From 263e441c75d6bdced22068f3df9e806338629f5c Mon Sep 17 00:00:00 2001
From: Wei <wwei6@fb.com>
Date: Sun, 26 Jun 2022 00:42:21 -0700
Subject: [PATCH 01/10] Create getting_started_with_fx_path.rst

---
 .../getting_started_with_fx_path.rst          | 304 ++++++++++++++++++
 1 file changed, 304 insertions(+)
 create mode 100644 docsrc/tutorials/getting_started_with_fx_path.rst

diff --git a/docsrc/tutorials/getting_started_with_fx_path.rst b/docsrc/tutorials/getting_started_with_fx_path.rst
new file mode 100644
index 0000000000..9f15616e28
--- /dev/null
+++ b/docsrc/tutorials/getting_started_with_fx_path.rst
@@ -0,0 +1,304 @@
+.. user_guide:
+Torch-TensorRT (FX Path) User Guide 
+========================
+Torch-TensorRT (FX Path) is a tool that can convert a PyTorch model through torch.FX to an TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine developed by Nvidia which composed of various kinds of optimization including kernel fusion, graph optimization, low precision, etc..
+This tool is developed in Python environment providing most usability to researchers and engineers. There are a few stages that a user want to use this tool and we will introduce them here. 
+ 
+Installation
+------------
+* Method 1. Follow the instrucions for Torch-TensorRT
+* Method 2. To install FX path only (Python path) and avoid the C++ build for torchscript path
+
+.. code-block:: shell
+
+    $ conda create --name python_env python=3.8
+    $ conda activate python_env
+
+    # Recommend to install PyTorch 1.12 and later
+    $ conda install pytorch torchvision torchtext cudatoolkit=11.3 -c pytorch-nightly
+
+    # Install TensorRT python package
+    $ pip3 install nvidia-pyindex
+    $ pip3 install nvidia-tensorrt==8.2.4.2
+    $ git clone https://github.com/pytorch/TensorRT.git
+    $ cd TensorRT/py && python setup.py install --fx-only && cd ..
+
+    $ pyton -c "import torch_tensorrt.fx"
+    # Test an example by 
+    $ python py/torch_tensorrt/fx/example/lower_example.py
+ 
+
+Converting a PyTorch Model to TensorRT Engine
+---------------------------------------------
+We will go through an example to illustrate the major steps that FX path uses to 
+
+* **Step 1: Trace the model with acc_tracer**
+Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
+
+.. code-block:: shell
+
+   import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+
+   # Build the model which needs to be a PyTorch nn.Module. 
+   my_pytorch_model = build_model()
+
+   # Prepare inputs to the model. Inputs have to be a List of Tensors
+   inputs = [Tensor, Tensor, ...]
+
+   # Trace the model with acc_tracer.
+   acc_mod = acc_tracer.trace(my_pytorch_model, inputs)
+
+*Common Errors:*
+  
+symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in `FX guide <https://pytorch.org/docs/stable/fx.html#dynamic-control-flow>`_.
+
+* **Step 2: Build TensorRT engine**
+There are `two different modes <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch>`_ for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set ``explicit_batch_dimension=False`` in ``lower_to_trt()``. We do not recommend to use it since it will lack of support in future TensorRT versions. 
+
+Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable ``dynamic_batch`` in ``lower_to_trt()`` if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).
+
+For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range. 
+
+.. code-block:: shell
+
+    import deeplearning.trt.fx2trt.converter.converters
+    from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
+
+    # InputTensorSpec is a dataclass we use to store input information.
+    # There're two ways we can build input_specs.
+    # Option 1, build it manually.
+    input_specs = [
+      InputTensorSpec(shape=(1, 2, 3), dtype=torch.float32),
+      InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+    # Option 2, build it using sample_inputs where user provide a sample
+    inputs = [
+    torch.rand((1,2,3), dtype=torch.float32),
+    torch.rand((1,4,5), dtype=torch.float32),
+    ]
+    input_specs = InputTensorSpec.from_tensors(inputs)
+
+    # IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.
+    input_specs = [
+        InputTensorSpec(
+            shape=(-1, 2, 3), 
+            dtype=torch.float32,
+            # Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models
+            # (min_shape, optimize_target_shape, max_shape)
+            # For more information refer to fx/input_tensor_spec.py
+            shape_ranges = [
+                ((1, 2, 3), (4, 2, 3), (100, 2, 3)),
+            ],
+        ),
+        InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+
+    # Build a TRT interpreter. Set explicit_batch_dimension accordingly.
+    interpreter = TRTInterpreter(
+        acc_mod, input_specs, explicit_batch_dimension=True/False
+    )
+
+    # The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.
+    # The TRTInterpreterResult contains required parameter to build TRTModule, 
+    # and other informational output from TRTInterpreter run.
+    class TRTInterpreterResult(NamedTuple):
+        engine: Any
+        input_names: Sequence[str]
+        output_names: Sequence[str]
+        serialized_cache: bytearray
+
+    #max_batch_size: set accordingly for maximum batch size you will use.
+    #max_workspace_size: set to the maximum size we can afford for temporary buffer
+    #lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).
+    #sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity
+    #force_fp32_output: force output to be fp32
+    #strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.
+    #algorithm_selector: set up algorithm selection for certain layer
+    #timing_cache: enable timing cache for TensorRT
+    #profiling_verbosity: TensorRT logging level
+    trt_interpreter_result = interpreter.run(
+        max_batch_size=64,
+        max_workspace_size=1 << 25,     
+        sparse_weights=False,
+        force_fp32_output=False,
+        strict_type_constraints=False,
+        algorithm_selector=None,
+        timing_cache=None,
+        profiling_verbosity=None,
+    )
+
+
+*Common Errors:*
+
+RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.
+
+* **Step 3: Run the model**
+One way is using TRTModule, which is basically a PyTorch nn.Module.
+
+.. code-block:: shell
+
+    from torch_tensorrt.fx import TRTModule
+    mod = TRTModule(
+        trt_interpreter_result.engine, 
+        trt_interpreter_result.input_names, 
+        trt_interpreter_result.output_names)
+    # Just like all other PyTorch modules
+    outputs = mod(*inputs)
+    torch.save(mod, "trt.pt")
+    reload_trt_mod = torch.load("trt.pt")
+    reload_model_output = reload_trt_mod(*inputs)
+
+So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph's operation to corresponding TensorRT operation and build up the TensoRT engine for it. 
+
+Acc Tracer
+---------
+
+Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:
+
+1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write. 
+2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
+
+FX2TRT
+--------
+After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
+
+Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+        
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+
+How to Add a Missing Op
+****************
+
+You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer. 
+
+* **Step 1. Add a new acc op**
+
+TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.
+
+In `acc tracer <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py>`_, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.  
+
+In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.
+
+Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py>`_. e.g. the following code adds an acc op named foo() which adds two given inputs.
+
+.. code-block:: shell
+
+    # NOTE: all acc ops should only take kwargs as inputs, therefore we need the "*"
+    # at the beginning.
+    @register_acc_op
+    def foo(*, input, other, alpha):
+        return input + alpha * other
+ 
+There’re two ways to register a mapping. One is `register_acc_op_mapping() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164>`_. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.
+ 
+.. code-block:: shell
+
+    this_arg_is_optional = True
+
+    @register_acc_op_mapping(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other    
+ 
+``op_and_target`` determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the ``op_and_target``, we will trigger the mapping. Since we want to map from ``torch.add``, then op would be call_function and target would be ``torch.add``. ``arg_replacement_tuples`` determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in ``arg_replacement_tuples`` represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in *original node*. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.
+
+1. kwargs of original nodes and acc op nodes have different name.
+2. there’re optional arguments.
+
+The other way to register a mapping is through `register_custom_acc_mapper_fn() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206>`_. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.
+
+.. code-block:: shell
+
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other
+
+    @register_custom_acc_mapper_fn(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    def custom_mapper(node: torch.fx.Node, _: nn.Module) -> torch.fx.Node:
+        """
+        `node` is original node, which is a call_function node with target
+        being torch.add.
+        """
+        alpha = 1
+        if "alpha" in node.kwargs:
+            alpha = node.kwargs["alpha"]
+        foo_kwargs = {"input": node["input"], "other": node["other"], "alpha": alpha}
+        with node.graph.inserting_before(node):
+            foo_node = node.graph.call_function(foo, kwargs=foo_kwargs)
+            foo_node.meta = node.meta.copy()
+            return foo_node
+ 
+
+In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379>`_.
+
+The last step would be *adding unit test* for the new acc op or mapper function we added. The place to add the unit test is here `test_acc_tracer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py>`_.
+
+* **Step 2. Add a new fx2trt converter**
+
+All the developed converters for acc ops are all in `acc_op_converter.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py>`_. It could give you a good example of how the converter is added. 
+
+Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using `TensorRT APIs <https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html>`_.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+        
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+ 
+We need to use ``tensorrt_converter`` decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is `acc_ops.sigmoid` which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.
+
+**What if we can not find corresponding layers in TensorRT that do the same thing as the node.**
+
+In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. *We have not implement this feature yet. We will update once it is enabled*.
+
+Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this `folder <https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op>`_. 

From eb8ebb368d324abb7cfe38cbf88af45649ed6a6c Mon Sep 17 00:00:00 2001
From: Wei <wwei6@fb.com>
Date: Mon, 27 Jun 2022 15:14:16 -0700
Subject: [PATCH 02/10] Create get_started_fx_path.ipynb

---
 notebooks/get_started_fx_path.ipynb | 453 ++++++++++++++++++++++++++++
 1 file changed, 453 insertions(+)
 create mode 100644 notebooks/get_started_fx_path.ipynb

