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    Torch-TensorRT Getting Started - CitriNet

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    FP16 (half precision)

    What’s next

    -

    Now it’s time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.

    +

    Now it’s time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.

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    Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.

    +

    Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.

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    Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.

    +

    Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.

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    Torch-TensorRT Getting Started - ResNet 50

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    Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.

    +

    Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.

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    +

    Setting up the model

    In this section, we will: * Get sample data. * Download model from torch hub. * Build simple utility functions

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    Build simple utility functions 
    [7]:
    @@ -1292,12 +1287,13 @@

    Benchmarking with Torch-TRT (without dynamic shapes)

    With the baseline ready, we can proceed to the section working discussing dynamic shapes!

    +

    Working with Dynamic shapes in Torch TRT

    -

    Enabling “Dynamic Shaped” tensors to be used is essentially enabling the ability to defer defining the shape of tensors until run-time. Torch TensorRT simply leverages TensorRT’s Dynamic shape support. You can read more about TensorRT’s implementation in the TensorRT Documentation.

    -

    To make use of dynamic shapes, you need to provide three shapes: * min_shape: The minimum size of the tensor considered for optimizations. * opt_shape: The optimizations will be done in an effort to maximize performance for this shape. * max_shape: The maximum size of the tensor considered for optimizations.

    -

    Generally, users can expect the best performance within the specified ranges. Performance for other shapes maybe be lower for other shapes (depending on the model ops and GPU used)

    -

    In the following example, we will showcase varying batch sizes, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won’t be changing the sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used.

    +

    Enabling “Dynamic Shaped” tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT’s Dynamic shape support. You can read more about TensorRT’s implementation in the TensorRT Documentation.

    +

    To make use of dynamic shapes, you need to provide three shapes: * min_shape: The minimum size of the tensor considered for optimizations. * opt_shape: The optimizations will be done with an effort to maximize performance for this shape. * min_shape: The maximum size of the tensor considered for optimizations.

    +

    Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)

    +

    In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won’t be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used.

    [11]:
    @@ -1456,15 +1452,6 @@

    What’s Next? - - - - - - -

    - @@ -1474,7 +1461,7 @@

    What’s Next?

    - © Copyright 2021, NVIDIA Corporation. + © Copyright 2022, NVIDIA Corporation.

    @@ -1494,7 +1481,6 @@

    What’s Next?

    @@ -1201,15 +1213,6 @@

    What’s next - - - - - - - - @@ -1219,7 +1222,7 @@

    What’s next

    - © Copyright 2021, NVIDIA Corporation. + © Copyright 2022, NVIDIA Corporation.

    @@ -1351,19 +1354,43 @@

    Resources

    -     + +
    @@ -1402,10 +1429,6 @@

    Resources

    Mobile -
  • - PyTorch Hub -
    • -
  • Blog
    • @@ -1435,6 +1458,18 @@

    Resources

    torchvision +
  • + torcharrow +
    • + +
  • + TorchData +
    • + +
  • + TorchRec +
    • +
  • TorchServe
    • @@ -1452,26 +1487,39 @@

    Resources

    Resources -
    -

    aa77691fd7754170ac652a03d651d0b9

    +

    6c149d85c6314d4ea59e4ea603d757b7

    Object Detection with Torch-TensorRT (SSD)

    @@ -770,8 +782,8 @@

    Contents

    --++ @@ -779,8 +791,8 @@

    Single Shot MultiBox Detector model for object detection

    - + +

    _

    alt

    alt

    alt

    image1
    @@ -1316,15 +1328,6 @@

    References - - - - - - - - @@ -1334,7 +1337,7 @@

    References

    - © Copyright 2021, NVIDIA Corporation. + © Copyright 2022, NVIDIA Corporation.

    @@ -1468,19 +1471,43 @@

    Resources

    -     + +
    @@ -1519,10 +1546,6 @@

    Resources

    Mobile -
  • - PyTorch Hub -
    • -
  • Blog
    • @@ -1552,6 +1575,18 @@

    Resources

    torchvision +
  • + torcharrow +
    • + +
  • + TorchData +
    • + +
  • + TorchRec +
    • +
  • TorchServe
    • @@ -1569,26 +1604,39 @@

    Resources

    Resources -
    - + +
    @@ -1823,10 +1828,6 @@

    Resources

    Mobile -
  • - PyTorch Hub -
    • -
  • Blog
    • @@ -1856,6 +1857,18 @@

    Resources

    torchvision +
  • + torcharrow +
    • + +
  • + TorchData +
    • + +
  • + TorchRec +
    • +
  • TorchServe
    • @@ -1873,26 +1886,39 @@

