,7 +10,7 @@
 
   
   
-  Torch Compile Advanced Usage — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Torch Compile Advanced Usage — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
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    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
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  • Using Torch-TensorRT Directly From PyTorch
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    Indices
    
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html
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@@ -10,7 +10,7 @@
 
   
   
-  Compiling ResNet using the Torch-TensorRT torch.compile Backend — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Compiling ResNet using the Torch-TensorRT torch.compile Backend — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
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    • 
+
  • Saving models compiled with Torch-TensorRT
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+
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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index 3cb2c100af..c2271caae4 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html
@@ -10,7 +10,7 @@
 
   
   
-  Compiling a Transformer using torch.compile and TensorRT — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Compiling a Transformer using torch.compile and TensorRT — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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    • 
 
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+
  • Saving models compiled with Torch-TensorRT
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+
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  • Using Torch-TensorRT Directly From PyTorch
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  • DLA
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-  Torch-TensorRT Tutorials — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Torch-TensorRT Tutorials — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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    • 
 
  • Post Training Quantization (PTQ)
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    • 
+
  • Saving models compiled with Torch-TensorRT
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+
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diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html
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+++ b/docs/tutorials/notebooks.html
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-  Example notebooks — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Example notebooks — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
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    • 
+
  • Saving models compiled with Torch-TensorRT
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+
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-  Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.2.0.dev0+b50290d documentation
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  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
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    • 
+
  • Saving models compiled with Torch-TensorRT
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+
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  • Using Torch-TensorRT Directly From PyTorch
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diff --git a/docs/user_guide/creating_torchscript_module_in_python.html b/docs/user_guide/creating_torchscript_module_in_python.html
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@@ -10,7 +10,7 @@
 
   
   
-  Creating a TorchScript Module — Torch-TensorRT v2.2.0.dev0+b50290d documentation
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    • 
 
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    • 
 
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    • 
+
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+
+
+
+
+
+  
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+  
+  
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+  
+  
+  Dynamic shapes with Torch-TensorRT — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
+  
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+  
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+   
+
+    
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+    
    
+      Table of Contents
+      
+    
    
+
+    
+
+    
    
+      
    
+        
    
+          
+
+
+
+
+
+
+
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+
    
+
+  
      
+    
+      
    • 
+        
+          
+            Docs
+          
+         >
+      
      • 
+
+        
+      
    • Dynamic shapes with Torch-TensorRT
      • 
+    
+    
+      
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+        
+            
+            
+          
+        
+      
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+    
+  
    
+
+  
+
    
+        
    
+
+        
    
+          Shortcuts
+        
    
+      
    
+
+      
    
+        
    
+
+        
+          
+          
+          
+          
+          
    
+          
+            
    
+             
    
+              
+  
    
+
    Dynamic shapes with Torch-TensorRT
    
+
    By default, you can run a pytorch model with varied input shapes and the output shapes are determined eagerly.
+However, Torch-TensorRT is an AOT compiler which requires some prior information about the input shapes to compile and optimize the model.
+In the case of dynamic input shapes, we must provide the (min_shape, opt_shape, max_shape) arguments so that the model can be optimized for
+these range of input shapes. An example usage of static and dynamic shapes is as follows.
    
+
    NOTE: The following code uses dynamo IR. Incase of Torchscript IR, please swap out ir=dynamo with ir=ts and the behavior is exactly the same.
    
+
    import torch
+import torch_tensorrt
+
+model = MyModel().eval().cuda()
+# Compile with static shapes
+inputs = torch_tensorrt.Input(shape=[1, 3, 224, 224], dtype=torch.float32)
+# or compile with dynamic shapes
+inputs = torch_tensorrt.Input(min_shape=[1, 3, 224, 224],
+                              opt_shape=[4, 3, 224, 224],
+                              max_shape=[8, 3, 224, 224],
+                              dtype=torch.float32)
+trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+
    
+
    
+
    
+
    Under the hood
    
+
    There are two phases of compilation when we use torch_tensorrt.compile API with ir=dynamo (default).
    