diff --git a/notebooks/get_started_fx_path.ipynb b/notebooks/get_started_fx_path.ipynb
new file mode 100644
index 0000000000..fb88c64714
--- /dev/null
+++ b/notebooks/get_started_fx_path.ipynb
@@ -0,0 +1,453 @@
+{
+  "metadata": {
+    "dataExplorerConfig": {},
+    "bento_stylesheets": {
+      "bento/extensions/flow/main.css": true,
+      "bento/extensions/kernel_selector/main.css": true,
+      "bento/extensions/kernel_ui/main.css": true,
+      "bento/extensions/new_kernel/main.css": true,
+      "bento/extensions/system_usage/main.css": true,
+      "bento/extensions/theme/main.css": true
+    },
+    "kernelspec": {
+      "display_name": "accelerators",
+      "language": "python",
+      "name": "bento_kernel_accelerators",
+      "metadata": {
+        "kernel_name": "bento_kernel_accelerators",
+        "nightly_builds": true,
+        "fbpkg_supported": true,
+        "cinder_runtime": false,
+        "is_prebuilt": true
+      }
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3"
+    },
+    "last_server_session_id": "42b65868-6af0-4f04-bf2f-b7e2511f23dd",
+    "last_kernel_id": "a08a7dfc-0fcc-4486-a2d5-604483260888",
+    "last_base_url": "https://devgpu005.ftw6.facebook.com:8093/",
+    "last_msg_id": "3f4cd9a4-65001843cf56aec954e05889_80",
+    "outputWidgetContext": {}
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "f9189964-8f5f-4c3d-b58c-ebff076ba890",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Here is a benchmark example demonstrates the basic usage of `lower_to_trt` interface.\n",
+        "It shows the boosted performance of TensorRT after lowering. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
+        "showInput": false,
+        "customInput": null
+      },
+      "source": [
+        "The purpose of this example is to demonstrate the overall flow of lowering a PyTorch\n",
+        "model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like:\n",
+        "1. Use splitter to split the model if there're ops in the model that we don't want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance.\n",
+        "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx2trt.\n",
+        "If we know the model is fully supported by fx2trt then we can skip the splitter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "ca68b029-68a6-42d6-968e-95bb7c1aae73",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "f56944ff-ade2-4041-bdd6-3bce44b1405f",
+        "customOutput": null,
+        "executionStartTime": 1656367410991,
+        "executionStopTime": 1656367412604
+      },
+      "source": [
+        "import torch\n",
+        "import torch.fx\n",
+        "import torch.nn as nn\n",
+        "from torch_tensorrt.fx.utils import LowerPrecision\n",
+        "import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer\n",
+        "from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule\n",
+        "from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "8f974ab2-d187-4ffe-a09b-16cd85949be4",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "564359f5-ac69-4666-91e1-41b299495ed1",
+        "customOutput": null,
+        "executionStartTime": 1656367414494,
+        "executionStopTime": 1656367422756
+      },
+      "source": [
+        "class Model(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.linear = nn.Linear(10, 10)\n",
+        "        self.relu = nn.ReLU()\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.linear(x)\n",
+        "        x = self.relu(x)\n",
+        "        x = torch.linalg.norm(x, ord=2, dim=1)\n",
+        "        x = self.relu(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "inputs = [torch.randn((1, 10), device=torch.device('cuda'))]\n",
+        "model = Model().cuda().eval()"
+      ],
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "0d407e92-e9e7-48aa-9c9e-1c21a9b5fd8f",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators\n",
+        "to acc ops."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "a1d9c8c2-8ec7-425a-8518-6f7e53ab1e67",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "ee2da608-5f1c-4f63-9927-544717e84e8a",
+        "customOutput": null,
+        "executionStartTime": 1656367480626,
+        "executionStopTime": 1656367482881
+      },
+      "source": [
+        "traced = acc_tracer.trace(model, inputs)"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "246613eb-14b5-488e-9aae-35306fc99db1",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Splitter will split the model into several submodules. The name of submodules will\n",
+        "be either `run_on_acc_{}` or `run_on_gpu_{}`. Submodules named `run_on_acc_{}` can\n",
+        "be fully lowered to TensorRT via fx2trt while submodules named `run_on_gpu_{}` has\n",
+        "unsupported ops and can't be lowered by fx2trt. We can still run `run_on_gpu_{}`\n",
+        "submodules on GPU if ops there have cuda implementation.\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "1103c70a-3766-4d89-ad2f-cdcb1c3891e0",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "feb888ea-ef9c-4577-b0c6-cf95bc1dd25e",
+        "customOutput": null,
+        "executionStartTime": 1656367487073,
+        "executionStopTime": 1656367487154
+      },
+      "source": [
+        "splitter = TRTSplitter(traced, inputs)"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "3d65e07e-57ed-47d5-adb9-4685c69c9c6b",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Preview functionality allows us to see what are the supported ops and unsupported\n",
+        "ops. We can optionally the dot graph which will color supported ops and unsupported\n",
+        "ops differently."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "6aaed2d5-61b7-438e-a72a-63f91d0709e2",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "2948c2f8-854b-4bc2-b399-321469da320c",
+        "customOutput": null,
+        "executionStartTime": 1656367489373,
+        "executionStopTime": 1656367489556
+      },
+      "source": [
+        "splitter.node_support_preview()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\nSupported node types in the model:\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\nacc_ops.relu: ((), {'input': torch.float32})\n\nUnsupported node types in the model:\nacc_ops.linalg_norm: ((), {'input': torch.float32})\n\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "\"\\nSupported node types in the model:\\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\\nacc_ops.relu: ((), {'input': torch.float32})\\n\\nUnsupported node types in the model:\\nacc_ops.linalg_norm: ((), {'input': torch.float32})\\n\""
+          },
+          "metadata": {
+            "bento_obj_id": "139812830161136"
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "8d8035ab-869e-4096-b8e1-3539a0cfe1af",
+        "showInput": false,
+        "customInput": null
+      },
+      "source": [
+        "After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "80e03730-955a-4cc8-b071-7f92a2cff3df",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "2ca46574-7176-4699-a809-2a2e2d5ffda0",
+        "customOutput": null,
+        "executionStartTime": 1656367495077,
+        "executionStopTime": 1656367495250
+      },
+      "source": [
+        "split_mod = splitter()\n",
+        "print(split_mod.graph)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Got 2 acc subgraphs and 1 non-acc subgraphs\ngraph():\n    %x : [#users=1] = placeholder[target=x]\n    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})\n    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})\n    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})\n    return _run_on_acc_2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "9ce75161-978e-468e-9989-ecdbc9af0d5b",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "0370de27-39ec-4be0-826b-9aec90df1155",
+        "customOutput": null,
+        "executionStartTime": 1656367496353,
+        "executionStopTime": 1656367496452
+      },
+      "source": [
+        "print(split_mod._run_on_acc_0.graph)\n",
+        "print(split_mod._run_on_gpu_1.graph)\n",
+        "print(split_mod._run_on_acc_2.graph)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "graph():\n    %x : [#users=1] = placeholder[target=x]\n    %linear_weight : [#users=1] = get_attr[target=linear.weight]\n    %linear_bias : [#users=1] = get_attr[target=linear.bias]\n    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})\n    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})\n    return relu_2\ngraph():\n    %relu_2 : [#users=1] = placeholder[target=relu_2]\n    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})\n    return linalg_norm_1\ngraph():\n    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]\n    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})\n    return relu_3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "7a6857bc-fedd-4847-ba17-a5d114de34f3",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "The `split_mod` contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "174fd2eb-a864-49cf-a204-6d24a8e2849d",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "cf7fdfe4-e781-47c8-9a9a-85b5664c10f7",
+        "customOutput": null,
+        "executionStartTime": 1656367502837,
+        "executionStopTime": 1656367510024,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "def get_submod_inputs(mod, submod, inputs):\n",
+        "    acc_inputs = None\n",
+        "\n",
+        "    def get_input(self, inputs):\n",
+        "        nonlocal acc_inputs\n",
+        "        acc_inputs = inputs\n",
+        "\n",
+        "    handle = submod.register_forward_pre_hook(get_input)\n",
+        "    mod(*inputs)\n",
+        "    handle.remove()\n",
+        "    return acc_inputs\n",
+        "\n",
+        "# Since the model is splitted into three segments. We need to lower each TRT eligible segment.\n",
+        "# If we know the model can be fully lowered, we can skip the splitter part.\n",
+        "for name, _ in split_mod.named_children():\n",
+        "    if \"_run_on_acc\" in name:\n",
+        "        submod = getattr(split_mod, name)\n",
+        "        # Get submodule inputs for fx2trt\n",
+        "        acc_inputs = get_submod_inputs(split_mod, submod, inputs)\n",
+        "\n",
+        "        # fx2trt replacement\n",
+        "        interp = TRTInterpreter(\n",
+        "            submod,\n",
+        "            InputTensorSpec.from_tensors(acc_inputs),\n",
+        "            explicit_batch_dimension=True,\n",
+        "        )\n",
+        "        r = interp.run(lower_precision=LowerPrecision.FP32)\n",
+        "        trt_mod = TRTModule(*r)\n",
+        "        setattr(split_mod, name, trt_mod)\n",
+        "\n",
+        "lowered_model_output = split_mod(*inputs)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "f1db3e1e-3a70-4735-a403-baa557b0f8a6",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "a7c4fa0f-cac6-4959-8fa6-13b3455137d3",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "f0c264ac-2bda-4c8e-a236-e2bd475e601e",
+        "customOutput": null,
+        "executionStartTime": 1656367515833,
+        "executionStopTime": 1656367516184
+      },
+      "source": [
+        "torch.save(split_mod, \"trt.pt\")\n",
+        "reload_trt_mod = torch.load(\"trt.pt\")\n",
+        "reload_model_output = reload_trt_mod(*inputs)\n",
+        "\n",
+        "# Make sure the results match\n",
+        "regular_model_output = model(*inputs)\n",
+        "torch.testing.assert_close(\n",
+        "    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2\n",
+        ")"
+      ],
+      "execution_count": 9,
+      "outputs": []
+    }
+  ]
+}

From fcf7e01c66dc9cef8b05b246849c43ef97c87f99 Mon Sep 17 00:00:00 2001
From: Wei <wwei6@fb.com>
Date: Mon, 27 Jun 2022 15:17:11 -0700
Subject: [PATCH 03/10] Update and rename get_started_fx_path.ipynb to
 getting_started_with_fx_path_module.ipynb

---
 ...getting_started_with_fx_path_module.ipynb} | 20 ++++---------------
 1 file changed, 4 insertions(+), 16 deletions(-)
 rename notebooks/{get_started_fx_path.ipynb => getting_started_with_fx_path_module.ipynb} (96%)

diff --git a/notebooks/get_started_fx_path.ipynb b/notebooks/getting_started_with_fx_path_module.ipynb
similarity index 96%
rename from notebooks/get_started_fx_path.ipynb
rename to notebooks/getting_started_with_fx_path_module.ipynb
index fb88c64714..5ee8884790 100644
--- a/notebooks/get_started_fx_path.ipynb
+++ b/notebooks/getting_started_with_fx_path_module.ipynb
@@ -44,30 +44,18 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "originalKey": "f9189964-8f5f-4c3d-b58c-ebff076ba890",
-        "showInput": true,
+        "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
+        "showInput": false,
         "customInput": null,
         "code_folding": [],
         "hidden_ranges": []
       },
-      "source": [
-        "Here is a benchmark example demonstrates the basic usage of `lower_to_trt` interface.\n",
-        "It shows the boosted performance of TensorRT after lowering. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
-        "showInput": false,
-        "customInput": null
-      },
       "source": [
         "The purpose of this example is to demonstrate the overall flow of lowering a PyTorch\n",
         "model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like:\n",
         "1. Use splitter to split the model if there're ops in the model that we don't want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance.\n",
-        "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx2trt.\n",
-        "If we know the model is fully supported by fx2trt then we can skip the splitter."
+        "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path.\n",
+        "If we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter."
       ]
     },
     {

From 672de80e43fc20509d74be9e14d4ba7e206da287 Mon Sep 17 00:00:00 2001
From: Wei <wwei6@fb.com>
Date: Mon, 27 Jun 2022 23:49:04 -0700
Subject: [PATCH 04/10] Create getting_started_with_fx_path_lower_to_trt.ipynb

---
 ...ng_started_with_fx_path_lower_to_trt.ipynb | 432 ++++++++++++++++++
 1 file changed, 432 insertions(+)
 create mode 100644 notebooks/getting_started_with_fx_path_lower_to_trt.ipynb