    Resources

    Resources -
      -
    • - Developer Resources -
      • +
    • diff --git a/docs/_notebooks/vgg-qat.ipynb b/docs/_notebooks/vgg-qat.ipynb index 5888950378..cca771ad92 100644 --- a/docs/_notebooks/vgg-qat.ipynb +++ b/docs/_notebooks/vgg-qat.ipynb @@ -1,35 +1,10 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "a054a8df", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2020-2022 NVIDIA Corporation. All Rights Reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "# ==============================================================================" - ] - }, { "cell_type": "markdown", "id": "8033b2eb", "metadata": {}, "source": [ - "\n", - "\n", "# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT" ] }, @@ -60,7 +35,7 @@ "source": [ "\n", "## 1. Requirements\n", - "Please install the required dependencies and import these libraries accordingly" + "Please install the required dependencies and import these libraries accordingly" ] }, { @@ -1028,7 +1003,7 @@ "%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)\n", "%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)\n", "```\n", - "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to quantization op converters in Torch-TensorRT." + "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to quantization op converters in Torch-TensorRT." ] }, { @@ -1193,8 +1168,8 @@ "## 9. References\n", "* Very Deep Convolution Networks for large scale Image Recognition\n", "* Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT\n", - "* QAT workflow for VGG16\n", - "* Deploying VGG QAT model in C++ using Torch-TensorRT\n", + "* QAT workflow for VGG16\n", + "* Deploying VGG QAT model in C++ using Torch-TensorRT\n", "* Pytorch-quantization toolkit from NVIDIA\n", "* Pytorch quantization toolkit userguide\n", "* Quantization basics" @@ -1217,7 +1192,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt b/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt index b0fa677071..caa9ad6226 100644 --- a/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt +++ b/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt @@ -11,4 +11,3 @@ Function Documentation .. doxygenfunction:: torch_tensorrt::ptq::make_int8_calibrator(DataLoader, const std::string&, bool) - :project: Torch-TensorRT \ No newline at end of file diff --git a/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt b/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt index 9312e7b01f..477c2655ae 100644 --- a/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt +++ b/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt @@ -11,4 +11,3 @@ Function Documentation .. doxygenfunction:: torch_tensorrt::ptq::make_int8_cache_calibrator(const std::string&) - :project: Torch-TensorRT \ No newline at end of file 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 906dd8e29d..e4cc75e0b0 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 @@ -17,9 +17,9 @@ Program Listing for File torch_tensorrt.h * This library is licensed under the BSD-style license found in the * LICENSE file in the root directory of this source tree. */ - + #pragma once - + #include #include #include @@ -27,9 +27,9 @@ Program Listing for File torch_tensorrt.h #include #include #include "torch/custom_class.h" - + #include "torch_tensorrt/macros.h" - + // Just include the .h? #ifndef DOXYGEN_SHOULD_SKIP_THIS namespace torch { @@ -38,19 +38,19 @@ Program Listing for File torch_tensorrt.h struct Module; } // namespace jit } // namespace torch - + namespace c10 { enum class DeviceType : int8_t; enum class ScalarType : int8_t; template class ArrayRef; } // namespace c10 - + namespace nvinfer1 { class IInt8Calibrator; } #endif // DOXYGEN_SHOULD_SKIP_THIS - + namespace torch_tensorrt { class DataType { public: @@ -62,7 +62,7 @@ Program Listing for File torch_tensorrt.h kBool, kUnknown }; - + DataType() = default; constexpr DataType(Value t) : value(t) {} TORCHTRT_API DataType(c10::ScalarType t); @@ -82,12 +82,12 @@ Program Listing for File torch_tensorrt.h constexpr bool operator!=(DataType::Value other) const { return value != other; } - + private: friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const DataType& dtype); Value value; }; - + struct Device { class DeviceType { public: @@ -95,7 +95,7 @@ Program Listing for File torch_tensorrt.h kGPU, kDLA, }; - + DeviceType() = default; constexpr DeviceType(Value t) : value(t) {} DeviceType(c10::DeviceType t); @@ -109,34 +109,34 @@ Program Listing for File torch_tensorrt.h constexpr bool operator!=(DeviceType other) const { return value != other.value; } - + private: Value value; }; - + DeviceType device_type; - + /* * Target gpu id */ int64_t gpu_id; - + /* * When using DLA core on NVIDIA AGX platforms gpu_id should be set as Xavier device */ int64_t dla_core; - + bool allow_gpu_fallback; - + Device() : device_type(DeviceType::kGPU), gpu_id(0), dla_core(0), allow_gpu_fallback(false) {} }; - + enum class EngineCapability : int8_t { kSTANDARD, kSAFETY, kDLA_STANDALONE, }; - + class TensorFormat { public: enum Value : int8_t { @@ -144,7 +144,7 @@ Program Listing for File torch_tensorrt.h kChannelsLast, kUnknown, }; - + TensorFormat() = default; constexpr TensorFormat(Value t) : value(t) {} TORCHTRT_API TensorFormat(at::MemoryFormat t); @@ -164,12 +164,12 @@ Program Listing for File torch_tensorrt.h constexpr bool operator!=(TensorFormat::Value other) const { return value != other; } - + private: friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const TensorFormat& format); Value value; }; - + struct Input : torch::CustomClassHolder { std::vector min_shape; std::vector opt_shape; @@ -177,118 +177,118 @@ Program Listing for File torch_tensorrt.h std::vector shape; DataType dtype; TensorFormat format; - + Input() {} TORCHTRT_API Input(std::vector shape, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input(std::vector shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input(c10::ArrayRef shape, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input(c10::ArrayRef shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input( std::vector min_shape, std::vector opt_shape, std::vector max_shape, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input( std::vector min_shape, std::vector opt_shape, std::vector max_shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input( c10::ArrayRef min_shape, c10::ArrayRef opt_shape, c10::ArrayRef max_shape, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input( c10::ArrayRef min_shape, c10::ArrayRef opt_shape, c10::ArrayRef max_shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous); - + TORCHTRT_API Input(at::Tensor tensor); - + private: friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const Input& input); bool input_is_dynamic; }; - + struct GraphInputs { torch::jit::IValue input_signature; // nested Input, full input spec std::vector inputs; // flatten input spec }; - + TORCHTRT_API std::string get_build_info(); - + TORCHTRT_API void dump_build_info(); - + TORCHTRT_API void set_device(const int gpu_id); - + namespace torchscript { struct CompileSpec { TORCHTRT_API CompileSpec(std::vector> fixed_sizes); - + TORCHTRT_API CompileSpec(std::vector> fixed_sizes); - + TORCHTRT_API CompileSpec(std::vector inputs); - + TORCHTRT_API CompileSpec(torch::jit::IValue input_signature); // Defaults should reflect TensorRT defaults for BuilderConfig - + GraphInputs graph_inputs; std::set enabled_precisions = {DataType::kFloat}; - + bool disable_tf32 = false; - + bool sparse_weights = false; - + bool refit = false; - + bool debug = false; - + bool truncate_long_and_double = false; - + Device device; - + EngineCapability capability = EngineCapability::kSTANDARD; - + uint64_t num_avg_timing_iters = 1; - + uint64_t workspace_size = 0; - + uint64_t dla_sram_size = 1048576; - + uint64_t dla_local_dram_size = 1073741824; - + uint64_t dla_global_dram_size = 536870912; - + nvinfer1::IInt8Calibrator* ptq_calibrator = nullptr; - + bool require_full_compilation = false; - + uint64_t min_block_size = 3; - + std::vector torch_executed_ops; - + std::vector torch_executed_modules; }; - + TORCHTRT_API bool check_method_operator_support(const torch::jit::Module& module, std::string method_name); - + TORCHTRT_API torch::jit::Module compile(const torch::jit::Module& module, CompileSpec info); - + TORCHTRT_API std::string convert_method_to_trt_engine( const torch::jit::Module& module, std::string method_name, CompileSpec info); - + TORCHTRT_API torch::jit::Module embed_engine_in_new_module( const std::string& engine, Device device, diff --git a/docs/_sources/_notebooks/CitriNet-example.ipynb.txt b/docs/_sources/_notebooks/CitriNet-example.ipynb.txt index b9d615d5f1..0573af0176 100644 --- a/docs/_sources/_notebooks/CitriNet-example.ipynb.txt +++ b/docs/_sources/_notebooks/CitriNet-example.ipynb.txt @@ -26,7 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "# Torch-TensorRT Getting Started - CitriNet" ] @@ -929,7 +929,7 @@ "In this notebook, we have walked through the complete process of optimizing the Citrinet model with Torch-TensorRT. On an A100 GPU, with Torch-TensorRT, we observe a speedup of ~**2.4X** with FP32, and ~**2.9X** with FP16 at batchsize of 128.\n", "\n", "### What's next\n", - "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n" + "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n" ] }, { @@ -956,7 +956,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt b/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt index 518254c3ab..31a3dad874 100644 --- a/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt +++ b/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt @@ -26,7 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "# Torch-TensorRT Getting Started - EfficientNet-B0" ] @@ -658,7 +658,7 @@ "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.35x** with FP32, and **3.13x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n", "\n", "### What's next\n", - "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n" + "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n" ] }, { @@ -685,7 +685,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt b/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt index 81034d8e38..9b027b473e 100644 --- a/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt +++ b/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt @@ -28,8 +28,7 @@ "id": "d0a97ac5", "metadata": {}, "source": [ - "\n", - "\n", + "\n", "\n", "# Masked Language Modeling (MLM) with Hugging Face BERT Transformer" ] @@ -679,7 +678,7 @@ "Torch-TensorRT (FP16): 3.15x\n", "\n", "### What's next\n", - "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT." + "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT." ] }, { @@ -707,7 +706,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/_notebooks/Resnet50-example.ipynb.txt b/docs/_sources/_notebooks/Resnet50-example.ipynb.txt index f75a2b0e64..f020662c73 100644 --- a/docs/_sources/_notebooks/Resnet50-example.ipynb.txt +++ b/docs/_sources/_notebooks/Resnet50-example.ipynb.txt @@ -897,7 +897,7 @@ "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.84x** with FP32, and **5.2x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n", "\n", "### What's next\n", - "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n" + "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n" ] } ], diff --git a/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt b/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt index 5738f13521..a0ceaab576 100644 --- a/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt +++ b/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Copyright 2022 NVIDIA Corporation. All Rights Reserved.\n", + "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", @@ -28,8 +28,6 @@ "id": "18ee9d62", "metadata": {}, "source": [ - "\n", - "\n", "# Torch-TensorRT - Using Dynamic Shapes" ] }, @@ -38,14 +36,14 @@ "id": "73703695", "metadata": {}, "source": [ - "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into a module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extension and compiles modules that integrate into the JIT runtime seamlessly. After compilation, using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile-time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n", + "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n", "\n", - "We highly encourage users to run this notebook using our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). It comes packaged with a host of NVIDIA libraries and optimizations to widely used third-party libraries. In addition, this container is tested and updated on a monthly cadence!\n", + "We highly encorage users to use our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!\n", "\n", "This notebook has the following sections:\n", "1. [TL;DR Explanation](#1)\n", - "2. [Setting up the model](#2)\n", - "3. [Working with Dynamic shapes in Torch TRT](#3)" + "1. [Setting up the model](#2)\n", + "1. [Working with Dynamic shapes in Torch TRT](#3)" ] }, { @@ -53,7 +51,6 @@ "id": "1603028d", "metadata": {}, "source": [ - "\n", "---\n", "## TL;DR Explanation\n", "\n", @@ -190,7 +187,6 @@ "id": "0ed1b4f7", "metadata": {}, "source": [ - "\n", "---\n", "## Setting up the model\n", "\n", @@ -637,7 +633,7 @@ "id": "21402d53", "metadata": {}, "source": [ - "Let's test our util functions on the model we have set up, starting with simple predictions." + "Let's test our util functions on the model we have set up, starting with simple predictions" ] }, { @@ -822,22 +818,21 @@ "id": "bd5746da", "metadata": {}, "source": [ - "\n", "---\n", "## Working with Dynamic shapes in Torch TRT\n", - " \n", - "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until run-time. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n", - " \n", + "\n", + "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n", + "\n", "#### How can you use this feature?\n", - " \n", + "\n", "To make use of dynamic shapes, you need to provide three shapes:\n", "* `min_shape`: The minimum size of the tensor considered for optimizations.\n", - "* `opt_shape`: The optimizations will be done in an effort to maximize performance for this shape.\n", - "* `max_shape`: The maximum size of the tensor considered for optimizations.\n", - " \n", - "Generally, users can expect the best performance within the specified ranges. Performance for other shapes maybe be lower for other shapes (depending on the model ops and GPU used)\n", - " \n", - "In the following example, we will showcase varying batch sizes, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing the sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used." + "* `opt_shape`: The optimizations will be done with an effort to maximize performance for this shape.\n", + "* `min_shape`: The maximum size of the tensor considered for optimizations.\n", + "\n", + "Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)\n", + "\n", + "In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used." ] }, { diff --git a/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt b/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt index 144d47813b..2db954946d 100644 --- a/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt +++ b/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt @@ -6,7 +6,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Copyright 2019-22 NVIDIA Corporation. All Rights Reserved.\n", + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", @@ -26,8 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", + "\n", "\n", "# Torch-TensorRT Getting Started - LeNet" ] @@ -691,7 +690,7 @@ "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.\n", "\n", "### What's next\n", - "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n" + "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n" ] } ], @@ -711,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt b/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt index b7ae8dc2e8..f2a0bd8063 100644 --- a/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt +++ b/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt @@ -26,7 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "# Object Detection with Torch-TensorRT (SSD)" ] @@ -935,7 +935,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/_notebooks/vgg-qat.ipynb.txt b/docs/_sources/_notebooks/vgg-qat.ipynb.txt index 5888950378..cca771ad92 100644 --- a/docs/_sources/_notebooks/vgg-qat.ipynb.txt +++ b/docs/_sources/_notebooks/vgg-qat.ipynb.txt @@ -1,35 +1,10 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "a054a8df", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2020-2022 NVIDIA Corporation. All Rights Reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "# ==============================================================================" - ] - }, { "cell_type": "markdown", "id": "8033b2eb", "metadata": {}, "source": [ - "\n", - "\n", "# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT" ] }, @@ -60,7 +35,7 @@ "source": [ "\n", "## 1. Requirements\n", - "Please install the required dependencies and import these libraries accordingly" + "Please install the required dependencies and import these libraries accordingly" ] }, { @@ -1028,7 +1003,7 @@ "%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)\n", "%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)\n", "```\n", - "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to quantization op converters in Torch-TensorRT." + "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to quantization op converters in Torch-TensorRT." ] }, { @@ -1193,8 +1168,8 @@ "## 9. References\n", "* Very Deep Convolution Networks for large scale Image Recognition\n", "* Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT\n", - "* QAT workflow for VGG16\n", - "* Deploying VGG QAT model in C++ using Torch-TensorRT\n", + "* QAT workflow for VGG16\n", + "* Deploying VGG QAT model in C++ using Torch-TensorRT\n", "* Pytorch-quantization toolkit from NVIDIA\n", "* Pytorch quantization toolkit userguide\n", "* Quantization basics" @@ -1217,7 +1192,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/_sources/tutorials/installation.rst.txt b/docs/_sources/tutorials/installation.rst.txt index 4c4905db96..949fa2ddc9 100644 --- a/docs/_sources/tutorials/installation.rst.txt +++ b/docs/_sources/tutorials/installation.rst.txt @@ -25,14 +25,14 @@ You can install the python package using .. code-block:: sh - pip3 install torch-tensorrt -f https://github.com/pytorch/TensorRT/releases + pip3 install torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/releases .. _bin-dist: C++ Binary Distribution ------------------------ -Precompiled tarballs for releases are provided here: https://github.com/pytorch/TensorRT/releases +Precompiled tarballs for releases are provided here: https://github.com/NVIDIA/Torch-TensorRT/releases .. _compile-from-source: diff --git a/docs/_sources/tutorials/torchtrtc.rst.txt b/docs/_sources/tutorials/torchtrtc.rst.txt index dc9e4b6768..b841c891e5 100644 --- a/docs/_sources/tutorials/torchtrtc.rst.txt +++ b/docs/_sources/tutorials/torchtrtc.rst.txt @@ -92,12 +92,10 @@ to standard TorchScript. Load with ``torch.jit.load()`` and run like you would r used to select kernels --workspace-size=[workspace_size] Maximum size of workspace given to TensorRT - --atol=[atol] Absolute tolerance threshold for acceptable - numerical deviation from standard torchscript - output (default 1e-8) - --rtol=[rtol] Relative tolerance threshold for acceptable - numerical deviation from standard torchscript - output (default 1e-5) + -t[threshold], + --threshold=[threshold] Maximum acceptable numerical deviation + from standard torchscript output + (default 2e-5) --no-threshold-check Skip checking threshold compliance --truncate-long-double, --truncate, --truncate-64bit Truncate weights that are provided in diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 7e8ecffa6a..7a5ef527a0 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: 'master', + VERSION: 'v1.3.0', LANGUAGE: 'None', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/cli/torchtrtc.html b/docs/cli/torchtrtc.html index 2e1cbc3b20..146ddb5d3b 100644 --- a/docs/cli/torchtrtc.html +++ b/docs/cli/torchtrtc.html @@ -9,22 +9,22 @@ - - torchtrtc — Torch-TensorRT master documentation - - - - - + torchtrtc — Torch-TensorRT v1.3.0 documentation + + + + + + + + + + + - - - - - @@ -36,11 +36,11 @@ - + - - + + @@ -196,9 +196,9 @@ - - + +
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      torchtrtc