+
      
+
    • aten_tracer.trace (which uses torch.export to trace the graph with the given inputs)
      • 
+
    
+
    In the tracing phase, we use torch.export along with the constraints. In the case of
+dynamic shaped inputs, the range can be provided to the tracing via constraints. Please
+refer to this docstring
+for detailed information on how to set constraints. In short, we create new inputs for
+torch.export tracing and provide constraints on the min and max values(provided by the user), a particular dimension can take.
+Please take a look at aten_tracer.py file to understand how this works under the hood.
    
+
      
+
    • dynamo.compile (which compiles a torch.fx.GraphModule object using TensorRT)
      • 
+
    
+
    In the conversion to TensorRT, we use the user provided dynamic shape inputs.
+We perform shape analysis using dummy inputs (across min, opt and max shapes) and store the
+intermediate output shapes which can be used in case the graph has a mix of Pytorch
+and TensorRT submodules.
    
+
    
+
    
+
    Custom Constraints
    
+
    Given an input x = torch_tensorrt.Input(min_shape, opt_shape, max_shape, dtype),
+Torch-TensorRT automatically sets the constraints during torch.export tracing as follows
    
+
    for dim in constraint_dims:
+    if min_shape[dim] > 1:
+        constraints.append(min_shape[dim] <= dynamic_dim(trace_input, dim))
+    if max_shape[dim] > 1:
+        constraints.append(dynamic_dim(trace_input, dim) <= max_shape[dim])
+
    
+
    
+
    Sometimes, we might need to set additional constraints and Torchdynamo errors out if we don’t specify them.
+For example, in the case of BERT model compilation, there are two inputs and a constraint has to be set involving the sequence length size of these two inputs.
    
+
    constraints.append(dynamic_dim(trace_inputs[0], 0) == dynamic_dim(trace_inputs[1], 0))
+
    
+
    
+
    If you have to provide any custom constraints to your model, the overall workflow for model compilation using ir=dynamo would involve a few steps.
    
+
    import torch
+import torch_tensorrt
+from torch_tensorrt.dynamo.lowering import apply_lowering_passes, get_decompositions
+# Assume the model has two inputs
+model = MyModel()
+torch_input_1 = torch.randn((1, 14), dtype=torch.int32).cuda()
+torch_input_2 = torch.randn((1, 14), dtype=torch.int32).cuda()
+
+dynamic_inputs = [torch_tensorrt.Input(min_shape=[1, 14],
+                    opt_shape=[4, 14],
+                    max_shape=[8, 14],
+                    dtype=torch.int32),
+                  torch_tensorrt.Input(min_shape=[1, 14],
+                    opt_shape=[4, 14],
+                    max_shape=[8, 14],
+                    dtype=torch.int32)]
+
+# Export the model with additional constraints
+constraints = []
+# The following constraints are automatically added by Torch-TensorRT in the
+# general case when you call torch_tensorrt.compile directly on MyModel()
+constraints.append(dynamic_dim(torch_input_1, 0) < 8)
+constraints.append(dynamic_dim(torch_input_2, 0) < 8)
+# This is an additional constraint as instructed by Torchdynamo
+constraints.append(dynamic_dim(torch_input_1, 0) == dynamic_dim(torch_input_2, 0))
+with unittest.mock.patch(
+    "torch._export.DECOMP_TABLE", get_decompositions(experimental_decompositions)
+):
+    graph_module = export(
+        model, (torch_input_1, torch_input_2), constraints=constraints
+    ).module()
+
+# Use the dynamo.compile API
+trt_mod = torch_tensorrt.dynamo.compile(graph_module, inputs=dynamic_inputs, **compile_spec)
+
    