diff --git a/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb b/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
new file mode 100644
index 0000000000..22bae41ac7
--- /dev/null
+++ b/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
@@ -0,0 +1,432 @@
+{
+  "metadata": {
+    "dataExplorerConfig": {},
+    "bento_stylesheets": {
+      "bento/extensions/flow/main.css": true,
+      "bento/extensions/kernel_selector/main.css": true,
+      "bento/extensions/kernel_ui/main.css": true,
+      "bento/extensions/new_kernel/main.css": true,
+      "bento/extensions/system_usage/main.css": true,
+      "bento/extensions/theme/main.css": true
+    },
+    "kernelspec": {
+      "display_name": "accelerators",
+      "language": "python",
+      "name": "bento_kernel_accelerators",
+      "metadata": {
+        "kernel_name": "bento_kernel_accelerators",
+        "nightly_builds": true,
+        "fbpkg_supported": true,
+        "cinder_runtime": false,
+        "is_prebuilt": true
+      }
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3"
+    },
+    "last_server_session_id": "c6f6ab3c-9274-41e7-8592-b1b583442e00",
+    "last_kernel_id": "fcbf3a69-76a4-4730-9b41-bcd0b24729ca",
+    "last_base_url": "https://devgpu005.ftw6.facebook.com:8093/",
+    "last_msg_id": "e28f842c-f32dde25c1b80ef7d423dfee_407",
+    "outputWidgetContext": {}
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "8ca7695d-8a19-454e-b32b-3d5c36d52faf",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "The purpose of this example is to demostrate the onverall flow of lowering a PyTorch model\n",
+        "to TensorRT conveniently with lower.py. We integrated the transformation process including `TRTInterpreter`, `TRTModule`, pass optimization into the `lower_to_trt` API, users are encouraged to check the docstring of the API and tune it to meet your needs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "7909785f-b9b4-41dd-82af-c144b879df39",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "7db2accc-9fa4-4a1e-8142-d887f2947bcd",
+        "customOutput": null,
+        "executionStartTime": 1656395936225,
+        "executionStopTime": 1656395937851
+      },
+      "source": [
+        "import typing as t\n",
+        "from copy import deepcopy\n",
+        "from dataclasses import dataclass, field, replace\n",
+        "\n",
+        "import torch\n",
+        "import torchvision\n",
+        "from torch_tensorrt.fx.lower import lower_to_trt\n",
+        "from torch_tensorrt.fx.utils import LowerPrecision"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "e324a1ff-1bc2-4e78-932f-33534c3ac3f5",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Specify the `configuration` class used for FX path lowering and benchmark. To extend, add a new configuration field to this class, and modify the lowering or benchmark behavior in `run_configuration_benchmark()` correspondingly. It automatically stores all its values to a `Result` dataclass.   \n",
+        "`Result` is another dataclass that holds raw essential benchmark result values like Batch size, QPS, accuracy, etc..\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "a4455135-8633-4d2d-bdd3-6435a4a9f4dd",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "2835fffa-cc50-479a-9080-c4f7002c0726",
+        "customOutput": null,
+        "executionStartTime": 1656398717455,
+        "executionStopTime": 1656398717662
+      },
+      "source": [
+        "@dataclass\n",
+        "class Configuration:\n",
+        "    # number of inferences to run\n",
+        "    batch_iter: int\n",
+        "\n",
+        "    # Input batch size\n",
+        "    batch_size: int\n",
+        "\n",
+        "    # Friendly name of the configuration\n",
+        "    name: str = \"\"\n",
+        "\n",
+        "    # Whether to apply TRT lowering to the model before benchmarking\n",
+        "    trt: bool = False\n",
+        "\n",
+        "    # Whether to apply engine holder to the lowered model\n",
+        "    jit: bool = False\n",
+        "\n",
+        "    # Whether to enable FP16 mode for TRT lowering\n",
+        "    fp16: bool = False\n",
+        "\n",
+        "    # Relative tolerance for accuracy check after lowering. -1 means do not\n",
+        "    # check accuracy.\n",
+        "    accuracy_rtol: float = -1  # disable\n",
+        "    \n",
+        "@dataclass\n",
+        "class Result:\n",
+        "    module: torch.nn.Module = field(repr=False)\n",
+        "    input: t.Any = field(repr=False)\n",
+        "    conf: Configuration\n",
+        "    time_sec: float\n",
+        "    accuracy_res: t.Optional[bool] = None\n",
+        "\n",
+        "    @property\n",
+        "    def time_per_iter_ms(self) -> float:\n",
+        "        return self.time_sec * 1.0e3\n",
+        "\n",
+        "    @property\n",
+        "    def qps(self) -> float:\n",
+        "        return self.conf.batch_size / self.time_sec\n",
+        "\n",
+        "    def format(self) -> str:\n",
+        "        return (\n",
+        "            f\"== Benchmark Result for: {self.conf}\\n\"\n",
+        "            f\"BS: {self.conf.batch_size}, \"\n",
+        "            f\"Time per iter: {self.time_per_iter_ms:.2f}ms, \"\n",
+        "            f\"QPS: {self.qps:.2f}, \"\n",
+        "            f\"Accuracy: {self.accuracy_res} (rtol={self.conf.accuracy_rtol})\"\n",
+        "        )"
+      ],
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "3e462cf6-d282-402d-955b-a3ecb400bf0b",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Run FX path lowering and benchmark the given model according to the specified benchmark configuration. Prints the benchmark result for each configuration at the end of the run. `benchmark_torch_function` is the actual function that computes the fixed number of iterations of functions runs.\n",
+        "The FX path lowering and TensorRT engine creation is integrated into `low_to_trt()` API which is defined in `fx/lower.py` file.\n",
+        "It is good to list it out and show the usage of it. It takes in original module, input and lowering setting, run lowering workflow to turn module into a executable TRT engine \n",
+        "```\n",
+        "def lower_to_trt(\n",
+        "    module: nn.Module,\n",
+        "    input: ,\n",
+        "    max_batch_size: int = 2048,\n",
+        "    max_workspace_size=1 << 25,\n",
+        "    explicit_batch_dimension=False,\n",
+        "    lower_precision=LowerPrecision.FP16,\n",
+        "    verbose_log=False,\n",
+        "    timing_cache_prefix=\"\",\n",
+        "    save_timing_cache=False,\n",
+        "    cuda_graph_batch_size=-1,\n",
+        "    dynamic_batch=False,\n",
+        ") -> nn.Module:\n",
+        "``` \n",
+        "\n",
+        "    Args:\n",
+        "        module: Original module for lowering.\n",
+        "        input: Input for module.\n",
+        "        max_batch_size: Maximum batch size (must be >= 1 to be set, 0 means not set)\n",
+        "        max_workspace_size: Maximum size of workspace given to TensorRT.\n",
+        "        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.\n",
+        "        lower_precision: lower_precision config given to TRTModule.\n",
+        "        verbose_log: Enable verbose log for TensorRT if set True.\n",
+        "        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.\n",
+        "        save_timing_cache: Update timing cache with current timing cache data if set to True.\n",
+        "        cuda_graph_batch_size: Cuda graph batch size, default to be -1.\n",
+        "\n",
+        "    Returns:\n",
+        "        A torch.nn.Module lowered by TensorRT.\n",
+        "We testd a resnet18 network with input size of [128,3,224,224] for [Batch, Channel, Width, Height]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "91333212-7f6d-4bde-a248-44d485e83e5e",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "3002935b-b95a-4a08-a57f-f7a35485af5b",
+        "customOutput": null,
+        "executionStartTime": 1656397903207,
+        "executionStopTime": 1656397964752
+      },
+      "source": [
+        "test_model = torchvision.models.resnet18(pretrained=True)\n",
+        "input = [torch.rand(128, 3, 224, 224)]   \n",
+        "benchmark(test_model, input, 50, 128)\n",
+        "\n",
+        "def benchmark_torch_function(iters: int, f, *args) -> float:\n",
+        "    \"\"\"Estimates the average time duration for a single inference call in second\n",
+        "\n",
+        "    If the input is batched, then the estimation is for the batches inference call.\n",
+        "    \"\"\"\n",
+        "    with torch.inference_mode():\n",
+        "        f(*args)\n",
+        "    torch.cuda.synchronize()\n",
+        "    start_event = torch.cuda.Event(enable_timing=True)\n",
+        "    end_event = torch.cuda.Event(enable_timing=True)\n",
+        "    print(\"== Start benchmark iterations\")\n",
+        "    with torch.inference_mode():\n",
+        "        start_event.record()\n",
+        "        for _ in range(iters):\n",
+        "            f(*args)\n",
+        "        end_event.record()\n",
+        "    torch.cuda.synchronize()\n",
+        "    print(\"== End benchmark iterations\")\n",
+        "    return (start_event.elapsed_time(end_event) * 1.0e-3) / iters\n",
+        "\n",
+        "\n",
+        "def run_configuration_benchmark(\n",
+        "    module,\n",
+        "    input,\n",
+        "    conf: Configuration,\n",
+        ") -> Result:\n",
+        "    print(f\"=== Running benchmark for: {conf}\", \"green\")\n",
+        "    time = -1.0\n",
+        "\n",
+        "    if conf.fp16:\n",
+        "        module = module.half()\n",
+        "        input = [i.half() for i in input]\n",
+        "\n",
+        "    if not conf.trt:\n",
+        "        # Run eager mode benchmark\n",
+        "        time = benchmark_torch_function(conf.batch_iter, lambda: module(*input))\n",
+        "    elif not conf.jit:\n",
+        "        # Run lowering eager mode benchmark\n",
+        "        lowered_module = lower_to_trt(\n",
+        "            module,\n",
+        "            input,\n",
+        "            max_batch_size=conf.batch_size,\n",
+        "            lower_precision=LowerPrecision.FP16 if conf.fp16 else LowerPrecision.FP32,\n",
+        "        )\n",
+        "        time = benchmark_torch_function(conf.batch_iter, lambda: lowered_module(*input))\n",
+        "    else:\n",
+        "        print(\"Lowering with JIT is not available!\", \"red\")\n",
+        "\n",
+        "    result = Result(module=module, input=input, conf=conf, time_sec=time)\n",
+        "    return result\n",
+        "\n",
+        "@torch.inference_mode()\n",
+        "def benchmark(\n",
+        "    model,\n",
+        "    inputs,\n",
+        "    batch_iter: int,\n",
+        "    batch_size: int,\n",
+        ") -> None:\n",
+        "    model = model.cuda().eval()\n",
+        "    inputs = [x.cuda() for x in inputs]\n",
+        "\n",
+        "    # benchmark base configuration\n",
+        "    conf = Configuration(batch_iter=batch_iter, batch_size=batch_size)\n",
+        "\n",
+        "    configurations = [\n",
+        "        # Baseline\n",
+        "        replace(conf, name=\"CUDA Eager\", trt=False),\n",
+        "        # FP32\n",
+        "        replace(\n",
+        "            conf,\n",
+        "            name=\"TRT FP32 Eager\",\n",
+        "            trt=True,\n",
+        "            jit=False,\n",
+        "            fp16=False,\n",
+        "            accuracy_rtol=1e-3,\n",
+        "        ),\n",
+        "        # FP16\n",
+        "        replace(\n",
+        "            conf,\n",
+        "            name=\"TRT FP16 Eager\",\n",
+        "            trt=True,\n",
+        "            jit=False,\n",
+        "            fp16=True,\n",
+        "            accuracy_rtol=1e-2,\n",
+        "        ),\n",
+        "    ]\n",
+        "\n",
+        "    results = [\n",
+        "        run_configuration_benchmark(deepcopy(model), inputs, conf_)\n",
+        "        for conf_ in configurations\n",
+        "    ]\n",
+        "\n",
+        "    for res in results:\n",
+        "        print(res.format())"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 233143.380 manifold.py:1430] URL manifold://torchvision/tree/models/resnet18-f37072fd.pth was already cached in /home/wwei6/.torch/iopath_cache/manifold_cache/tree/models/resnet18-f37072fd.pth\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "=== Running benchmark for: Configuration(batch_iter=50, batch_size=128, name='CUDA Eager', trt=False, jit=False, fp16=False, accuracy_rtol=-1) green\n== Start benchmark iterations\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== End benchmark iterations\n=== Running benchmark for: Configuration(batch_iter=50, batch_size=128, name='TRT FP32 Eager', trt=True, jit=False, fp16=False, accuracy_rtol=0.001) green\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== Log pass <function fuse_permute_matmul at 0x7fbdfcc9f1f0> before/after graph to /tmp/tmpaayayg72\n== Log pass <function fuse_permute_linear at 0x7fbe36555f70> before/after graph to /tmp/tmpdw_pq71j\n\nSupported node types in the model:\nacc_ops.conv2d: ((), {'input': torch.float32, 'weight': torch.float32})\nacc_ops.batch_norm: ((), {'input': torch.float32, 'running_mean': torch.float32, 'running_var': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\nacc_ops.relu: ((), {'input': torch.float32})\nacc_ops.max_pool2d: ((), {'input': torch.float32})\nacc_ops.add: ((), {'input': torch.float32, 'other': torch.float32})\nacc_ops.adaptive_avg_pool2d: ((), {'input': torch.float32})\nacc_ops.flatten: ((), {'input': torch.float32})\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\n\nUnsupported node types in the model:\n\nGot 1 acc subgraphs and 0 non-acc subgraphs\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 233146.650 fx2trt.py:190] Run Module elapsed time: 0:00:00.244369\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 233206.570 fx2trt.py:241] Build TRT engine elapsed time: 0:00:19.918630\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== Start benchmark iterations\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== End benchmark iterations\n=== Running benchmark for: Configuration(batch_iter=50, batch_size=128, name='TRT FP16 Eager', trt=True, jit=False, fp16=True, accuracy_rtol=0.01) green\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== Log pass <function fuse_permute_matmul at 0x7fbdfcc9f1f0> before/after graph to /tmp/tmpnoeblgd5\n== Log pass <function fuse_permute_linear at 0x7fbe36555f70> before/after graph to /tmp/tmpyb1egsof\n\nSupported node types in the model:\nacc_ops.conv2d: ((), {'input': torch.float16, 'weight': torch.float16})\nacc_ops.batch_norm: ((), {'input': torch.float16, 'running_mean': torch.float16, 'running_var': torch.float16, 'weight': torch.float16, 'bias': torch.float16})\nacc_ops.relu: ((), {'input': torch.float16})\nacc_ops.max_pool2d: ((), {'input': torch.float16})\nacc_ops.add: ((), {'input': torch.float16, 'other': torch.float16})\nacc_ops.adaptive_avg_pool2d: ((), {'input': torch.float16})\nacc_ops.flatten: ((), {'input': torch.float16})\nacc_ops.linear: ((), {'input': torch.float16, 'weight': torch.float16, 'bias': torch.float16})\n\nUnsupported node types in the model:\n\nGot 1 acc subgraphs and 0 non-acc subgraphs\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 233208.996 fx2trt.py:190] Run Module elapsed time: 0:00:00.217076\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 233244.147 fx2trt.py:241] Build TRT engine elapsed time: 0:00:35.150950\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== Start benchmark iterations\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "== End benchmark iterations\n== Benchmark Result for: Configuration(batch_iter=50, batch_size=128, name='CUDA Eager', trt=False, jit=False, fp16=False, accuracy_rtol=-1)\nBS: 128, Time per iter: 15.00ms, QPS: 8530.72, Accuracy: None (rtol=-1)\n== Benchmark Result for: Configuration(batch_iter=50, batch_size=128, name='TRT FP32 Eager', trt=True, jit=False, fp16=False, accuracy_rtol=0.001)\nBS: 128, Time per iter: 7.95ms, QPS: 16098.45, Accuracy: None (rtol=0.001)\n== Benchmark Result for: Configuration(batch_iter=50, batch_size=128, name='TRT FP16 Eager', trt=True, jit=False, fp16=True, accuracy_rtol=0.01)\nBS: 128, Time per iter: 4.36ms, QPS: 29365.31, Accuracy: None (rtol=0.01)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "80bbae99-41ff-4baa-94a5-12bf0c9938f3",
+        "showInput": true,
+        "customInput": null
+      },
+      "source": [
+        ""
+      ]
+    }
+  ]
+}

From 193b29ccbf67d12466dee8bf33e162603d094d0c Mon Sep 17 00:00:00 2001
From: Wei <wwei6@fb.com>
Date: Mon, 27 Jun 2022 23:50:55 -0700
Subject: [PATCH 05/10] Update getting_started_with_fx_path_lower_to_trt.ipynb

---
 .../getting_started_with_fx_path_lower_to_trt.ipynb      | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb b/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
index 22bae41ac7..5bbe61659d 100644
--- a/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
+++ b/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
@@ -51,7 +51,7 @@
         "hidden_ranges": []
       },
       "source": [
-        "The purpose of this example is to demostrate the onverall flow of lowering a PyTorch model\n",
+        "The purpose of this example is to demostrate the overall flow of lowering a PyTorch model\n",
         "to TensorRT conveniently with lower.py. We integrated the transformation process including `TRTInterpreter`, `TRTModule`, pass optimization into the `lower_to_trt` API, users are encouraged to check the docstring of the API and tune it to meet your needs."
       ]
     },
@@ -324,13 +324,6 @@
       ],
       "execution_count": 21,
       "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "I0627 233143.380 manifold.py:1430] URL manifold://torchvision/tree/models/resnet18-f37072fd.pth was already cached in /home/wwei6/.torch/iopath_cache/manifold_cache/tree/models/resnet18-f37072fd.pth\n"
-          ]
-        },
         {
           "output_type": "stream",
           "name": "stdout",