      torchtrtc is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a @@ -508,25 +508,25 @@

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        Conversion Phase

        Once the graph has be simplified to a form thats easy to convert, we then set up a conversion context @@ -424,25 +424,25 @@

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        Lowering Phase

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        The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT. @@ -587,25 +587,25 @@

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            Using Torch-TensorRT in C++

            If you haven’t already, acquire a tarball of the library by following the instructions in Installation

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            Table of Contents @@ -207,21 +207,21 @@
            @@ -300,7 +300,7 @@
            - + @@ -319,30 +319,30 @@
            - +
            @@ -354,13 +354,13 @@
            - - + +
            - +
            - + @@ -409,12 +409,12 @@

            Changelog @@ -423,9 +423,9 @@

            Changelog - - + + @@ -436,7 +436,7 @@

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          • YouTube
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        Resources

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            Table of Contents @@ -207,21 +207,21 @@
            @@ -300,7 +300,7 @@
            - + @@ -319,30 +319,30 @@
            - +
            @@ -354,13 +354,13 @@
            - - + +
            - +
            - + @@ -512,12 +512,12 @@

            How the Table of Contents builds @@ -526,9 +526,9 @@

            How the Table of Contents builds - - + + @@ -539,7 +539,7 @@

            How the Table of Contents buildsTwitter

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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 e85eb99f37..981c08adab 100644 --- a/docs/src/pytorch-sphinx-theme/docs/index.html +++ b/docs/src/pytorch-sphinx-theme/docs/index.html @@ -9,22 +9,22 @@ - - <no title> — Torch-TensorRT master documentation - - - - - + <no title> — Torch-TensorRT v1.3.0 documentation + + + + + + + + + + + - - - - - @@ -34,11 +34,11 @@ - + - - + + @@ -194,9 +194,9 @@ - - + +
            Table of Contents @@ -207,21 +207,21 @@
            @@ -300,7 +300,7 @@
            - + @@ -319,30 +319,30 @@
            - +
            @@ -354,13 +354,13 @@
            - - + +
            - +
            - +

            Theme Documentation

              @@ -459,16 +459,16 @@
            - +
            @@ -499,12 +499,12 @@
            - - - + + + @@ -513,9 +513,9 @@ - - + + @@ -526,7 +526,7 @@ jQuery(function () { SphinxRtdTheme.Navigation.enable(true); }); - + @@ -590,7 +590,7 @@

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            Table of Contents @@ -207,21 +207,21 @@
            @@ -300,7 +300,7 @@
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            @@ -354,13 +354,13 @@
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            Table of Contents @@ -209,21 +209,21 @@
            @@ -302,7 +302,7 @@
            - + @@ -321,30 +321,30 @@
              - +
            • - + Docs - + >
              • - +
            • Creating a TorchScript Module
              • - - + +
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              • - +
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            @@ -356,13 +356,13 @@
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            - +
            - +

            Creating a TorchScript Module

            TorchScript is a way to create serializable and optimizable models from PyTorch code. @@ -484,25 +484,25 @@

            Saving TorchScript Module to Disk - + - - + + - +

            - - + +
            - +

            @@ -510,11 +510,11 @@

            Saving TorchScript Module to DiskSphinx using a theme provided by Read the Docs.