+
    
+
    
+
    
+
    Limitations
    
+
    If there are operations in the graph that use the dynamic dimension of the input, Pytorch
+introduces torch.ops.aten.sym_size.int ops in the graph. Currently, we cannot handle these operators and
+the compilation results in undefined behavior. We plan to add support for these operators and implement
+robust support for shape tensors in the next release. Here is an example of the limitation described above
    
+
    import torch
+import torch_tensorrt
+
+class MyModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.avgpool = torch.nn.AdaptiveAvgPool2d((1, 1))
+
+    def forward(self, x):
+        x = self.avgpool(x)
+        out = torch.flatten(x, 1)
+        return out
+
+model = MyModel().eval().cuda()
+# Compile with dynamic shapes
+inputs = torch_tensorrt.Input(min_shape=(1, 512, 1, 1),
+                     opt_shape=(4, 512, 1, 1),
+                     max_shape=(8, 512, 1, 1),
+                     dtype=torch.float32)
+trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+
    
+
    
+
    The traced graph of MyModule() looks as follows
    
+
    Post export graph: graph():
+%arg0_1 : [num_users=2] = placeholder[target=arg0_1]
+%mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [-1, -2], True), kwargs = {})
+%sym_size : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%arg0_1, 0), kwargs = {})
+%view : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%mean, [%sym_size, 512]), kwargs = {})
+return (view,)
+
    
+
    
+
    Here the %sym_size node captures the dynamic batch and uses it in the aten.view layer. This requires shape tensors support
+which would be a part of our next release.
    
+
    
+
    
+
    Workaround (BERT static compilation example)
    
+
    In the case where you encounter the issues mentioned in the Limitations section,
+you can compile the model (static mode) with max input size that can be provided. In the cases of smaller inputs,
+we can pad them accordingly. This is only a workaround until we address the limitations.
    
+
    import torch
+import torch_tensorrt
+from transformers.utils.fx import symbolic_trace as transformers_trace
+
+model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
+
+# Input sequence length is 20.
+input1 = torch.randint(0, 5, (1, 20), dtype=torch.int32).to("cuda")
+input2 = torch.randint(0, 5, (1, 20), dtype=torch.int32).to("cuda")
+
+model = transformers_trace(model, input_names=["input_ids", "attention_mask"]).eval().cuda()
+trt_mod = torch_tensorrt.compile(model, inputs=[input1, input2], **compile_spec)
+model_outputs = model(input, input2)
+
+# If you have a sequence of length 14, pad 6 zero tokens and run inference
+# or recompile for sequence length of 14.
+input1 = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+input2 = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+trt_mod = torch_tensorrt.compile(model, inputs=[input1, input2], **compile_spec)
+model_outputs = model(input, input2)
+
    
+
    
+
    
+
    
+
    Dynamic shapes with ir=torch_compile
    
+
    torch_tensorrt.compile(model, inputs, ir="torch_compile") returns a torch.compile boxed function with the backend
+configured to Tensorrt. In the case of ir=torch_compile, users have to recompile for different input shapes.
+In the future, we plan to explore the option of compiling with dynamic shapes in the first execution of the model.
    
+
    import torch
+import torch_tensorrt
+
+model = MyModel().eval().cuda()
+inputs = torch.randn((1, 3, 224, 224), dtype=float32)
+trt_gm = torch_tensorrt.compile(model, ir="torch_compile", inputs)
+# Compilation happens when you call the model
+trt_gm(inputs)
+
+# Recompilation happens with modified batch size
+inputs_bs2 = torch.randn((2, 3, 224, 224), dtype=torch.float32)
+trt_gm = torch_tensorrt.compile(model, ir="torch_compile", inputs_bs2)
+
    
+
    
+
    
+
    
+
+
+             
    
+             
+            
    
+            
+
+          
    
+        
    
+
+        
    
+          
+        
    
+      
    
+    
    
+
+  
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+  
+
+     
+       
+         
+         
+         
+         
+         
+         
+         
+     
+
+  
+
+  
+  
+  
+  
+
+   
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
    Docs
    
+          
    Access comprehensive developer documentation for PyTorch
    
+          View Docs
+        
    
+
+        
    
+          
    Tutorials
    
+          
    Get in-depth tutorials for beginners and advanced developers
    
+          View Tutorials
+        
    
+
+        
    
+          
    Resources
    
+          
    Find development resources and get your questions answered
    
+          View Resources
+        
    
+      
    
+    
    
+  
    
+
+  
+
+  
    
+  
    
+    
    To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.
    