From c00b15800d0e49c96f74e95972572565ba145a1b Mon Sep 17 00:00:00 2001
From: Wei <wwei6@fb.com>
Date: Tue, 28 Jun 2022 00:18:35 -0700
Subject: [PATCH 06/10] Update getting_started_with_fx_path_lower_to_trt.ipynb

---
 notebooks/getting_started_with_fx_path_lower_to_trt.ipynb | 1 +
 1 file changed, 1 insertion(+)

diff --git a/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb b/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
index 5bbe61659d..5ef957fa36 100644
--- a/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
+++ b/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb
@@ -202,6 +202,7 @@
         "        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.\n",
         "        save_timing_cache: Update timing cache with current timing cache data if set to True.\n",
         "        cuda_graph_batch_size: Cuda graph batch size, default to be -1.\n",
+        "        dynamic_batch: batch dimension (dim=0) is dynamic.\n",
         "\n",
         "    Returns:\n",
         "        A torch.nn.Module lowered by TensorRT.\n",

From c19c6b903f4448aaf7cbd09163cbf1163c6ec5ba Mon Sep 17 00:00:00 2001
From: Wei Wei <wwei6@fb.com>
Date: Tue, 28 Jun 2022 10:02:07 -0700
Subject: [PATCH 07/10] add version() in setup.py

---
 py/setup.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/py/setup.py b/py/setup.py
index f195abdbfd..ceaa681016 100644
--- a/py/setup.py
+++ b/py/setup.py
@@ -143,6 +143,7 @@ def finalize_options(self):
 
     def run(self):
         if FX_ONLY:
+            gen_version_file()
             develop.run(self)
         else:
             global CXX11_ABI
@@ -163,6 +164,7 @@ def finalize_options(self):
 
     def run(self):
         if FX_ONLY:
+            gen_version_file()
             install.run(self)
         else:
             global CXX11_ABI

From 7db85f3111d02ccfd6aa615d663c4db61940f598 Mon Sep 17 00:00:00 2001
From: Wei Wei <wwei6@fb.com>
Date: Tue, 28 Jun 2022 10:08:14 -0700
Subject: [PATCH 08/10] make html