            - + @@ -536,12 +536,12 @@

            Saving TorchScript Module to Disk @@ -550,9 +550,9 @@

            Saving TorchScript Module to Disk - - + + @@ -563,7 +563,7 @@

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          • diff --git a/docs/tutorials/getting_started_with_cpp_api.html b/docs/tutorials/getting_started_with_cpp_api.html index 44105b3d23..f8a8d7ef68 100644 --- a/docs/tutorials/getting_started_with_cpp_api.html +++ b/docs/tutorials/getting_started_with_cpp_api.html @@ -1,510 +1,933 @@ - - - - - - - - - - - - Getting Started with C++ — Torch-TensorRT master documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            -
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            - Shortcuts -
            +
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            Getting Started with C++

            -

            If you haven’t already, acquire a tarball of the library by following the instructions in Installation

            -
            -

            Using Torch-TensorRT in C++

            -

            Torch-TensorRT C++ API accepts TorchScript modules (generated either from torch.jit.script or torch.jit.trace) as an input and returns +

            +
            +
            + + +

            + Getting Started with C++ + + ¶ + +

            +

            + If you haven’t already, acquire a tarball of the library by following the instructions in + + + Installation + + +

            +

            + Using Torch-TensorRT in C++ + + ¶ + +

            +

            + Torch-TensorRT C++ API accepts TorchScript modules (generated either from + + + torch.jit.script + + + or + + + torch.jit.trace + + + ) as an input and returns a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand. -Please refer to Creating TorchScript modules in Python section to generate torchscript graphs.

            -
            -

            [Torch-TensorRT Quickstart] Compiling TorchScript Modules with torchtrtc

            -

            An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through -torchtrtc, which supports almost all features of the compiler from the command line including post training quantization +Please refer to + + Creating TorchScript modules in Python + + section to generate torchscript graphs. +

            + + +

            + [Torch-TensorRT Quickstart] Compiling TorchScript Modules with + + + torchtrtc + + + + ¶ + +

            +

            + An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through + + + torchtrtc + + + , which supports almost all features of the compiler from the command line including post training quantization (given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating -precision and input size. This new TorchScript file can be loaded into Python (note: you need to import torch_tensorrt before loading -these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).

            -
            ❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts "(1,1,32,32)"
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to
+        
+         
+          import
+         
+         
+          torch_tensorrt
+         
+        
+        before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
+       
            
+       
            
+        
            
+         
            ❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts "(1,1,32,32)"
 
 ❯ python3
 Python 3.6.9 (default, Apr 18 2020, 01:56:04)
 [GCC 8.4.0] on linux
-Type "help", "copyright", "credits" or "license" for more information.
+Type "help", "copyright", "credits" or "license" for more information.
 >>> import torch
 >>> import torch_tensorrt
 >>> ts_model = torch.jit.load(“trt_lenet_scripted.ts”)
 >>> ts_model(torch.randn((1,1,32,32)).to(“cuda”).half())
-
            
-
            
-
            You can learn more about torchtrtc usage here: torchtrtc
            
-
            -
            -

            Working with TorchScript in C++

            -

            If we are developing an application to deploy with C++, we can save either our traced or scripted module using torch.jit.save -which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.

            -
            torch_script_module.save("lenet.jit.pt")
-
            -
            -

            From here we can now load our TorchScript module in C++

            -
            #include <torch/script.h> // One-stop header.
-
-#include <iostream>
-#include <memory>
-
-int main(int argc, const char* argv[]) {
-    torch::jit::Module module;
-    try {
-        // Deserialize the ScriptModule from a file using torch::jit::load().
-        module = torch::jit::load("<PATH TO SAVED TS MOD>");
-    }
-    catch (const c10::Error& e) {
-        std::cerr << "error loading the model\n";
-        return -1;
-    }
-
-    std::cout << "ok\n";
-
            -
            -

            You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and -have access to the same powerful tensor library that backs PyTorch. (For more information: https://pytorch.org/cppdocs/). -For instance we can do inference with our LeNet module like this:

            -
            mod.eval();
-torch::Tensor in = torch::randn({1, 1, 32, 32});
-auto out = mod.forward(in);
-
            -
            -

            and to run on the GPU:

            -
            mod.eval();
-mod.to(torch::kCUDA);
-torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
-auto out = mod.forward(in);
-
            -
            -

            As you can see it is pretty similar to the Python API. When you call the forward method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.

            -
            -
            -

            Compiling with Torch-TensorRT in C++

            -

            We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work -since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.

            -

            With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.

            -
            #include "torch/script.h"
-#include "torch_tensorrt/torch_tensorrt.h"
-...
-
-    mod.to(at::kCUDA);
-    mod.eval();
-
-    auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA});
-    auto trt_mod = torch_tensorrt::CompileGraph(mod, std::vector<torch_tensorrt::CompileSpec::InputRange>{{in.sizes()}});
-    auto out = trt_mod.forward({in});
-
            -
            -

            Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).

            -

            We can also set settings like operating precision to run in FP16.

            -
            #include "torch/script.h"
-#include "torch_tensorrt/torch_tensorrt.h"
-...
-
-    mod.to(at::kCUDA);
-    mod.eval();
-
-    auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
-    auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
-    torch_tensorrt::CompileSpec info(input_sizes);
-    info.enable_precisions.insert(torch::kHALF);
-    auto trt_mod = torch_tensorrt::CompileGraph(mod, info);
-    auto out = trt_mod.forward({in});
-
            -
            -

            And now we are running the module in FP16 precision. You can then save the module to load later.

            -
            trt_mod.save("<PATH TO SAVED TRT/TS MOD>")
-
            -
            -

            Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against libtorchtrt.so

            -
            #include "torch/script.h"
-#include "torch_tensorrt/torch_tensorrt.h"
-
-int main(int argc, const char* argv[]) {
-    torch::jit::Module module;
-    try {
-        // Deserialize the ScriptModule from a file using torch::jit::load().
-        module = torch::jit::load("<PATH TO SAVED TRT/TS MOD>");
-    }
-    catch (const c10::Error& e) {
-        std::cerr << "error loading the model\n";
-        return -1;
-    }
-
-    torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
-    auto out = mod.forward(in);
-
-    std::cout << "ok\n";
-}
-
            -
            -

            If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the ConvertGraphToTRTEngine API.

            -
            #include "torch/script.h"
-#include "torch_tensorrt/torch_tensorrt.h"
-...
-
-    mod.to(at::kCUDA);
-    mod.eval();
-
-    auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
-    auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
-    torch_tensorrt::CompileSpec info(input_sizes);
-    info.enabled_precisions.insert(torch::kHALF);
-    auto trt_mod = torch_tensorrt::ConvertGraphToTRTEngine(mod, "forward", info);
-    std::ofstream out("/tmp/engine_converted_from_jit.trt");
-    out << engine;
-    out.close();
-
            -
            -
            -
            -

            Under The Hood

            -

            When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:

            -
            graph(%input.2 : Tensor):
+
            +
            +
            +

            + You can learn more about + + + torchtrtc + + + usage here: + + + torchtrtc + + +

            + + +

            + Working with TorchScript in C++ + + ¶ + +

            +

            + If we are developing an application to deploy with C++, we can save either our traced or scripted module using + + + torch.jit.save + + + which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends. +

            +
            +
            + 
            torch_script_module.save("lenet.jit.pt")
+
            +
            +
            +

            + From here we can now load our TorchScript module in C++ +

            +
            +
            + 
            #include <torch/script.h> // One-stop header.
+
+#include <iostream>
+#include <memory>
+
+int main(int argc, const char* argv[]) {
+    torch::jit::Module module;
+    try {
+        // Deserialize the ScriptModule from a file using torch::jit::load().
+        module = torch::jit::load("<PATH TO SAVED TS MOD>");
+    }
+    catch (const c10::Error& e) {
+        std::cerr << "error loading the model\n";
+        return -1;
+    }
+
+    std::cout << "ok\n";
+
            +
            +
            +

            + You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and +have access to the same powerful tensor library that backs PyTorch. (For more information: + + https://pytorch.org/cppdocs/ + + ). +For instance we can do inference with our LeNet module like this: +

            +
            +
            + 
            mod.eval();
+torch::Tensor in = torch::randn({1, 1, 32, 32});
+auto out = mod.forward(in);
+
            +
            +
            +

            + and to run on the GPU: +

            +
            +
            + 
            mod.eval();
+mod.to(torch::kCUDA);
+torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+auto out = mod.forward(in);
+
            +
            +
            +

            + As you can see it is pretty similar to the Python API. When you call the + + + forward + + + method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code. +

            + + +

            + Compiling with Torch-TensorRT in C++ + + ¶ + +

            +

            + We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work +since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it. +

            +

            + With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings. +

            +
            +
            + 
            #include "torch/script.h"
+#include "torch_tensorrt/torch_tensorrt.h"
+...
+
+    mod.to(at::kCUDA);
+    mod.eval();
+
+    auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA});
+    auto trt_mod = torch_tensorrt::CompileGraph(mod, std::vector<torch_tensorrt::CompileSpec::InputRange>{{in.sizes()}});
+    auto out = trt_mod.forward({in});
+
            +
            +
            +

            + Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime). +

            +

            + We can also set settings like operating precision to run in FP16. +

            +
            +
            + 
            #include "torch/script.h"
+#include "torch_tensorrt/torch_tensorrt.h"
+...
+
+    mod.to(at::kCUDA);
+    mod.eval();
+
+    auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+    auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+    torch_tensorrt::CompileSpec info(input_sizes);
+    info.enable_precisions.insert(torch::kHALF);
+    auto trt_mod = torch_tensorrt::CompileGraph(mod, info);
+    auto out = trt_mod.forward({in});
+
            +
            +
            +

            + And now we are running the module in FP16 precision. You can then save the module to load later. +

            +
            +
            + 
            trt_mod.save("<PATH TO SAVED TRT/TS MOD>")
+
            +
            +
            +

            + Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against + + + libtorchtrt.so + + +

            +
            +
            + 
            #include "torch/script.h"
+#include "torch_tensorrt/torch_tensorrt.h"
+
+int main(int argc, const char* argv[]) {
+    torch::jit::Module module;
+    try {
+        // Deserialize the ScriptModule from a file using torch::jit::load().
+        module = torch::jit::load("<PATH TO SAVED TRT/TS MOD>");
+    }
+    catch (const c10::Error& e) {
+        std::cerr << "error loading the model\n";
+        return -1;
+    }
+
+    torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+    auto out = mod.forward(in);
+
+    std::cout << "ok\n";
+}
+
            +
            +
            +

            + If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the + + + ConvertGraphToTRTEngine + + + API. +

            +
            +
            + 
            #include "torch/script.h"
+#include "torch_tensorrt/torch_tensorrt.h"
+...
+
+    mod.to(at::kCUDA);
+    mod.eval();
+
+    auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+    auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+    torch_tensorrt::CompileSpec info(input_sizes);
+    info.enabled_precisions.insert(torch::kHALF);
+    auto trt_mod = torch_tensorrt::ConvertGraphToTRTEngine(mod, "forward", info);
+    std::ofstream out("/tmp/engine_converted_from_jit.trt");
+    out << engine;
+    out.close();
+
            +
            +
            + + +

            + Under The Hood + + ¶ + +

            +

            + When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this: +

            +
            +
            + 
            graph(%input.2 : Tensor):
     %2 : Float(84, 10) = prim::Constant[value=<Tensor>]()
     %3 : Float(120, 84) = prim::Constant[value=<Tensor>]()
     %4 : Float(576, 120) = prim::Constant[value=<Tensor>]()
@@ -543,395 +966,315 @@ 
            Using Torch-TensorRT in C++
            graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
-    %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
+by the Torch-TensorRT runtime.
+       
            
+       
            
+        Here is the graph that you get back after compilation is complete:
+       
            
+       
            
+        
            
+         
            graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+    %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
     %3 : Tensor[] = prim::ListConstruct(%input_0)
     %4 : Tensor[] = trt::execute_engine(%3, %1)
     %5 : Tensor = prim::ListUnpack(%4)
     return (%5)
-
            
-
            
-
            You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
            
-
            -
            -

            Working with Unsupported Operators

            -

            Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques -shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.

            -
            -

            You can check support without going through the full compilation pipleine using the torch_tensorrt::CheckMethodOperatorSupport(const torch::jit::Module& module, std::string method_name) api -to see what operators are not supported. torchtrtc automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.

            -
            -
            -

            Registering Custom Converters

            -

            Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT. -Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a aten::relu(%input0.4) instruction will trigger + +

            +
            +

            + You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user. +

            + + +

            + Working with Unsupported Operators + + ¶ + +

            +

            + Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques +shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops. +

            +
            +
            +

            + You can check support without going through the full compilation pipleine using the + + + torch_tensorrt::CheckMethodOperatorSupport(const + + + torch::jit::Module& + + + module, + + + std::string + + + method_name) + + + api +to see what operators are not supported. + + + torchtrtc + + + automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported. +

            +
            +
            + + +

            + Registering Custom Converters + + ¶ + +

            +

            + Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT. +Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a + + + aten::relu(%input0.4) + + + instruction will trigger the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT -to support your module.

            -

            Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.

            -

            For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (aten::flatten) you may see this error:

            -
            terminate called after throwing an instance of 'torch_tensorrt::Error'
+to support your module.
+       
            
+       
            
+        Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.
+       
            
+       
            
+        For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (
+        
+         
+          aten::flatten
+         
+        
+        ) you may see this error:
+       
            
+       
            
+        
            
+         
            terminate called after throwing an instance of 'torch_tensorrt::Error'
 what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
 Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
 Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)
 Converter for aten::flatten requested, but no such converter was found.
 If you need a converter for this operator, you can try implementing one yourself
 or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
-
            
-
            
-
            We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including torch_tensorrt/core/conversion/converters/converters.h.
-We start by creating an instance of the self-registering class torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns() which will register converters
-in the global converter registry, associating a function schema like aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor) with a lambda that
-will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
-Arguments are passed as a vector of inspectable unions of TensorRT ITensors and Torch IValues in the order arguments are listed in the schema.
            
-
            Below is a implementation of a aten::flatten converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
-for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.
            
-
            #include "torch/script.h"
-#include "torch_tensorrt/torch_tensorrt.h"
-#include "torch_tensorrt/core/conversion/converters/converters.h"
-
-static auto flatten_converter = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
-    .pattern({
-        "aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)",
-        [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
-           const torch::jit::Node* n,
-           torch_tensorrt::core::conversion::converters::args& args) -> bool {
-            auto in = args[0].ITensor();
-            auto start_dim = args[1].unwrapToInt();
-            auto end_dim = args[2].unwrapToInt();
-            auto in_shape = torch_tensorrt::core::util::toVec(in->getDimensions());
-            auto out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes();
-
-            auto shuffle = ctx->net->addShuffle(*in);
-            shuffle->setReshapeDimensions(torch_tensorrt::core::util::toDims(out_shape));
-            shuffle->setName(torch_tensorrt::core::util::node_info(n).c_str());
-
-            auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
-            return true;
-        }
-    });
-
-int main() {
-    ...
-
            
-
            
-
            To use this converter in Python, it is recommended to use PyTorch’s C++ / CUDA Extention
-template to wrap your library of converters into a .so that you can load with ctypes.CDLL() in your Python application.
            
-
            You can find more information on all the details of writing converters in the contributors documentation (Writing Converters).
-If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.
            
-
            - - - - - - - -
            - - -
            -
            - -
            - +
            - -
            - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            -
            -
            -
            -

            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 +
            +

            + We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including + + + torch_tensorrt/core/conversion/converters/converters.h + + + . +We start by creating an instance of the self-registering class + + + torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns() + + + which will register converters +in the global converter registry, associating a function schema like + + + aten::flatten.using_ints(Tensor + + + self, + + + int + + + start_dim=0, + + + int + + + end_dim=-1) + + + -> + + + (Tensor) + + + with a lambda that +will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network. +Arguments are passed as a vector of inspectable unions of TensorRT + + + ITensors + + + and Torch + + + IValues + + + in the order arguments are listed in the schema. +

            +

            + Below is a implementation of a + + + aten::flatten + + + converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So +for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter. +

            +
            +
            + 
            #include "torch/script.h"
+#include "torch_tensorrt/torch_tensorrt.h"
+#include "torch_tensorrt/core/conversion/converters/converters.h"
+
+static auto flatten_converter = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
+    .pattern({
+        "aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)",
+        [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
+           const torch::jit::Node* n,
+           torch_tensorrt::core::conversion::converters::args& args) -> bool {
+            auto in = args[0].ITensor();
+            auto start_dim = args[1].unwrapToInt();
+            auto end_dim = args[2].unwrapToInt();
+            auto in_shape = torch_tensorrt::core::util::toVec(in->getDimensions());
+            auto out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes();
+
+            auto shuffle = ctx->net->addShuffle(*in);
+            shuffle->setReshapeDimensions(torch_tensorrt::core::util::toDims(out_shape));
+            shuffle->setName(torch_tensorrt::core::util::node_info(n).c_str());
+
+            auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
+            return true;
+        }
+    });
+
+int main() {
+    ...
+
            -
            +
            +

            + To use this converter in Python, it is recommended to use PyTorch’s + + C++ / CUDA Extention + + template to wrap your library of converters into a + + + .so + + + that you can load with + + + ctypes.CDLL() + + + in your Python application. +