+    
+  
    
+
    
+
+  
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+  
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+  
    
+    
    
+      
    
+        
    
+          
+          
+        
    
+      
    
+    
    
+
+    
    
+      
    
+        
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+    
    
+  
    
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+
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diff --git a/docs/user_guide/getting_started_with_fx_path.html b/docs/user_guide/getting_started_with_fx_path.html
index 5ab7b8dae9..0fd21c0f5e 100644
--- a/docs/user_guide/getting_started_with_fx_path.html
+++ b/docs/user_guide/getting_started_with_fx_path.html
@@ -10,7 +10,7 @@
 
   
   
-  Torch-TensorRT (FX Frontend) User Guide — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Torch-TensorRT (FX Frontend) User Guide — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/user_guide/ptq.html b/docs/user_guide/ptq.html
index a70f149e28..e3bf2f87f1 100644
--- a/docs/user_guide/ptq.html
+++ b/docs/user_guide/ptq.html
@@ -10,7 +10,7 @@
 
   
   
-  Post Training Quantization (PTQ) — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Post Training Quantization (PTQ) — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -225,7 +225,7 @@
               
               
                 
    
-                  v2.2.0.dev0+b50290d
+                  v2.2.0.dev0+d375d10
                 
    
               
             
@@ -269,6 +269,8 @@
 
  • Torch-TensorRT (FX Frontend) User Guide
    • 
 
  • Post Training Quantization (PTQ)
    • 
 
  • Deploying Torch-TensorRT Programs
    • 
+
  • Saving models compiled with Torch-TensorRT
    • 
+
  • Dynamic shapes with Torch-TensorRT
    • 
 
  • Using Torch-TensorRT Directly From PyTorch
    • 
 
  • DLA
    • 
 
@@ -304,6 +306,7 @@
 
 
    Indices
    
diff --git a/docs/user_guide/runtime.html b/docs/user_guide/runtime.html
index 015a5d0b95..6edfbbbad7 100644
--- a/docs/user_guide/runtime.html
+++ b/docs/user_guide/runtime.html
@@ -10,7 +10,7 @@
 
   
   
-  Deploying Torch-TensorRT Programs — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Deploying Torch-TensorRT Programs — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -39,7 +39,7 @@
   
     
     
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+    
    
+      Table of Contents
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+  
      
+    
+      
    • 
+        
+          
+            Docs
+          
+         >
+      
      • 
+
+        
+      
    • Saving models compiled with Torch-TensorRT
      • 
+    
+    
+      
    • 
+        
+            
+            
+          
+        
+      
      • 
+    
+  
    
+
+  
+
    
+        
    
+
+        
    
+          Shortcuts
+        
    
+      
    
+
+      
    
+        
    
+
+        
+          
+          
+          
+          
+          
    
+          
+            
    
+             
    
+              
+  
    
+
    Saving models compiled with Torch-TensorRT
    
+
    Saving models compiled with Torch-TensorRT varies slightly with the ir that has been used for compilation.
    
+
      
+
    1. Dynamo IR
      2. 
+
    
+
    Starting with 2.1 release of Torch-TensorRT, we are switching the default compilation to be dynamo based.
+The output of ir=dynamo compilation is a torch.fx.GraphModule object. There are two ways to save these objects
    
+
    a) Converting to Torchscript
+torch.fx.GraphModule objects cannot be serialized directly. Hence we use torch.jit.trace to convert this into a ScriptModule object which can be saved to disk.
+The following code illustrates this approach.
    