---
 .../classtorch__tensorrt_1_1DataType.html     |    2 +-
 ...rch__tensorrt_1_1Device_1_1DeviceType.html |    2 +-
 .../classtorch__tensorrt_1_1TensorFormat.html |    2 +-
 ...ensorrt_1_1ptq_1_1Int8CacheCalibrator.html |    2 +-
 ...ch__tensorrt_1_1ptq_1_1Int8Calibrator.html |    2 +-
 ...8h_1a18d295a837ac71add5578860b55e5502.html |    2 +-
 ...8h_1a282fd3c0b1c3a215148ae372070e1268.html |    2 +-
 ...8h_1a31398a6d4d27e28817afb0f0139e909e.html |    2 +-
 ...8h_1a35703561b26b1a9d2738ad7d58b27827.html |    2 +-
 ...8h_1abd1465eb38256d3f22cc1426b23d516b.html |    2 +-
 ...8h_1abe87b341f562fd1cf40b7672e4d759da.html |    2 +-
 ...8h_1ad19939408f7be171a74a89928b36eb59.html |    2 +-
 ...8h_1adad592a7b1b7eed529cdf6acd584c883.html |    2 +-
 docs/_cpp_api/dir_cpp.html                    |    2 +-
 docs/_cpp_api/dir_cpp_include.html            |    2 +-
 .../dir_cpp_include_torch_tensorrt.html       |    2 +-
 ...8h_1a130f65408ad8cbaee060f05e8db69558.html |    2 +-
 ...8h_1a3fbe5d72e4fc624dbd038853079620eb.html |    8 +-
 ..._cpp_include_torch_tensorrt_logging.h.html |    2 +-
 ...e_cpp_include_torch_tensorrt_macros.h.html |    2 +-
 ...file_cpp_include_torch_tensorrt_ptq.h.html |    2 +-
 ...clude_torch_tensorrt_torch_tensorrt.h.html |    2 +-
 ...8h_1a0593f776f469c20469e2f729fc7861a3.html |    2 +-
 ...8h_1a0c012cb374addd90eb1f42eaec570650.html |    2 +-
 ...8h_1a56e110feaaba2c3fd44bd201fd21a76a.html |    2 +-
 ...8h_1a7cb50492421ea9de4e3db895819df6f2.html |    2 +-
 ...8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html |    2 +-
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 ...8h_1a9835f6e605dce1abf442a55b64d6dffa.html |    2 +-
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 ...8h_1ac4ab8313ae72c2c899ea31548b528528.html |    2 +-
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 ...8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html |    2 +-
 docs/_cpp_api/namespace_torch_tensorrt.html   |    2 +-
 .../namespace_torch_tensorrt__logging.html    |    2 +-
 .../namespace_torch_tensorrt__ptq.html        |    2 +-
 ...namespace_torch_tensorrt__torchscript.html |    2 +-
 ..._cpp_include_torch_tensorrt_logging.h.html |    2 +-
 ...e_cpp_include_torch_tensorrt_macros.h.html |    2 +-
 ...file_cpp_include_torch_tensorrt_ptq.h.html |    2 +-
 ...clude_torch_tensorrt_torch_tensorrt.h.html |    2 +-
 .../structtorch__tensorrt_1_1Device.html      |    2 +-
 .../structtorch__tensorrt_1_1Input.html       |    2 +-
 ...ensorrt_1_1torchscript_1_1CompileSpec.html |    2 +-
 docs/_cpp_api/torch_tensort_cpp.html          |    2 +-
 docs/_cpp_api/unabridged_orphan.html          |    2 +-
 docs/_notebooks/CitriNet-example.html         |   24 +-
 docs/_notebooks/EfficientNet-example.html     |   24 +-
 docs/_notebooks/Hugging-Face-BERT.html        |   28 +-
 docs/_notebooks/Resnet50-example.html         |   24 +-
 docs/_notebooks/dynamic-shapes.html           |    4 +-
 .../getting_started_with_fx_path_module.html  | 1093 +++++++++++++++++
 .../getting_started_with_fx_path_module.ipynb |  469 +++++++
 docs/_notebooks/lenet-getting-started.html    |   16 +-
 .../_notebooks/ssd-object-detection-demo.html |   24 +-
 docs/_notebooks/vgg-qat.html                  |   44 +-
 ...ting_started_with_fx_path_module.ipynb.txt |  441 +++++++
 .../getting_started_with_fx_path.rst.txt      |  304 +++++
 docs/contributors/conversion.html             |    2 +-
 docs/contributors/lowering.html               |    2 +-
 docs/contributors/partitioning.html           |    2 +-
 docs/contributors/phases.html                 |    2 +-
 docs/contributors/runtime.html                |    2 +-
 docs/contributors/system_overview.html        |    2 +-
 docs/contributors/useful_links.html           |    2 +-
 docs/contributors/writing_converters.html     |    2 +-
 docs/genindex.html                            |  256 +---
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diff --git a/docs/_notebooks/CitriNet-example.html b/docs/_notebooks/CitriNet-example.html
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@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="2acf632451c2482b8eee3d1e461fb6c5" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="058c2a32b62b4b52a6bf5f2233a2258a" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT-Getting-Started---CitriNet">
 <h1>Torch-TensorRT Getting Started - CitriNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---CitriNet" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -640,7 +640,9 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <li><p><a class="reference external" href="#4">Benchmark Torch-TensorRT models</a></p></li>
 <li><p><a class="reference external" href="#5">Conclusion</a></p></li>
 </ol>
-<p>## 1. Requirements</p>
+<blockquote>
+<div><p>## 1. Requirements</p>
+</div></blockquote>
 <p>Follow the steps in <a class="reference external" href="README.md">README</a> to prepare a Docker container, within which you can run this notebook. This notebook assumes that you are within a Jupyter environment in a docker container with Torch-TensorRT installed, such as an NGC monthly release of <code class="docutils literal notranslate"><span class="pre">nvcr.io/nvidia/pytorch:&lt;yy.mm&gt;-py3</span></code> (where <code class="docutils literal notranslate"><span class="pre">yy</span></code> indicates the last two numbers of a calendar year, and <code class="docutils literal notranslate"><span class="pre">mm</span></code> indicates the month in two-digit numerical form)</p>
 <p>Now that you are in the docker, the next step is to install the required dependencies.</p>
 <div class="nbinput docutils container">
@@ -913,7 +915,9 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
 </pre></div></div>
 </div>
-<p>## 2. Download Citrinet model</p>
+<blockquote>
+<div><p>## 2. Download Citrinet model</p>
+</div></blockquote>
 <p>Next, we download a pretrained Nemo Citrinet model and convert it to a Torchscript module:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -1220,7 +1224,9 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 +-----------------------------------------------------------------------------+
 </pre></div></div>
 </div>
-<p>## 3. Create Torch-TensorRT modules</p>
+<blockquote>
+<div><p>## 3. Create Torch-TensorRT modules</p>
+</div></blockquote>
 <p>In this step, we optimize the Citrinet Torchscript module with Torch-TensorRT with various precisions and batch sizes.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -1266,7 +1272,9 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 Generating Torchscript-TensorRT module for batchsize 128 precision torch.float16
 </pre></div></div>
 </div>
-<p>## 4. Benchmark Torch-TensorRT models</p>
+<blockquote>
+<div><p>## 4. Benchmark Torch-TensorRT models</p>
+</div></blockquote>
 <p>Finally, we are ready to benchmark the Torch-TensorRT optimized Citrinet models.</p>
 </section>
 <section id="FP32-(single-precision)">
@@ -1417,7 +1425,9 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 
 </pre></div></div>
 </div>
-<p>## 5. Conclusion</p>
+<blockquote>
+<div><p>## 5. Conclusion</p>
+</div></blockquote>
 <p>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 ~<strong>2.4X</strong> with FP32, and ~<strong>2.9X</strong> with FP16 at batchsize of 128.</p>
 </section>
 <section id="What’s-next">
diff --git a/docs/_notebooks/EfficientNet-example.html b/docs/_notebooks/EfficientNet-example.html
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@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="783b2cf2fc5d4e06a7736325633fdd88" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="41bd2c3185814f14a40caa4b3963533a" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
 <h1>Torch-TensorRT Getting Started - EfficientNet-B0<a class="headerlink" href="#Torch-TensorRT-Getting-Started---EfficientNet-B0" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -691,16 +691,22 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 +-----------------------------------------------------------------------------+
 </pre></div></div>
 </div>
-<p>## 1. Requirements</p>
+<blockquote>
+<div><p>## 1. Requirements</p>
+</div></blockquote>
 <p>NVIDIA’s NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
 <p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
-<p>## 2. EfficientNet Overview</p>
+<blockquote>
+<div><p>## 2. EfficientNet Overview</p>
+</div></blockquote>
 <p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
 <section id="Model-Description">
 <h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
 <p>This model is based on the <a class="reference external" href="https://arxiv.org/abs/1905.11946">EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks</a> paper.</p>
 <p><img alt="alt" class="no-scaled-link" src="https://1.bp.blogspot.com/-Cdtb97FtgdA/XO3BHsB7oEI/AAAAAAAAEKE/bmtkonwgs8cmWyI5esVo8wJPnhPLQ5bGQCLcBGAs/s1600/image4.png" style="width: 100%;" /></p>
-<p>## 3. Running the model without optimizations</p>
+<blockquote>
+<div><p>## 3. Running the model without optimizations</p>
+</div></blockquote>
 <p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -975,7 +981,9 @@ <h3>Model Description<a class="headerlink" href="#Model-Description" title="Perm
 Average throughput: 3393.46 images/second
 </pre></div></div>
 </div>
-<p>## 4. Accelerating with Torch-TensorRT</p>
+<blockquote>
+<div><p>## 4. Accelerating with Torch-TensorRT</p>
+</div></blockquote>
 <p>Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a>.</p>
 </section>
 <section id="FP32-(single-precision)">
@@ -1126,7 +1134,9 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 Average throughput: 10362.23 images/second
 </pre></div></div>
 </div>
-<p>## 5. Conclusion</p>
+<blockquote>
+<div><p>## 5. Conclusion</p>
+</div></blockquote>
 <p>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 <strong>1.35x</strong> with FP32, and <strong>3.13x</strong> 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.</p>
 </section>
diff --git a/docs/_notebooks/Hugging-Face-BERT.html b/docs/_notebooks/Hugging-Face-BERT.html
index 42c58b5cef..5fd0d4ca30 100644
--- a/docs/_notebooks/Hugging-Face-BERT.html
+++ b/docs/_notebooks/Hugging-Face-BERT.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="2e8a7944f7a54c9a93c9656fc33a0ab3" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="8ebcdec6af2d48ce9991ef016c10f3fd" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer">
 <h1>Masked Language Modeling (MLM) with Hugging Face BERT Transformer<a class="headerlink" href="#Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer" title="Permalink to this headline">¶</a></h1>
 <section id="Learning-objectives">
@@ -635,7 +635,9 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 <li><p><a class="reference external" href="#5">Benchmarking</a></p></li>
 <li><p><a class="reference external" href="#6">Conclusion</a></p></li>
 </ol>
-<p>## 1. Requirements</p>
+<blockquote>
+<div><p>## 1. Requirements</p>
+</div></blockquote>
 <p>NVIDIA’s NGC provides a PyTorch Docker Container which contains PyTorch and Torch-TensorRT. Starting with version <code class="docutils literal notranslate"><span class="pre">22.05-py3</span></code>, we can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
 <p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
 <div class="nbinput docutils container">
@@ -688,13 +690,17 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<p>## 2. BERT Overview</p>
+<blockquote>
+<div><p>## 2. BERT Overview</p>
+</div></blockquote>
 <p>Transformers comprise a class of deep learning algorithms employing self-attention; broadly speaking, the models learn large matrices of numbers, each element of which denotes how important one component of input data is to another. Since their introduction in 2017, transformers have enjoyed widespread adoption, particularly in natural language processing, but also in computer vision problems. This is largely because they are easier to parallelize than the sequence models which attention
 mechanisms were originally designed to augment.</p>
 <p>Hugging Face is a company that maintains a huge respository of pre-trained transformer models. The company also provides tools for integrating those models into PyTorch code and running inference with them.</p>
 <p>One of the most popular transformer models is BERT (Bidirectional Encoder Representations from Transformers). First developed at Google and released in 2018, it has become the backbone of Google’s search engine and a standard benchmark for NLP experiments. BERT was originally trained for next sentence prediction and masked language modeling (MLM), which aims to predict hidden words in sentences. In this notebook, we will use Hugging Face’s <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> model (BERT’s smallest and
 simplest form, which does not employ text capitalization) for MLM.</p>
-<p>## 3. Creating TorchScript modules</p>
+<blockquote>
+<div><p>## 3. Creating TorchScript modules</p>
+</div></blockquote>
 <p>First, create a pretrained BERT tokenizer from the <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> model</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -809,7 +815,9 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.
 </pre></div></div>
 </div>
-<p>## 4. Compiling with Torch-TensorRT</p>
+<blockquote>
+<div><p>## 4. Compiling with Torch-TensorRT</p>
+</div></blockquote>
 <p>Change the logging level to avoid long printouts</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -879,7 +887,9 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<p>## 5. Benchmarking</p>
+<blockquote>
+<div><p>## 5. Benchmarking</p>
+</div></blockquote>
 <p>In developing this notebook, we conducted our benchmarking on a single NVIDIA A100 GPU. Your results may differ from those shown, particularly on a different GPU.</p>
 <p>This function passes the inputs into the model and runs inference <code class="docutils literal notranslate"><span class="pre">num_loops</span></code> times, then returns a list of length containing the amount of time in seconds that each instance of inference took.</p>
 <div class="nbinput nblast docutils container">
@@ -1049,7 +1059,9 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 
 </pre></div></div>
 </div>
-<p>## 6. Conclusion</p>
+<blockquote>
+<div><p>## 6. Conclusion</p>
+</div></blockquote>
 <p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for Masked Language Modeling with Hugging Face’s <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> transformer and testing the performance impact of the optimization. With Torch-TensorRT on an NVIDIA A100 GPU, we observe the speedups indicated below. 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.</p>
 <p>Scripted (GPU): 1.0x Traced (GPU): 1.62x Torch-TensorRT (FP32): 2.14x Torch-TensorRT (FP16): 3.15x</p>
diff --git a/docs/_notebooks/Resnet50-example.html b/docs/_notebooks/Resnet50-example.html
index f5ef38075d..32e209e3d6 100644
--- a/docs/_notebooks/Resnet50-example.html
+++ b/docs/_notebooks/Resnet50-example.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="fdd5160d4757402ea245c7fbb8dc0f03" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
+<p><img alt="44c689c3c3824ebd9639c100f7c30cd4" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
 <section id="Torch-TensorRT-Getting-Started---ResNet-50">
 <h1>Torch-TensorRT Getting Started - ResNet 50<a class="headerlink" href="#Torch-TensorRT-Getting-Started---ResNet-50" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -744,16 +744,22 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
 </pre></div></div>
 </div>
-<p>## 1. Requirements</p>
+<blockquote>
+<div><p>## 1. Requirements</p>
+</div></blockquote>
 <p>NVIDIA’s NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
 <p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
-<p>## 2. ResNet-50 Overview</p>
+<blockquote>
+<div><p>## 2. ResNet-50 Overview</p>
+</div></blockquote>
 <p>PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.</p>
 <section id="Model-Description">
 <h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
 <p>This ResNet-50 model is based on the <a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a> paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network”. The input size is fixed to 32x32.</p>
 <p><img alt="alt" class="no-scaled-link" src="https://pytorch.org/assets/images/resnet.png" style="width: 50%;" /></p>
-<p>## 3. Running the model without optimizations</p>
+<blockquote>
+<div><p>## 3. Running the model without optimizations</p>
+</div></blockquote>
 <p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -1222,7 +1228,9 @@ <h3>Model Description<a class="headerlink" href="#Model-Description" title="Perm
 Average batch time: 75.41 ms
 </pre></div></div>
 </div>
-<p>## 4. Accelerating with Torch-TensorRT</p>
+<blockquote>
+<div><p>## 4. Accelerating with Torch-TensorRT</p>
+</div></blockquote>
 <p>Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a>.</p>
 </section>
 <section id="FP32-(single-precision)">
@@ -1345,7 +1353,9 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 Average batch time: 14.69 ms
 </pre></div></div>
 </div>
-<p>## 5. Conclusion</p>
+<blockquote>
+<div><p>## 5. Conclusion</p>
+</div></blockquote>
 <p>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 <strong>1.84x</strong> with FP32, and <strong>5.2x</strong> 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.</p>
 </section>
diff --git a/docs/_notebooks/dynamic-shapes.html b/docs/_notebooks/dynamic-shapes.html
index 172b61fbce..b8694b4eec 100644
--- a/docs/_notebooks/dynamic-shapes.html
+++ b/docs/_notebooks/dynamic-shapes.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="195ac3794a674b1a8c327ebde46a3be4" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="22724bc77cb446a7944bbacf7074a017" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT---Using-Dynamic-Shapes">
 <h1>Torch-TensorRT - Using Dynamic Shapes<a class="headerlink" href="#Torch-TensorRT---Using-Dynamic-Shapes" title="Permalink to this headline">¶</a></h1>
 <p>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
diff --git a/docs/_notebooks/getting_started_with_fx_path_module.html b/docs/_notebooks/getting_started_with_fx_path_module.html
new file mode 100644
index 0000000000..dbeab43b3a
--- /dev/null