            +

            + You can find more information on all the details of writing converters in the contributors documentation ( + + + Writing Converters + + + ). +If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well. +

            + +
            +
        - -
        ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

        PyTorch Source

        Recommended C++ Compilation Command

        Recommended Python Compilation Command

        PyTorch whl file from PyTorch.org

        bazel build //:libtorchtrt -c opt –config pre_cxx11_abi

        python3 setup.py bdist_wheel

        libtorch-shared-with-deps-*.zip from PyTorch.org

        bazel build //:libtorchtrt -c opt –config pre_cxx11_abi

        python3 setup.py bdist_wheel

        libtorch-cxx11-abi-shared-with-deps-*.zip from PyTorch.org

        bazel build //:libtorchtrt -c opt

        python3 setup.py bdist_wheel –use-cxx11-abi

        PyTorch preinstalled in an NGC container

        bazel build //:libtorchtrt -c opt

        python3 setup.py bdist_wheel –use-cxx11-abi

        PyTorch from the NVIDIA Forums for Jetson

        bazel build //:libtorchtrt -c opt

        python3 setup.py bdist_wheel –jetpack-version 4.6 –use-cxx11-abi

        PyTorch built from Source

        bazel build //:libtorchtrt -c opt

        python3 setup.py bdist_wheel –use-cxx11-abi

        -
        -

        NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information

        -
        -

        You then have two compilation options:

        -
        -

    -
    -

    Building using cuDNN & TensorRT tarball distributions

    -
    -

    This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues

    -

    Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your $LD_LIBRARY_PATH

    -
    -
    -
    You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
    -
    -
    -

    Place these files in a directory (the directories third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu] exist for this purpose)

    -

    Then compile referencing the directory with the tarballs

    -
    -

    If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in WORKSPACE

    -
    -
    -

    Release Build

    -
    bazel build //:libtorchtrt -c opt --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
-
    -
    -

    A tarball with the include files and library can then be found in bazel-bin

    -
    -
    -

    Debug Build

    -

    To build with debug symbols use the following command

    -
    bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
-
    -
    -

    A tarball with the include files and library can then be found in bazel-bin

    -
    -
    -

    Pre CXX11 ABI Build

    -

    To build using the pre-CXX11 ABI use the pre_cxx11_abi config

    -
    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt] --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
-
    -
    -

    A tarball with the include files and library can then be found in bazel-bin

    -
    -
    -
    -

    Building using locally installed cuDNN & TensorRT

    -
    -

    If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)

    -
    -

    Install TensorRT, CUDA and cuDNN on the system before starting to compile.

    -

    In WORKSPACE comment out:

    -
    # Downloaded distributions to use with --distdir
+recommended commands:
+       
    
+       
+        +         +         +         +        
+        
+         
+          
+          
+          
+         
+        
+        
+         
+          
+          
+          
+         
+         
+          
+          
+          
+         
+         
+          
+          
+          
+         
+         
+          
+          
+          
+         
+         
+          
+          
+          
+         
+         
+          
+          
+          
+         
+        
+       
    




    
+           
    
+            PyTorch Source
+           
    
+              		
+           
    
+            Recommended C++ Compilation Command
+           
    
+              		
+           
    
+            Recommended Python Compilation Command
+           
    
+          
    
+           
    
+            PyTorch whl file from PyTorch.org
+           
    
+              		
+           
    
+            bazel build //:libtorchtrt -c opt –config pre_cxx11_abi
+           
    
+              		
+           
    
+            python3 setup.py bdist_wheel
+           
    
+          
    
+           
    
+            libtorch-shared-with-deps-
+            
+             
+              *
+             
+            
+            .zip from PyTorch.org
+           
    
+              		
+           
    
+            bazel build //:libtorchtrt -c opt –config pre_cxx11_abi
+           
    
+              		
+           
    
+            python3 setup.py bdist_wheel
+           
    
+          
    
+           
    
+            libtorch-cxx11-abi-shared-with-deps-
+            
+             
+              *
+             
+            
+            .zip from PyTorch.org
+           
    
+              		
+           
    
+            bazel build //:libtorchtrt -c opt
+           
    
+              		
+           
    
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           
    
+          
    
+           
    
+            PyTorch preinstalled in an NGC container
+           
    
+              		
+           
    
+            bazel build //:libtorchtrt -c opt
+           
    
+              		
+           
    
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           
    
+          
    
+           
    
+            PyTorch from the NVIDIA Forums for Jetson
+           
    
+              		
+           
    
+            bazel build //:libtorchtrt -c opt
+           
    
+              		
+           
    
+            python3 setup.py bdist_wheel –jetpack-version 4.6 –use-cxx11-abi
+           
    
+          
    
+           
    
+            PyTorch built from Source
+           
    
+              		
+           
    
+            bazel build //:libtorchtrt -c opt
+           
    
+              		
+           
    
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           
    
+          
    
+       
    
+        
    
+         
    
+          NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information
+         
    
+        
    
+       
    
+       
    
+        You then have two compilation options:
+       
    
+       
+       
+       
    
+        
+         Building using cuDNN & TensorRT tarball distributions
+        
+        
+         ¶
+        
+       
    
+       
    
+        
    
+         
    
+          This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues
+         
    
+         
    
+          Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your
+          
+           
+            $LD_LIBRARY_PATH
+           
+          
+         
    
+        
    
+       
    
+       
    
+        
    
+         You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
+        
    
+        
    
+         
+        
    
+       
    
+       
    
+        Place these files in a directory (the directories
+        
+         
+          third_party/distdir/[x86_64-linux-gnu
+         
+         
+          |
+         
+         
+          aarch64-linux-gnu]
+         
+        
+        exist for this purpose)
+       
    
+       
    
+        Then compile referencing the directory with the tarballs
+       
    
+       
    
+        
    
+         
    
+          If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in
+          
+           
+            WORKSPACE
+           
+          
+         
    
+        
    
+       
    
+       
    
+        Release Build
+        
+         ¶
+        
+       
    
+       
    
+        
    
+         
    bazel build //:libtorchtrt -c opt --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
    
+        
    
+       
    
+       
    
+        A tarball with the include files and library can then be found in
+        
+         
+          bazel-bin
+         
+        
+       
    
+       
+       
+       
    
+        Debug Build
+        
+         ¶
+        
+       
    
+       
    
+        To build with debug symbols use the following command
+       
    
+       
    
+        
    
+         
    bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
    
+        
    
+       
    
+       
    
+        A tarball with the include files and library can then be found in
+        
+         
+          bazel-bin
+         
+        
+       
    
+       
    
+        Pre CXX11 ABI Build
+        
+         ¶
+        
+       
    
+       
    
+        To build using the pre-CXX11 ABI use the
+        
+         
+          pre_cxx11_abi
+         
+        
+        config
+       
    
+       
    
+        
    
+         
    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt] --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
    
+        
    
+       
    
+       
    
+        A tarball with the include files and library can then be found in
+        
+         
+          bazel-bin
+         
+        
+       
    
+       
+       
+       
    
+        
+         Building using locally installed cuDNN & TensorRT
+        
+        
+         ¶
+        
+       
    
+       
    
+        
    
+         
    
+          If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)
+         
    
+        
    
+       
    
+       
    
+        Install TensorRT, CUDA and cuDNN on the system before starting to compile.
+       
    
+       
    
+        In WORKSPACE comment out:
+       
    
+       
    
+        
    
+         
    # Downloaded distributions to use with --distdir
 http_archive(
-    name = "cudnn",
-    urls = ["<URL>",],
+    name = "cudnn",
+    urls = ["<URL>",],
 
-    build_file = "@//third_party/cudnn/archive:BUILD",
-    sha256 = "<TAR SHA256>",
-    strip_prefix = "cuda"
+    build_file = "@//third_party/cudnn/archive:BUILD",
+    sha256 = "<TAR SHA256>",
+    strip_prefix = "cuda"
 )
 
 http_archive(
-    name = "tensorrt",
-    urls = ["<URL>",],
+    name = "tensorrt",
+    urls = ["<URL>",],
 
-    build_file = "@//third_party/tensorrt/archive:BUILD",
-    sha256 = "<TAR SHA256>",
-    strip_prefix = "TensorRT-<VERSION>"
+    build_file = "@//third_party/tensorrt/archive:BUILD",
+    sha256 = "<TAR SHA256>",
+    strip_prefix = "TensorRT-<VERSION>"
 )
-
    