+
    import torch
+import torch_tensorrt
+
+model = MyModel().eval().cuda()
+inputs = torch.randn((1, 3, 224, 224)).cuda()
+trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
+trt_script_model = torch.jit.trace(trt_gm, inputs)
+torch.jit.save(trt_script_model, "trt_model.ts")
+
+# Later, you can load it and run inference
+model = torch.jit.load("trt_model.ts").cuda()
+model(inputs)
+
    
+
    
+
    b) ExportedProgram
+torch.export.ExportedProgram is a new format introduced in Pytorch 2.1. After we compile a Pytorch module using Torch-TensorRT, the resultant
+torch.fx.GraphModule along with additional metadata can be used to create ExportedProgram which can be saved and loaded from disk.
    
+
    import torch
+import torch_tensorrt
+from torch_tensorrt.dynamo.export import transform, create_exported_program
+
+model = MyModel().eval().cuda()
+inputs = torch.randn((1, 3, 224, 224)).cuda()
+trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
+# Transform and create an exported program
+trt_gm = transform(trt_gm, inputs)
+trt_exp_program = create_exported_program(trt_gm, call_spec, trt_gm.state_dict())
+torch._export.save(trt_exp_program, "trt_model.ep")
+
+# Later, you can load it and run inference
+model = torch._export.load("trt_model.ep")
+model(inputs)
+
    
+
    
+
    torch_tensorrt.dynamo.export.transform inlines the submodules within a GraphModule to their corresponding nodes and stiches all the nodes together.
+This is needed as torch._export serialization cannot handle serializing and deserializing of submodules (call_module nodes).
    
+
    NOTE: This way of saving the models using ExportedProgram is experimental. Here is a known issue : https://github.com/pytorch/TensorRT/issues/2341
    
+
      
+
    1. Torchscript IR
      2. 
+
    
+
    
+
    In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
+This behavior stays the same in 2.X versions as well.
    
+
    import torch
+import torch_tensorrt
+
+model = MyModel().eval().cuda()
+inputs = torch.randn((1, 3, 224, 224)).cuda()
+trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+torch.jit.save(trt_ts, "trt_model.ts")
+
+# Later, you can load it and run inference
+model = torch.jit.load("trt_model.ts").cuda()
+model(inputs)
+
    
+
    
+
    
+
    
+
+
+             
    
+             
+            
    
+            
+
+          
    
+        
    
+
+        
    
+          
+        
    
+      
    
+    
    
+
+  
+
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+  
+
+     
+       
+         
+         
+         
+         
+         
+         
+         
+     
+
+  
+
+  
+  
+  
+  
+
+   
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
    Docs
    
+          
    Access comprehensive developer documentation for PyTorch
    
+          View Docs
+        
    
+
+        
    
+          
    Tutorials
    
+          
    Get in-depth tutorials for beginners and advanced developers
    
+          View Tutorials
+        
    
+
+        
    
+          
    Resources
    
+          
    Find development resources and get your questions answered
    
+          View Resources
+        
    
+      
    
+    
    
+  
    
+
+  
+
+  
    
+  
    
+    
    To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.
    
+    
+  
    
+
    
+
+  
+
+  
+
+  
    
+    
    
+      
    
+        
    
+          
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+        
    
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\ No newline at end of file
diff --git a/docs/user_guide/use_from_pytorch.html b/docs/user_guide/use_from_pytorch.html
index f28c094938..aba617c02f 100644
--- a/docs/user_guide/use_from_pytorch.html
+++ b/docs/user_guide/use_from_pytorch.html
@@ -10,7 +10,7 @@
 
   
   
-  Using Torch-TensorRT Directly From PyTorch — Torch-TensorRT v2.2.0.dev0+b50290d documentation
+  Using Torch-TensorRT Directly From PyTorch — Torch-TensorRT v2.2.0.dev0+d375d10 documentation
   
 
   
@@ -40,7 +40,7 @@
     
     
     
-    
+