+++ b/docs/_notebooks/getting_started_with_fx_path_module.html
@@ -0,0 +1,1093 @@
+
+
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+<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
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+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<p>The purpose of this example is to demonstrate the overall flow of lowering a PyTorch model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like: 1. Use splitter to split the model if there’re ops in the model that we don’t want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance. 2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path. If
+we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+import torch.fx
+import torch.nn as nn
+from torch_tensorrt.fx.utils import LowerPrecision
+import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule
+from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(10, 10)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.relu(x)
+        x = torch.linalg.norm(x, ord=2, dim=1)
+        x = self.relu(x)
+        return x
+
+
+inputs = [torch.randn((1, 10), device=torch.device(&#39;cuda&#39;))]
+model = Model().cuda().eval()
+</pre></div>
+</div>
+</div>
+<p>acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators to acc ops.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>traced = acc_tracer.trace(model, inputs)
+</pre></div>
+</div>
+</div>
+<p>Splitter will split the model into several submodules. The name of submodules will be either <code class="docutils literal notranslate"><span class="pre">run_on_acc_{}</span></code> or <code class="docutils literal notranslate"><span class="pre">run_on_gpu_{}</span></code>. Submodules named <code class="docutils literal notranslate"><span class="pre">run_on_acc_{}</span></code> can be fully lowered to TensorRT via fx2trt while submodules named <code class="docutils literal notranslate"><span class="pre">run_on_gpu_{}</span></code> has unsupported ops and can’t be lowered by fx2trt. We can still run <code class="docutils literal notranslate"><span class="pre">run_on_gpu_{}</span></code> submodules on GPU if ops there have cuda implementation.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>splitter = TRTSplitter(traced, inputs)
+</pre></div>
+</div>
+</div>
+<p>Preview functionality allows us to see what are the supported ops and unsupported ops. We can optionally the dot graph which will color supported ops and unsupported ops differently.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>splitter.node_support_preview()
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+Supported node types in the model:
+acc_ops.linear: ((), {&#39;input&#39;: torch.float32, &#39;weight&#39;: torch.float32, &#39;bias&#39;: torch.float32})
+acc_ops.relu: ((), {&#39;input&#39;: torch.float32})
+
+Unsupported node types in the model:
+acc_ops.linalg_norm: ((), {&#39;input&#39;: torch.float32})
+
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#34;\nSupported node types in the model:\nacc_ops.linear: ((), {&#39;input&#39;: torch.float32, &#39;weight&#39;: torch.float32, &#39;bias&#39;: torch.float32})\nacc_ops.relu: ((), {&#39;input&#39;: torch.float32})\n\nUnsupported node types in the model:\nacc_ops.linalg_norm: ((), {&#39;input&#39;: torch.float32})\n&#34;
+</pre></div></div>
+</div>
+<p>After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>split_mod = splitter()
+print(split_mod.graph)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Got 2 acc subgraphs and 1 non-acc subgraphs
+graph():
+    %x : [#users=1] = placeholder[target=x]
+    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})
+    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})
+    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})
+    return _run_on_acc_2
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(split_mod._run_on_acc_0.graph)
+print(split_mod._run_on_gpu_1.graph)
+print(split_mod._run_on_acc_2.graph)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+graph():
+    %x : [#users=1] = placeholder[target=x]
+    %linear_weight : [#users=1] = get_attr[target=linear.weight]
+    %linear_bias : [#users=1] = get_attr[target=linear.bias]
+    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})
+    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})
+    return relu_2
+graph():
+    %relu_2 : [#users=1] = placeholder[target=relu_2]
+    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})
+    return linalg_norm_1
+graph():
+    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]
+    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})
+    return relu_3
+</pre></div></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">split_mod</span></code> contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def get_submod_inputs(mod, submod, inputs):
+    acc_inputs = None
+
+    def get_input(self, inputs):
+        nonlocal acc_inputs
+        acc_inputs = inputs
+
+    handle = submod.register_forward_pre_hook(get_input)
+    mod(*inputs)
+    handle.remove()
+    return acc_inputs
+
+# Since the model is splitted into three segments. We need to lower each TRT eligible segment.
+# If we know the model can be fully lowered, we can skip the splitter part.
+for name, _ in split_mod.named_children():
+    if &quot;_run_on_acc&quot; in name:
+        submod = getattr(split_mod, name)
+        # Get submodule inputs for fx2trt
+        acc_inputs = get_submod_inputs(split_mod, submod, inputs)
+
+        # fx2trt replacement
+        interp = TRTInterpreter(
+            submod,
+            InputTensorSpec.from_tensors(acc_inputs),
+            explicit_batch_dimension=True,
+        )
+        r = interp.run(lower_precision=LowerPrecision.FP32)
+        trt_mod = TRTModule(*r)
+        setattr(split_mod, name, trt_mod)
+
+lowered_model_output = split_mod(*inputs)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965
+I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029
+I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302
+I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192
+</pre></div></div>
+</div>
+<p>Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>torch.save(split_mod, &quot;trt.pt&quot;)
+reload_trt_mod = torch.load(&quot;trt.pt&quot;)
+reload_model_output = reload_trt_mod(*inputs)
+
+# Make sure the results match
+regular_model_output = model(*inputs)
+torch.testing.assert_close(
+    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2
+)
+</pre></div>
+</div>
+</div>
+
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diff --git a/docs/_notebooks/getting_started_with_fx_path_module.ipynb b/docs/_notebooks/getting_started_with_fx_path_module.ipynb
new file mode 100644
index 0000000000..f17ad14e03
--- /dev/null
+++ b/docs/_notebooks/getting_started_with_fx_path_module.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "code_folding": [],
+    "customInput": null,
+    "hidden_ranges": [],
+    "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
+    "showInput": false
+   },
+   "source": [
+    "The purpose of this example is to demonstrate the overall flow of lowering a PyTorch\n",
+    "model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like:\n",
+    "1. Use splitter to split the model if there're ops in the model that we don't want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance.\n",
+    "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path.\n",
+    "If we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367410991,
+    "executionStopTime": 1656367412604,
+    "originalKey": "ca68b029-68a6-42d6-968e-95bb7c1aae73",
+    "requestMsgId": "f56944ff-ade2-4041-bdd6-3bce44b1405f",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.fx\n",
+    "import torch.nn as nn\n",
+    "from torch_tensorrt.fx.utils import LowerPrecision\n",
+    "import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer\n",
+    "from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule\n",
+    "from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367414494,
+    "executionStopTime": 1656367422756,
+    "hidden_ranges": [],
+    "originalKey": "8f974ab2-d187-4ffe-a09b-16cd85949be4",
+    "requestMsgId": "564359f5-ac69-4666-91e1-41b299495ed1",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "class Model(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.linear = nn.Linear(10, 10)\n",
+    "        self.relu = nn.ReLU()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.linear(x)\n",
+    "        x = self.relu(x)\n",
+    "        x = torch.linalg.norm(x, ord=2, dim=1)\n",
+    "        x = self.relu(x)\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "inputs = [torch.randn((1, 10), device=torch.device('cuda'))]\n",
+    "model = Model().cuda().eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "code_folding": [],
+    "customInput": null,
+    "hidden_ranges": [],
+    "originalKey": "0d407e92-e9e7-48aa-9c9e-1c21a9b5fd8f",
+    "showInput": false
+   },
+   "source": [
+    "acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators\n",
+    "to acc ops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367480626,
+    "executionStopTime": 1656367482881,
+    "hidden_ranges": [],
+    "originalKey": "a1d9c8c2-8ec7-425a-8518-6f7e53ab1e67",
+    "requestMsgId": "ee2da608-5f1c-4f63-9927-544717e84e8a",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "traced = acc_tracer.trace(model, inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "code_folding": [],
+    "customInput": null,
+    "hidden_ranges": [],
+    "originalKey": "246613eb-14b5-488e-9aae-35306fc99db1",
+    "showInput": false
+   },
+   "source": [
+    "Splitter will split the model into several submodules. The name of submodules will\n",
+    "be either `run_on_acc_{}` or `run_on_gpu_{}`. Submodules named `run_on_acc_{}` can\n",
+    "be fully lowered to TensorRT via fx2trt while submodules named `run_on_gpu_{}` has\n",
+    "unsupported ops and can't be lowered by fx2trt. We can still run `run_on_gpu_{}`\n",
+    "submodules on GPU if ops there have cuda implementation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367487073,
+    "executionStopTime": 1656367487154,
+    "originalKey": "1103c70a-3766-4d89-ad2f-cdcb1c3891e0",
+    "requestMsgId": "feb888ea-ef9c-4577-b0c6-cf95bc1dd25e",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "splitter = TRTSplitter(traced, inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "code_folding": [],
+    "customInput": null,
+    "hidden_ranges": [],
+    "originalKey": "3d65e07e-57ed-47d5-adb9-4685c69c9c6b",
+    "showInput": false
+   },
+   "source": [
+    "Preview functionality allows us to see what are the supported ops and unsupported\n",
+    "ops. We can optionally the dot graph which will color supported ops and unsupported\n",
+    "ops differently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367489373,
+    "executionStopTime": 1656367489556,
+    "originalKey": "6aaed2d5-61b7-438e-a72a-63f91d0709e2",
+    "requestMsgId": "2948c2f8-854b-4bc2-b399-321469da320c",
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Supported node types in the model:\n",
+      "acc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\n",
+      "acc_ops.relu: ((), {'input': torch.float32})\n",
+      "\n",
+      "Unsupported node types in the model:\n",
+      "acc_ops.linalg_norm: ((), {'input': torch.float32})\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nSupported node types in the model:\\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\\nacc_ops.relu: ((), {'input': torch.float32})\\n\\nUnsupported node types in the model:\\nacc_ops.linalg_norm: ((), {'input': torch.float32})\\n\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "bento_obj_id": "139812830161136"
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "splitter.node_support_preview()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "customInput": null,
+    "originalKey": "8d8035ab-869e-4096-b8e1-3539a0cfe1af",
+    "showInput": false
+   },
+   "source": [
+    "After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367495077,
+    "executionStopTime": 1656367495250,
+    "hidden_ranges": [],
+    "originalKey": "80e03730-955a-4cc8-b071-7f92a2cff3df",
+    "requestMsgId": "2ca46574-7176-4699-a809-2a2e2d5ffda0",
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Got 2 acc subgraphs and 1 non-acc subgraphs\n",
+      "graph():\n",
+      "    %x : [#users=1] = placeholder[target=x]\n",
+      "    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})\n",
+      "    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})\n",
+      "    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})\n",
+      "    return _run_on_acc_2\n"
+     ]
+    }
+   ],
+   "source": [
+    "split_mod = splitter()\n",
+    "print(split_mod.graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367496353,
+    "executionStopTime": 1656367496452,
+    "originalKey": "9ce75161-978e-468e-9989-ecdbc9af0d5b",
+    "requestMsgId": "0370de27-39ec-4be0-826b-9aec90df1155",
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "graph():\n",
+      "    %x : [#users=1] = placeholder[target=x]\n",
+      "    %linear_weight : [#users=1] = get_attr[target=linear.weight]\n",
+      "    %linear_bias : [#users=1] = get_attr[target=linear.bias]\n",
+      "    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})\n",
+      "    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})\n",
+      "    return relu_2\n",
+      "graph():\n",
+      "    %relu_2 : [#users=1] = placeholder[target=relu_2]\n",
+      "    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})\n",
+      "    return linalg_norm_1\n",
+      "graph():\n",
+      "    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]\n",
+      "    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})\n",
+      "    return relu_3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(split_mod._run_on_acc_0.graph)\n",
+    "print(split_mod._run_on_gpu_1.graph)\n",
+    "print(split_mod._run_on_acc_2.graph)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "code_folding": [],
+    "customInput": null,
+    "hidden_ranges": [],
+    "originalKey": "7a6857bc-fedd-4847-ba17-a5d114de34f3",
+    "showInput": false
+   },
+   "source": [
+    "The `split_mod` contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367502837,
+    "executionStopTime": 1656367510024,
+    "hidden_ranges": [],
+    "originalKey": "174fd2eb-a864-49cf-a204-6d24a8e2849d",
+    "requestMsgId": "cf7fdfe4-e781-47c8-9a9a-85b5664c10f7",
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_submod_inputs(mod, submod, inputs):\n",
+    "    acc_inputs = None\n",
+    "\n",
+    "    def get_input(self, inputs):\n",
+    "        nonlocal acc_inputs\n",
+    "        acc_inputs = inputs\n",
+    "\n",
+    "    handle = submod.register_forward_pre_hook(get_input)\n",
+    "    mod(*inputs)\n",
+    "    handle.remove()\n",
+    "    return acc_inputs\n",
+    "\n",
+    "# Since the model is splitted into three segments. We need to lower each TRT eligible segment.\n",
+    "# If we know the model can be fully lowered, we can skip the splitter part.\n",
+    "for name, _ in split_mod.named_children():\n",
+    "    if \"_run_on_acc\" in name:\n",
+    "        submod = getattr(split_mod, name)\n",
+    "        # Get submodule inputs for fx2trt\n",
+    "        acc_inputs = get_submod_inputs(split_mod, submod, inputs)\n",
+    "\n",
+    "        # fx2trt replacement\n",
+    "        interp = TRTInterpreter(\n",
+    "            submod,\n",
+    "            InputTensorSpec.from_tensors(acc_inputs),\n",
+    "            explicit_batch_dimension=True,\n",
+    "        )\n",
+    "        r = interp.run(lower_precision=LowerPrecision.FP32)\n",
+    "        trt_mod = TRTModule(*r)\n",
+    "        setattr(split_mod, name, trt_mod)\n",
+    "\n",
+    "lowered_model_output = split_mod(*inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "code_folding": [],
+    "customInput": null,
+    "hidden_ranges": [],
+    "originalKey": "f1db3e1e-3a70-4735-a403-baa557b0f8a6",
+    "showInput": false
+   },
+   "source": [
+    "Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "customOutput": null,
+    "executionStartTime": 1656367515833,
+    "executionStopTime": 1656367516184,
+    "originalKey": "a7c4fa0f-cac6-4959-8fa6-13b3455137d3",
+    "requestMsgId": "f0c264ac-2bda-4c8e-a236-e2bd475e601e",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "torch.save(split_mod, \"trt.pt\")\n",
+    "reload_trt_mod = torch.load(\"trt.pt\")\n",
+    "reload_model_output = reload_trt_mod(*inputs)\n",
+    "\n",
+    "# Make sure the results match\n",
+    "regular_model_output = model(*inputs)\n",
+    "torch.testing.assert_close(\n",
+    "    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "bento_stylesheets": {
+   "bento/extensions/flow/main.css": true,
+   "bento/extensions/kernel_selector/main.css": true,
+   "bento/extensions/kernel_ui/main.css": true,
+   "bento/extensions/new_kernel/main.css": true,
+   "bento/extensions/system_usage/main.css": true,
+   "bento/extensions/theme/main.css": true
+  },
+  "dataExplorerConfig": {},
+  "kernelspec": {
+   "display_name": "accelerators",
+   "language": "python",
+   "metadata": {
+    "cinder_runtime": false,
+    "fbpkg_supported": true,
+    "is_prebuilt": true,
+    "kernel_name": "bento_kernel_accelerators",
+    "nightly_builds": true
+   },
+   "name": "bento_kernel_accelerators"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  },
+  "last_base_url": "https://devgpu005.ftw6.facebook.com:8093/",
+  "last_kernel_id": "a08a7dfc-0fcc-4486-a2d5-604483260888",
+  "last_msg_id": "3f4cd9a4-65001843cf56aec954e05889_80",
+  "last_server_session_id": "42b65868-6af0-4f04-bf2f-b7e2511f23dd",
+  "outputWidgetContext": {}
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_notebooks/lenet-getting-started.html b/docs/_notebooks/lenet-getting-started.html
index 27012a117b..16ce55d61d 100644
--- a/docs/_notebooks/lenet-getting-started.html
+++ b/docs/_notebooks/lenet-getting-started.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="e35ce4ca215c4513810fb9d533909cc9" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="2f280e0eb74d46ecafdf6432a54d6ea6" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT-Getting-Started---LeNet">