-
    
-
    and uncomment
    
-
    # Locally installed dependencies
+
    
+        
    
+       
    
+       
    
+        and uncomment
+       
    
+       
    
+        
    
+         
    # Locally installed dependencies
 new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
+    name = "cudnn",
+    path = "/usr/",
+    build_file = "@//third_party/cudnn/local:BUILD"
 )
 
 new_local_repository(
-name = "tensorrt",
-path = "/usr/",
-build_file = "@//third_party/tensorrt/local:BUILD"
+name = "tensorrt",
+path = "/usr/",
+build_file = "@//third_party/tensorrt/local:BUILD"
 )
-
    
-
    
-
    
-
    Release Build
    
-
    Compile using:
    
-
    bazel build //:libtorchtrt -c opt
-
    
-
    
-
    A tarball with the include files and library can then be found in bazel-bin
    
-
    
-
    
-
    Debug Build
    
-
    To build with debug symbols use the following command
    
-
    bazel build //:libtorchtrt -c dbg
-
    
-
    
-
    A tarball with the include files and library can then be found in bazel-bin
    
-
    
-
    
-
    Pre CXX11 ABI Build
    
-
    To build using the pre-CXX11 ABI use the pre_cxx11_abi config
    
-
    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt]
-
    
-
    
-
    
-
    -
    -

    Building the Python package

    -

    Begin by installing ninja

    -

    You can build the Python package using setup.py (this will also build the correct version of libtorchtrt.so)

    -
    python3 setup.py [install/bdist_wheel]
-
    -
    -
    -

    Debug Build

    -
    python3 setup.py develop [--user]
-
    -
    -

    This also compiles a debug build of libtorchtrt.so

    -
    -
    -
    -

    Building Natively on aarch64 (Jetson)

    -
    -

    Prerequisites

    -

    Install or compile a build of PyTorch/LibTorch for aarch64

    -

    NVIDIA hosts builds the latest release branch for Jetson here:

    -
    -
    -
    -
    -

    Enviorment Setup

    -

    To build natively on aarch64-linux-gnu platform, configure the WORKSPACE with local available dependencies.

    -
      -

    1. Disable the rules with http_archive for x86_64 by commenting the following rules:

      2. -
    -
    #http_archive(
-#    name = "libtorch",
-#    build_file = "@//third_party/libtorch:BUILD",
-#    strip_prefix = "libtorch",
-#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.5.1.zip"],
-#    sha256 = "cf0691493d05062fe3239cf76773bae4c5124f4b039050dbdd291c652af3ab2a"
+
    +
    +
    +

    + Release Build + + ¶ + +

    +

    + Compile using: +

    +
    +
    + 
    bazel build //:libtorchtrt -c opt
+
    +
    +
    +

    + A tarball with the include files and library can then be found in + + + bazel-bin + + +

    + + +

    + Debug Build + + ¶ + +

    +

    + To build with debug symbols use the following command +

    +
    +
    + 
    bazel build //:libtorchtrt -c dbg
+
    +
    +
    +

    + A tarball with the include files and library can then be found in + + + bazel-bin + + +

    +

    + Pre CXX11 ABI Build + + ¶ + +

    +

    + To build using the pre-CXX11 ABI use the + + + pre_cxx11_abi + + + config +

    +
    +
    + 
    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt]
+
    +
    +
    +

    + + Building the Python package + + + ¶ + +

    +

    + Begin by installing + + + ninja + + +

    +

    + You can build the Python package using + + + setup.py + + + (this will also build the correct version of + + + libtorchtrt.so + + + ) +

    +
    +
    + 
    python3 setup.py [install/bdist_wheel]
+
    +
    +
    +

    + Debug Build + + ¶ + +

    +
    +
    + 
    python3 setup.py develop [--user]
+
    +
    +
    +

    + This also compiles a debug build of + + + libtorchtrt.so + + +

    +

    + + Building Natively on aarch64 (Jetson) + + + ¶ + +

    +

    + Prerequisites + + ¶ + +

    +

    + Install or compile a build of PyTorch/LibTorch for aarch64 +

    +

    + NVIDIA hosts builds the latest release branch for Jetson here: +

    +
    + +
    +

    + Enviorment Setup + + ¶ + +

    +

    + To build natively on aarch64-linux-gnu platform, configure the + + + WORKSPACE + + + with local available dependencies. +

    +
      +
    1. +

      + Disable the rules with + + + http_archive + + + for x86_64 by commenting the following rules: +

      +
      2. +
    +
    +
    + 
    #http_archive(
+#    name = "libtorch",
+#    build_file = "@//third_party/libtorch:BUILD",
+#    strip_prefix = "libtorch",
+#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.5.1.zip"],
+#    sha256 = "cf0691493d05062fe3239cf76773bae4c5124f4b039050dbdd291c652af3ab2a"
 #)
 
 #http_archive(
-#    name = "libtorch_pre_cxx11_abi",
-#    build_file = "@//third_party/libtorch:BUILD",
-#    strip_prefix = "libtorch",
-#    sha256 = "818977576572eadaf62c80434a25afe44dbaa32ebda3a0919e389dcbe74f8656",
-#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-shared-with-deps-1.5.1.zip"],
+#    name = "libtorch_pre_cxx11_abi",
+#    build_file = "@//third_party/libtorch:BUILD",
+#    strip_prefix = "libtorch",
+#    sha256 = "818977576572eadaf62c80434a25afe44dbaa32ebda3a0919e389dcbe74f8656",
+#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-shared-with-deps-1.5.1.zip"],
 #)
 
 # Download these tarballs manually from the NVIDIA website
 # Either place them in the distdir directory in third_party and use the --distdir flag
-# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
+# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
 
 #http_archive(
-#    name = "cudnn",
-#    urls = ["https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.1.13/10.2_20200626/cudnn-10.2-linux-x64-v8.0.1.13.tgz"],
-#    build_file = "@//third_party/cudnn/archive:BUILD",
-#    sha256 = "0c106ec84f199a0fbcf1199010166986da732f9b0907768c9ac5ea5b120772db",
-#    strip_prefix = "cuda"
+#    name = "cudnn",
+#    urls = ["https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.1.13/10.2_20200626/cudnn-10.2-linux-x64-v8.0.1.13.tgz"],
+#    build_file = "@//third_party/cudnn/archive:BUILD",
+#    sha256 = "0c106ec84f199a0fbcf1199010166986da732f9b0907768c9ac5ea5b120772db",
+#    strip_prefix = "cuda"
 #)
 
 #http_archive(
-#    name = "tensorrt",
-#    urls = ["https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.1/tars/TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz"],
-#    build_file = "@//third_party/tensorrt/archive:BUILD",
-#    sha256 = "9205bed204e2ae7aafd2e01cce0f21309e281e18d5bfd7172ef8541771539d41",
-#    strip_prefix = "TensorRT-7.1.3.4"
+#    name = "tensorrt",
+#    urls = ["https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.1/tars/TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz"],
+#    build_file = "@//third_party/tensorrt/archive:BUILD",
+#    sha256 = "9205bed204e2ae7aafd2e01cce0f21309e281e18d5bfd7172ef8541771539d41",
+#    strip_prefix = "TensorRT-7.1.3.4"
 #)
 
 NOTE: You may also need to configure the CUDA version to 10.2 by setting the path for the cuda new_local_repository
-
    -
    -
      -

    1. Configure the correct paths to directory roots containing local dependencies in the new_local_repository rules:

      -
      -

      NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package. -In the case that you installed with sudo pip install this will be /usr/local/lib/python3.6/dist-packages/torch. -In the case you installed with pip install --user this will be $HOME/.local/lib/python3.6/site-packages/torch.