 <h1>Torch-TensorRT Getting Started - LeNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---LeNet" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -641,7 +641,9 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <li><p><a class="reference external" href="#2">Creating TorchScript modules</a></p></li>
 <li><p><a class="reference external" href="#3">Compiling with Torch-TensorRT</a></p></li>
 </ol>
-<p>## 1. Requirements</p>
+<blockquote>
+<div><p>## 1. Requirements</p>
+</div></blockquote>
 <p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this notebook.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
@@ -744,7 +746,9 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 +-----------------------------------------------------------------------------+
 </pre></div></div>
 </div>
-<p>## 2. Creating TorchScript modules</p>
+<blockquote>
+<div><p>## 2. Creating TorchScript modules</p>
+</div></blockquote>
 <p>Here we create two submodules for a feature extractor and a classifier and stitch them together in a single LeNet module. In this case this is overkill but modules give us granular control over our program including where we decide to optimize and where we don’t. It is also the unit that the TorchScript compiler operates on. So you can decide to only convert/optimize the feature extractor and leave the classifier in standard PyTorch or you can convert the whole thing. When compiling your module
 to TorchScript, there are two paths: Tracing and Scripting.</p>
 <div class="nbinput nblast docutils container">
@@ -1033,7 +1037,9 @@ <h3>Scripting<a class="headerlink" href="#Scripting" title="Permalink to this he
 Average batch time: 5.56 ms
 </pre></div></div>
 </div>
-<p>## 3. Compiling with Torch-TensorRT</p>
+<blockquote>
+<div><p>## 3. Compiling with Torch-TensorRT</p>
+</div></blockquote>
 </section>
 <section id="TorchScript-traced-model">
 <h3>TorchScript traced model<a class="headerlink" href="#TorchScript-traced-model" title="Permalink to this headline">¶</a></h3>
diff --git a/docs/_notebooks/ssd-object-detection-demo.html b/docs/_notebooks/ssd-object-detection-demo.html
index ad0fcb17bb..8e7ad225a9 100644
--- a/docs/_notebooks/ssd-object-detection-demo.html
+++ b/docs/_notebooks/ssd-object-detection-demo.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="aa77691fd7754170ac652a03d651d0b9" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="3185e4b1941b4216b8d84f22f38f5cf7" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Object-Detection-with-Torch-TensorRT-(SSD)">
 <h1>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#Object-Detection-with-Torch-TensorRT-(SSD)" title="Permalink to this headline">¶</a></h1>
 <hr class="docutils" />
@@ -647,7 +647,9 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 <li><p><a class="reference external" href="#7">Conclusion</a></p></li>
 </ol>
 <hr class="docutils" />
-<p>## 1. Requirements</p>
+<blockquote>
+<div><p>## 1. Requirements</p>
+</div></blockquote>
 <p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this demo notebook.</p>
 <p>In addition to that, run the following cell to obtain additional libraries specific to this demo.</p>
 <div class="nbinput docutils container">
@@ -765,7 +767,9 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<p>## 2. SSD</p>
+<blockquote>
+<div><p>## 2. SSD</p>
+</div></blockquote>
 <section id="Single-Shot-MultiBox-Detector-model-for-object-detection">
 <h3>Single Shot MultiBox Detector model for object detection<a class="headerlink" href="#Single-Shot-MultiBox-Detector-model-for-object-detection" title="Permalink to this headline">¶</a></h3>
 <table class="docutils align-default">
@@ -1078,7 +1082,9 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<p>## 3. Creating TorchScript modules</p>
+<blockquote>
+<div><p>## 3. Creating TorchScript modules</p>
+</div></blockquote>
 <p>To compile with Torch-TensorRT, the model must first be in <strong>TorchScript</strong>. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: <strong>Tracing</strong> and <strong>Scripting</strong>. - Tracing follows execution of PyTorch generating ops in TorchScript corresponding to
 what it sees. - Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do.</p>
 <p>Tracing however due to its simplicity is more likely to compile successfully with Torch-TensorRT (though both systems are supported).</p>
@@ -1134,7 +1140,9 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<p>## 4. Compiling with Torch-TensorRT TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.</p>
+<blockquote>
+<div><p>## 4. Compiling with Torch-TensorRT TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.</p>
+</div></blockquote>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
@@ -1173,7 +1181,9 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<p>## 5. Running Inference</p>
+<blockquote>
+<div><p>## 5. Running Inference</p>
+</div></blockquote>
 <p>Next, we run object detection</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
diff --git a/docs/_notebooks/vgg-qat.html b/docs/_notebooks/vgg-qat.html
index 6e29fe90a0..588e574d5c 100644
--- a/docs/_notebooks/vgg-qat.html
+++ b/docs/_notebooks/vgg-qat.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="ea58b17a1b2146f390fb14f89011e298" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="f4642ae2930e4f81bafc0d2cea4c2431" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT">
 <h1>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -635,7 +635,9 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 <li><p><a class="reference external" href="#8">Inference using Torch-TensorRT</a></p></li>
 <li><p><a class="reference external" href="#8">References</a></p></li>
 </ol>
-<p>## 1. Requirements Please install the required dependencies and import these libraries accordingly</p>
+<blockquote>
+<div><p>## 1. Requirements Please install the required dependencies and import these libraries accordingly</p>
+</div></blockquote>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
@@ -683,8 +685,12 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 2.1.0
 </pre></div></div>
 </div>
-<p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="0f5a0c4917974040a2d4b6035bb1c98a" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
-<p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
+<blockquote>
+<div><p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="67bfeaf143e44c928a91cc8c029d57f2" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
+</div></blockquote>
+<blockquote>
+<div><p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
+</div></blockquote>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -980,7 +986,9 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 Checkpoint saved
 </pre></div></div>
 </div>
-<p>## 4. Apply Quantization</p>
+<blockquote>
+<div><p>## 4. Apply Quantization</p>
+</div></blockquote>
 <p><code class="docutils literal notranslate"><span class="pre">quant_modules.initialize()</span></code> will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code>, <code class="docutils literal notranslate"><span class="pre">QuantLinear</span></code> and <code class="docutils literal notranslate"><span class="pre">QuantPooling</span></code> will be called. <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> basically wraps quantizer nodes around inputs and weights of regular <code class="docutils literal notranslate"><span class="pre">Conv2d</span></code>. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. A <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> is represented in
 <code class="docutils literal notranslate"><span class="pre">pytorch-quantization</span></code> toolkit as follows.</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def forward(self, input):
@@ -1038,7 +1046,9 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<p>## 5. Model Calibration</p>
+<blockquote>
+<div><p>## 5. Model Calibration</p>
+</div></blockquote>
 <p>The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration: <code class="docutils literal notranslate"><span class="pre">max</span></code>, <code class="docutils literal notranslate"><span class="pre">histogram</span></code> and <code class="docutils literal notranslate"><span class="pre">entropy</span></code>. We use <code class="docutils literal notranslate"><span class="pre">max</span></code> calibration technique as it is simple and effective.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -1266,7 +1276,9 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 classifier.6._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.3877, 0.5620](10) calibrator=MaxCalibrator scale=1.0 quant)
 </pre></div></div>
 </div>
-<p>## 6. Quantization Aware Training</p>
+<blockquote>
+<div><p>## 6. Quantization Aware Training</p>
+</div></blockquote>
 <p>In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an
 annealing learning-rate. Please refer to Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. During finetuning with QAT, the quantization is applied as a composition of <code class="docutils literal notranslate"><span class="pre">max</span></code>, <code class="docutils literal notranslate"><span class="pre">clamp</span></code>, <code class="docutils literal notranslate"><span class="pre">round</span></code> and <code class="docutils literal notranslate"><span class="pre">mul</span></code> ops.</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span># amax is absolute maximum value for an input
@@ -1316,8 +1328,10 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 Checkpoint saved
 </pre></div></div>
 </div>
-<p>## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">https://pytorch.org/docs/stable/jit.html</a>. Setting <code class="docutils literal notranslate"><span class="pre">quant_nn.TensorQuantizer.use_fb_fake_quant</span> <span class="pre">=</span> <span class="pre">True</span></code> enables the QAT model to use <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_channel_affine</span></code> operators instead of <code class="docutils literal notranslate"><span class="pre">tensor_quant</span></code> function to export quantization operators. In torchscript, they
-are represented as <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_channel_affine</span></code>.</p>
+<blockquote>
+<div><p>## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">https://pytorch.org/docs/stable/jit.html</a>. Setting <code class="docutils literal notranslate"><span class="pre">quant_nn.TensorQuantizer.use_fb_fake_quant</span> <span class="pre">=</span> <span class="pre">True</span></code> enables the QAT model to use <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_channel_affine</span></code> operators instead of <code class="docutils literal notranslate"><span class="pre">tensor_quant</span></code> function to export quantization operators. In torchscript, they</p>
+</div></blockquote>
+<p>are represented as <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_channel_affine</span></code>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
@@ -1475,8 +1489,10 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 E1109 04:02:37.488986 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
 </pre></div></div>
 </div>
-<p>## 8. Inference using Torch-TensorRT In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; <code class="docutils literal notranslate"><span class="pre">QuantizeLayer</span></code> and <code class="docutils literal notranslate"><span class="pre">DequantizeLayer</span></code>. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set
-<code class="docutils literal notranslate"><span class="pre">torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)</span></code>. For example, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> layer from <code class="docutils literal notranslate"><span class="pre">pytorch_quantization</span></code> toolkit is represented as follows in Torchscript</p>
+<blockquote>
+<div><p>## 8. Inference using Torch-TensorRT In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; <code class="docutils literal notranslate"><span class="pre">QuantizeLayer</span></code> and <code class="docutils literal notranslate"><span class="pre">DequantizeLayer</span></code>. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set</p>
+</div></blockquote>
+<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)</span></code>. For example, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> layer from <code class="docutils literal notranslate"><span class="pre">pytorch_quantization</span></code> toolkit is represented as follows in Torchscript</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)
 %quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)
 %input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)
@@ -1623,7 +1639,9 @@ <h3>Performance benchmarking<a class="headerlink" href="#Performance-benchmarkin
 Average batch time: 1.79 ms
 </pre></div></div>
 </div>
-<p>## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using Torch-TensorRT * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics</p>
+<blockquote>
+<div><p>## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using Torch-TensorRT * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics</p>
+</div></blockquote>
 </section>
 </section>
 </section>
diff --git a/docs/_sources/_notebooks/getting_started_with_fx_path_module.ipynb.txt b/docs/_sources/_notebooks/getting_started_with_fx_path_module.ipynb.txt
new file mode 100644
index 0000000000..5ee8884790
--- /dev/null
+++ b/docs/_sources/_notebooks/getting_started_with_fx_path_module.ipynb.txt
@@ -0,0 +1,441 @@
+{
+  "metadata": {
+    "dataExplorerConfig": {},
+    "bento_stylesheets": {
+      "bento/extensions/flow/main.css": true,
+      "bento/extensions/kernel_selector/main.css": true,
+      "bento/extensions/kernel_ui/main.css": true,
+      "bento/extensions/new_kernel/main.css": true,
+      "bento/extensions/system_usage/main.css": true,
+      "bento/extensions/theme/main.css": true
+    },
+    "kernelspec": {
+      "display_name": "accelerators",
+      "language": "python",
+      "name": "bento_kernel_accelerators",
+      "metadata": {
+        "kernel_name": "bento_kernel_accelerators",
+        "nightly_builds": true,
+        "fbpkg_supported": true,
+        "cinder_runtime": false,
+        "is_prebuilt": true
+      }
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3"
+    },
+    "last_server_session_id": "42b65868-6af0-4f04-bf2f-b7e2511f23dd",
+    "last_kernel_id": "a08a7dfc-0fcc-4486-a2d5-604483260888",
+    "last_base_url": "https://devgpu005.ftw6.facebook.com:8093/",
+    "last_msg_id": "3f4cd9a4-65001843cf56aec954e05889_80",
+    "outputWidgetContext": {}
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "The purpose of this example is to demonstrate the overall flow of lowering a PyTorch\n",
+        "model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like:\n",
+        "1. Use splitter to split the model if there're ops in the model that we don't want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance.\n",
+        "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path.\n",
+        "If we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "ca68b029-68a6-42d6-968e-95bb7c1aae73",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "f56944ff-ade2-4041-bdd6-3bce44b1405f",
+        "customOutput": null,
+        "executionStartTime": 1656367410991,
+        "executionStopTime": 1656367412604
+      },
+      "source": [
+        "import torch\n",
+        "import torch.fx\n",
+        "import torch.nn as nn\n",
+        "from torch_tensorrt.fx.utils import LowerPrecision\n",
+        "import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer\n",
+        "from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule\n",
+        "from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "8f974ab2-d187-4ffe-a09b-16cd85949be4",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "564359f5-ac69-4666-91e1-41b299495ed1",
+        "customOutput": null,
+        "executionStartTime": 1656367414494,
+        "executionStopTime": 1656367422756
+      },
+      "source": [
+        "class Model(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.linear = nn.Linear(10, 10)\n",
+        "        self.relu = nn.ReLU()\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.linear(x)\n",
+        "        x = self.relu(x)\n",
+        "        x = torch.linalg.norm(x, ord=2, dim=1)\n",
+        "        x = self.relu(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "inputs = [torch.randn((1, 10), device=torch.device('cuda'))]\n",
+        "model = Model().cuda().eval()"
+      ],
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "0d407e92-e9e7-48aa-9c9e-1c21a9b5fd8f",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators\n",
+        "to acc ops."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "a1d9c8c2-8ec7-425a-8518-6f7e53ab1e67",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "ee2da608-5f1c-4f63-9927-544717e84e8a",
+        "customOutput": null,
+        "executionStartTime": 1656367480626,
+        "executionStopTime": 1656367482881
+      },
+      "source": [
+        "traced = acc_tracer.trace(model, inputs)"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "246613eb-14b5-488e-9aae-35306fc99db1",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Splitter will split the model into several submodules. The name of submodules will\n",
+        "be either `run_on_acc_{}` or `run_on_gpu_{}`. Submodules named `run_on_acc_{}` can\n",
+        "be fully lowered to TensorRT via fx2trt while submodules named `run_on_gpu_{}` has\n",
+        "unsupported ops and can't be lowered by fx2trt. We can still run `run_on_gpu_{}`\n",
+        "submodules on GPU if ops there have cuda implementation.\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "1103c70a-3766-4d89-ad2f-cdcb1c3891e0",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "feb888ea-ef9c-4577-b0c6-cf95bc1dd25e",
+        "customOutput": null,
+        "executionStartTime": 1656367487073,
+        "executionStopTime": 1656367487154
+      },
+      "source": [