      -
      -
      2. -
    -

    In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike + + + +

      +
    1. +

      + Configure the correct paths to directory roots containing local dependencies in the + + + new_local_repository + + + rules: +

      +
      +
      +

      + NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package. +In the case that you installed with + + + sudo + + + pip + + + install + + + this will be + + + /usr/local/lib/python3.6/dist-packages/torch + + + . +In the case you installed with + + + pip + + + install + + + --user + + + this will be + + + $HOME/.local/lib/python3.6/site-packages/torch + + + . +

      +
      +
      +
      2. +
    +

    + In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to -use that library, set the paths to the same path but when you compile make sure to add the flag --config=pre_cxx11_abi

    -
    new_local_repository(
-    name = "libtorch",
-    path = "/usr/local/lib/python3.6/dist-packages/torch",
-    build_file = "third_party/libtorch/BUILD"
+use that library, set the paths to the same path but when you compile make sure to add the flag
+        
+         
+          --config=pre_cxx11_abi
+         
+        
+       
    
+       
    
+        
    
+         
    new_local_repository(
+    name = "libtorch",
+    path = "/usr/local/lib/python3.6/dist-packages/torch",
+    build_file = "third_party/libtorch/BUILD"
 )
 
 new_local_repository(
-    name = "libtorch_pre_cxx11_abi",
-    path = "/usr/local/lib/python3.6/dist-packages/torch",
-    build_file = "third_party/libtorch/BUILD"
+    name = "libtorch_pre_cxx11_abi",
+    path = "/usr/local/lib/python3.6/dist-packages/torch",
+    build_file = "third_party/libtorch/BUILD"
 )
 
 new_local_repository(
-    name = "cudnn",
-    path = "/usr/",
-    build_file = "@//third_party/cudnn/local:BUILD"
+    name = "cudnn",
+    path = "/usr/",
+    build_file = "@//third_party/cudnn/local:BUILD"
 )
 
 new_local_repository(
-    name = "tensorrt",
-    path = "/usr/",
-    build_file = "@//third_party/tensorrt/local:BUILD"
+    name = "tensorrt",
+    path = "/usr/",
+    build_file = "@//third_party/tensorrt/local:BUILD"
 )
-
    
-
    
-
    -
    -

    Compile C++ Library and Compiler CLI

    -
    -

    NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version

    -
    --platforms //toolchains:jetpack_4.x
-
    -
    -
    -

    Compile Torch-TensorRT library using bazel command:

    -
    bazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6
-
    -
    -
    -
    -

    Compile Python API

    -
    -

    NOTE: Due to shifting dependencies locations between Jetpack 4.5 and Jetpack 4.6 there is now a flag for setup.py which sets the jetpack version (default: 4.6)

    -
    -

    Compile the Python API using the following command from the //py directory:

    -
    python3 setup.py install --use-cxx11-abi
-
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    If you have a build of PyTorch that uses Pre-CXX11 ABI drop the --use-cxx11-abi flag

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    If you are building for Jetpack 4.5 add the --jetpack-version 4.5 flag

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    + Compile C++ Library and Compiler CLI + + ¶ + +

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    + NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version +

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    + Compile Torch-TensorRT library using bazel command: +

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    bazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6
+
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    + Compile the Python API using the following command from the + + + //py + + + directory: +

    +
    +
    + 
    python3 setup.py install --use-cxx11-abi
+
    -
    +
    +

    + If you have a build of PyTorch that uses Pre-CXX11 ABI drop the + + + --use-cxx11-abi + + + flag +

    +

    + If you are building for Jetpack 4.5 add the + + + --jetpack-version + + + 4.5 + + + flag +

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    -
    - - - - - - + - + \ No newline at end of file diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html index 4d742ee181..acb3cca32b 100644 --- a/docs/tutorials/notebooks.html +++ b/docs/tutorials/notebooks.html @@ -9,22 +9,22 @@ - - Example notebooks — Torch-TensorRT master documentation - - - - - + Example notebooks — Torch-TensorRT v1.3.0 documentation + + + + + + + + + + + - - - - - @@ -36,11 +36,11 @@ - + - - + + @@ -196,9 +196,9 @@ - - + +
    Table of Contents @@ -209,21 +209,21 @@
    @@ -302,7 +302,7 @@
    - + @@ -321,30 +321,30 @@
      - +
    • - + Docs - + >
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    • Example notebooks
      • - - + +
    • - - + + - - + +
      • - +
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    @@ -356,13 +356,13 @@
    - - + +
    - +
    - +

    Example notebooks

    There exists a number of notebooks which cover specific using specific features and models @@ -504,25 +504,25 @@

    Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT - +

    @@ -571,12 +571,12 @@

    Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT

    - - - + + + @@ -585,9 +585,9 @@

    Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT - - + + @@ -598,7 +598,7 @@

    Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT + @@ -662,7 +662,7 @@

    Resources

  • Twitter
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      • @@ -787,7 +787,7 @@

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      • diff --git a/docs/tutorials/ptq.html b/docs/tutorials/ptq.html index fe9f4a6fa4..b82e875b55 100644 --- a/docs/tutorials/ptq.html +++ b/docs/tutorials/ptq.html @@ -9,22 +9,22 @@ - - Post Training Quantization (PTQ) — Torch-TensorRT master documentation - - - - - + Post Training Quantization (PTQ) — Torch-TensorRT v1.3.0 documentation + + + + + + + + + + + - - - - - @@ -36,11 +36,11 @@ - + - - + + @@ -196,9 +196,9 @@ - - + +
        Table of Contents @@ -209,21 +209,21 @@
        @@ -302,7 +302,7 @@
        - + @@ -321,30 +321,30 @@
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        • - + Docs - + >
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        • Post Training Quantization (PTQ)
          • - - + +
        • - - + + - - + +
          • - +
        - +
        @@ -356,13 +356,13 @@
        - - + +
        - +
        - +

        Post Training Quantization (PTQ)

        Post Training Quantization (PTQ) is a technique to reduce the required computational resources for inference @@ -540,25 +540,25 @@

        Citations - + - - + + - +

        - - + +
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        @@ -566,11 +566,11 @@

        CitationsSphinx using a theme provided by Read the Docs.

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    Citations - - + + @@ -624,7 +624,7 @@

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        Table of Contents @@ -209,21 +209,21 @@
        @@ -302,7 +302,7 @@
        - + @@ -321,30 +321,30 @@
          - +
        • - + Docs - + >
          • - +
        • Deploying Torch-TensorRT Programs
          • - - + +
        • - - + + - - + +
          • - +
        - +
        @@ -356,13 +356,13 @@
        - - + +
        - +
        - +

        Deploying Torch-TensorRT Programs

        After compiling and saving Torch-TensorRT programs there is no longer a strict dependency on the full @@ -400,25 +400,25 @@

        Plugin Library - + - - + + - +

        - - + +
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        @@ -426,11 +426,11 @@

        Plugin LibrarySphinx using a theme provided by Read the Docs.

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        Plugin Library @@ -469,9 +469,9 @@

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    torchtrtc

    -

    torchtrtc is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a +

    +
    +
    + + +

    + torchtrtc + + ¶ + +

    +

    + + + torchtrtc + + + is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of a deployment pipeline. All basic features of the compiler are supported including post training quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can output two formats, either a TorchScript program with the TensorRT engine embedded or -the TensorRT engine itself as a PLAN file.

    -

    All that is required to run the program after compilation is for C++ linking against libtorchtrt.so -or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical -to standard TorchScript. Load with torch.jit.load() and run like you would run any other module.

    -
    torchtrtc [input_file_path] [output_file_path]
+the TensorRT engine itself as a PLAN file.
+       
    
+       
    
+        All that is required to run the program after compilation is for C++ linking against
+        
+         
+          libtorchtrt.so
+         
+        
+        or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with
+        
+         
+          torch.jit.load()
+         
+        
+        and run like you would run any other module.
+       
    
+       
    
+        
    
+         
    torchtrtc [input_file_path] [output_file_path]
   [input_specs...] {OPTIONS}
 
   torchtrtc is a compiler for TorchScript, it will compile and optimize
@@ -436,12 +593,10 @@
                                       used to select kernels
     --workspace-size=[workspace_size] Maximum size of workspace given to
                                       TensorRT
-    --atol=[atol]                     Absolute tolerance threshold for acceptable
-                                      numerical deviation from standard torchscript
-                                      output (default 1e-8)
-    --rtol=[rtol]                     Relative tolerance threshold for acceptable
-                                      numerical deviation from standard torchscript
-                                      output  (default 1e-5)
+    -t[threshold],
+    --threshold=[threshold]           Maximum acceptable numerical deviation
+                                      from standard torchscript output
+                                      (default 2e-5)
     --no-threshold-check              Skip checking threshold compliance
     --truncate-long-double,
     --truncate, --truncate-64bit      Truncate weights that are provided in
@@ -457,310 +612,89 @@
     input_specs...                    Specs for inputs to engine, can either
                                       be a single size or a range defined by
                                       Min, Optimal, Max sizes, e.g.
-                                      "(N,..,C,H,W)"
-                                      "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
+                                      "(N,..,C,H,W)"
+                                      "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
                                       Data Type and format can be specified by
-                                      adding an "@" followed by dtype and "%"
+                                      adding an "@" followed by dtype and "%"
                                       followed by format to the end of the
-                                      shape spec. e.g. "(3, 3, 32,
-                                      32)@f16%NHWC"
-    "--" can be used to terminate flag options and force all following
+                                      shape spec. e.g. "(3, 3, 32,
+                                      32)@f16%NHWC"
+    "--" can be used to terminate flag options and force all following
     arguments to be treated as positional options
-
    
-
    
-
    e.g.
    
-
    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
-
    
-
    
-
    - - - - -
    - - -
    + - -
    -
    -
    - - -
    -
    +
    +

    + e.g. +

    +
    +
    + 
    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
+
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