+        "splitter = TRTSplitter(traced, inputs)"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "3d65e07e-57ed-47d5-adb9-4685c69c9c6b",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Preview functionality allows us to see what are the supported ops and unsupported\n",
+        "ops. We can optionally the dot graph which will color supported ops and unsupported\n",
+        "ops differently."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "6aaed2d5-61b7-438e-a72a-63f91d0709e2",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "2948c2f8-854b-4bc2-b399-321469da320c",
+        "customOutput": null,
+        "executionStartTime": 1656367489373,
+        "executionStopTime": 1656367489556
+      },
+      "source": [
+        "splitter.node_support_preview()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\nSupported node types in the model:\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\nacc_ops.relu: ((), {'input': torch.float32})\n\nUnsupported node types in the model:\nacc_ops.linalg_norm: ((), {'input': torch.float32})\n\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "\"\\nSupported node types in the model:\\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\\nacc_ops.relu: ((), {'input': torch.float32})\\n\\nUnsupported node types in the model:\\nacc_ops.linalg_norm: ((), {'input': torch.float32})\\n\""
+          },
+          "metadata": {
+            "bento_obj_id": "139812830161136"
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "8d8035ab-869e-4096-b8e1-3539a0cfe1af",
+        "showInput": false,
+        "customInput": null
+      },
+      "source": [
+        "After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "80e03730-955a-4cc8-b071-7f92a2cff3df",
+        "showInput": true,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": [],
+        "collapsed": false,
+        "requestMsgId": "2ca46574-7176-4699-a809-2a2e2d5ffda0",
+        "customOutput": null,
+        "executionStartTime": 1656367495077,
+        "executionStopTime": 1656367495250
+      },
+      "source": [
+        "split_mod = splitter()\n",
+        "print(split_mod.graph)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Got 2 acc subgraphs and 1 non-acc subgraphs\ngraph():\n    %x : [#users=1] = placeholder[target=x]\n    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})\n    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})\n    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})\n    return _run_on_acc_2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "9ce75161-978e-468e-9989-ecdbc9af0d5b",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "0370de27-39ec-4be0-826b-9aec90df1155",
+        "customOutput": null,
+        "executionStartTime": 1656367496353,
+        "executionStopTime": 1656367496452
+      },
+      "source": [
+        "print(split_mod._run_on_acc_0.graph)\n",
+        "print(split_mod._run_on_gpu_1.graph)\n",
+        "print(split_mod._run_on_acc_2.graph)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "graph():\n    %x : [#users=1] = placeholder[target=x]\n    %linear_weight : [#users=1] = get_attr[target=linear.weight]\n    %linear_bias : [#users=1] = get_attr[target=linear.bias]\n    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})\n    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})\n    return relu_2\ngraph():\n    %relu_2 : [#users=1] = placeholder[target=relu_2]\n    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})\n    return linalg_norm_1\ngraph():\n    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]\n    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})\n    return relu_3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "7a6857bc-fedd-4847-ba17-a5d114de34f3",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "The `split_mod` contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "174fd2eb-a864-49cf-a204-6d24a8e2849d",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "cf7fdfe4-e781-47c8-9a9a-85b5664c10f7",
+        "customOutput": null,
+        "executionStartTime": 1656367502837,
+        "executionStopTime": 1656367510024,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "def get_submod_inputs(mod, submod, inputs):\n",
+        "    acc_inputs = None\n",
+        "\n",
+        "    def get_input(self, inputs):\n",
+        "        nonlocal acc_inputs\n",
+        "        acc_inputs = inputs\n",
+        "\n",
+        "    handle = submod.register_forward_pre_hook(get_input)\n",
+        "    mod(*inputs)\n",
+        "    handle.remove()\n",
+        "    return acc_inputs\n",
+        "\n",
+        "# Since the model is splitted into three segments. We need to lower each TRT eligible segment.\n",
+        "# If we know the model can be fully lowered, we can skip the splitter part.\n",
+        "for name, _ in split_mod.named_children():\n",
+        "    if \"_run_on_acc\" in name:\n",
+        "        submod = getattr(split_mod, name)\n",
+        "        # Get submodule inputs for fx2trt\n",
+        "        acc_inputs = get_submod_inputs(split_mod, submod, inputs)\n",
+        "\n",
+        "        # fx2trt replacement\n",
+        "        interp = TRTInterpreter(\n",
+        "            submod,\n",
+        "            InputTensorSpec.from_tensors(acc_inputs),\n",
+        "            explicit_batch_dimension=True,\n",
+        "        )\n",
+        "        r = interp.run(lower_precision=LowerPrecision.FP32)\n",
+        "        trt_mod = TRTModule(*r)\n",
+        "        setattr(split_mod, name, trt_mod)\n",
+        "\n",
+        "lowered_model_output = split_mod(*inputs)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "originalKey": "f1db3e1e-3a70-4735-a403-baa557b0f8a6",
+        "showInput": false,
+        "customInput": null,
+        "code_folding": [],
+        "hidden_ranges": []
+      },
+      "source": [
+        "Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "originalKey": "a7c4fa0f-cac6-4959-8fa6-13b3455137d3",
+        "showInput": true,
+        "customInput": null,
+        "collapsed": false,
+        "requestMsgId": "f0c264ac-2bda-4c8e-a236-e2bd475e601e",
+        "customOutput": null,
+        "executionStartTime": 1656367515833,
+        "executionStopTime": 1656367516184
+      },
+      "source": [
+        "torch.save(split_mod, \"trt.pt\")\n",
+        "reload_trt_mod = torch.load(\"trt.pt\")\n",
+        "reload_model_output = reload_trt_mod(*inputs)\n",
+        "\n",
+        "# Make sure the results match\n",
+        "regular_model_output = model(*inputs)\n",
+        "torch.testing.assert_close(\n",
+        "    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2\n",
+        ")"
+      ],
+      "execution_count": 9,
+      "outputs": []
+    }
+  ]
+}
diff --git a/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt b/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
new file mode 100644
index 0000000000..9f15616e28
--- /dev/null
+++ b/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
@@ -0,0 +1,304 @@
+.. user_guide:
+Torch-TensorRT (FX Path) User Guide 
+========================
+Torch-TensorRT (FX Path) is a tool that can convert a PyTorch model through torch.FX to an TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine developed by Nvidia which composed of various kinds of optimization including kernel fusion, graph optimization, low precision, etc..
+This tool is developed in Python environment providing most usability to researchers and engineers. There are a few stages that a user want to use this tool and we will introduce them here. 
+ 
+Installation
+------------
+* Method 1. Follow the instrucions for Torch-TensorRT
+* Method 2. To install FX path only (Python path) and avoid the C++ build for torchscript path
+
+.. code-block:: shell
+
+    $ conda create --name python_env python=3.8
+    $ conda activate python_env
+
+    # Recommend to install PyTorch 1.12 and later
+    $ conda install pytorch torchvision torchtext cudatoolkit=11.3 -c pytorch-nightly
+
+    # Install TensorRT python package
+    $ pip3 install nvidia-pyindex
+    $ pip3 install nvidia-tensorrt==8.2.4.2
+    $ git clone https://github.com/pytorch/TensorRT.git
+    $ cd TensorRT/py && python setup.py install --fx-only && cd ..
+
+    $ pyton -c "import torch_tensorrt.fx"
+    # Test an example by 
+    $ python py/torch_tensorrt/fx/example/lower_example.py
+ 
+
+Converting a PyTorch Model to TensorRT Engine
+---------------------------------------------
+We will go through an example to illustrate the major steps that FX path uses to 
+
+* **Step 1: Trace the model with acc_tracer**
+Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
+
+.. code-block:: shell
+
+   import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+
+   # Build the model which needs to be a PyTorch nn.Module. 
+   my_pytorch_model = build_model()
+
+   # Prepare inputs to the model. Inputs have to be a List of Tensors
+   inputs = [Tensor, Tensor, ...]
+
+   # Trace the model with acc_tracer.
+   acc_mod = acc_tracer.trace(my_pytorch_model, inputs)
+
+*Common Errors:*
+  
+symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in `FX guide <https://pytorch.org/docs/stable/fx.html#dynamic-control-flow>`_.
+
+* **Step 2: Build TensorRT engine**
+There are `two different modes <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch>`_ for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set ``explicit_batch_dimension=False`` in ``lower_to_trt()``. We do not recommend to use it since it will lack of support in future TensorRT versions. 
+
+Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable ``dynamic_batch`` in ``lower_to_trt()`` if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).
+
+For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range. 
+
+.. code-block:: shell
+
+    import deeplearning.trt.fx2trt.converter.converters
+    from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
+
+    # InputTensorSpec is a dataclass we use to store input information.
+    # There're two ways we can build input_specs.
+    # Option 1, build it manually.
+    input_specs = [
+      InputTensorSpec(shape=(1, 2, 3), dtype=torch.float32),
+      InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+    # Option 2, build it using sample_inputs where user provide a sample
+    inputs = [
+    torch.rand((1,2,3), dtype=torch.float32),
+    torch.rand((1,4,5), dtype=torch.float32),
+    ]
+    input_specs = InputTensorSpec.from_tensors(inputs)
+
+    # IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.
+    input_specs = [
+        InputTensorSpec(
+            shape=(-1, 2, 3), 
+            dtype=torch.float32,
+            # Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models
+            # (min_shape, optimize_target_shape, max_shape)
+            # For more information refer to fx/input_tensor_spec.py
+            shape_ranges = [
+                ((1, 2, 3), (4, 2, 3), (100, 2, 3)),
+            ],
+        ),
+        InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+
+    # Build a TRT interpreter. Set explicit_batch_dimension accordingly.
+    interpreter = TRTInterpreter(
+        acc_mod, input_specs, explicit_batch_dimension=True/False
+    )
+
+    # The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.
+    # The TRTInterpreterResult contains required parameter to build TRTModule, 
+    # and other informational output from TRTInterpreter run.
+    class TRTInterpreterResult(NamedTuple):
+        engine: Any
+        input_names: Sequence[str]
+        output_names: Sequence[str]
+        serialized_cache: bytearray
+
+    #max_batch_size: set accordingly for maximum batch size you will use.
+    #max_workspace_size: set to the maximum size we can afford for temporary buffer
+    #lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).
+    #sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity
+    #force_fp32_output: force output to be fp32
+    #strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.
+    #algorithm_selector: set up algorithm selection for certain layer
+    #timing_cache: enable timing cache for TensorRT
+    #profiling_verbosity: TensorRT logging level
+    trt_interpreter_result = interpreter.run(
+        max_batch_size=64,
+        max_workspace_size=1 << 25,     
+        sparse_weights=False,
+        force_fp32_output=False,
+        strict_type_constraints=False,
+        algorithm_selector=None,
+        timing_cache=None,
+        profiling_verbosity=None,
+    )
+
+
+*Common Errors:*
+
+RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.
+
+* **Step 3: Run the model**
+One way is using TRTModule, which is basically a PyTorch nn.Module.
+
+.. code-block:: shell
+
+    from torch_tensorrt.fx import TRTModule
+    mod = TRTModule(
+        trt_interpreter_result.engine, 
+        trt_interpreter_result.input_names, 
+        trt_interpreter_result.output_names)
+    # Just like all other PyTorch modules
+    outputs = mod(*inputs)
+    torch.save(mod, "trt.pt")
+    reload_trt_mod = torch.load("trt.pt")
+    reload_model_output = reload_trt_mod(*inputs)
+
+So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph's operation to corresponding TensorRT operation and build up the TensoRT engine for it. 
+
+Acc Tracer
+---------
+
+Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:
+
+1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write. 
+2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
+
+FX2TRT
+--------
+After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
+
+Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+        
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+
+How to Add a Missing Op
+****************
+
+You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer. 
+
+* **Step 1. Add a new acc op**
+
+TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.
+
+In `acc tracer <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py>`_, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.  
+
+In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.
+
+Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py>`_. e.g. the following code adds an acc op named foo() which adds two given inputs.
+
+.. code-block:: shell
+
+    # NOTE: all acc ops should only take kwargs as inputs, therefore we need the "*"
+    # at the beginning.
+    @register_acc_op
+    def foo(*, input, other, alpha):
+        return input + alpha * other
+ 
+There’re two ways to register a mapping. One is `register_acc_op_mapping() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164>`_. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.
+ 
+.. code-block:: shell
+
+    this_arg_is_optional = True
+
+    @register_acc_op_mapping(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other    
+ 
+``op_and_target`` determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the ``op_and_target``, we will trigger the mapping. Since we want to map from ``torch.add``, then op would be call_function and target would be ``torch.add``. ``arg_replacement_tuples`` determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in ``arg_replacement_tuples`` represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in *original node*. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.
+
+1. kwargs of original nodes and acc op nodes have different name.
+2. there’re optional arguments.
+
+The other way to register a mapping is through `register_custom_acc_mapper_fn() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206>`_. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.
+
+.. code-block:: shell
+
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other
+
+    @register_custom_acc_mapper_fn(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    def custom_mapper(node: torch.fx.Node, _: nn.Module) -> torch.fx.Node:
+        """
+        `node` is original node, which is a call_function node with target
+        being torch.add.
+        """
+        alpha = 1
+        if "alpha" in node.kwargs:
+            alpha = node.kwargs["alpha"]
+        foo_kwargs = {"input": node["input"], "other": node["other"], "alpha": alpha}
+        with node.graph.inserting_before(node):
+            foo_node = node.graph.call_function(foo, kwargs=foo_kwargs)
+            foo_node.meta = node.meta.copy()
+            return foo_node
+ 
+
+In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379>`_.
+
+The last step would be *adding unit test* for the new acc op or mapper function we added. The place to add the unit test is here `test_acc_tracer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py>`_.
+
+* **Step 2. Add a new fx2trt converter**
+
+All the developed converters for acc ops are all in `acc_op_converter.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py>`_. It could give you a good example of how the converter is added. 
+
+Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using `TensorRT APIs <https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html>`_.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+        
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+ 
+We need to use ``tensorrt_converter`` decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is `acc_ops.sigmoid` which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.
+
+**What if we can not find corresponding layers in TensorRT that do the same thing as the node.**
+
+In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. *We have not implement this feature yet. We will update once it is enabled*.
+
+Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this `folder <https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op>`_. 
diff --git a/docs/contributors/conversion.html b/docs/contributors/conversion.html
index 79e58ac7ea..22e91ace05 100644
--- a/docs/contributors/conversion.html
+++ b/docs/contributors/conversion.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/contributors/lowering.html b/docs/contributors/lowering.html
index 2fab6272d7..b21b1a64ea 100644
--- a/docs/contributors/lowering.html
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diff --git a/docs/contributors/partitioning.html b/docs/contributors/partitioning.html
index bc6779ca03..2e6d5dc08d 100644
--- a/docs/contributors/partitioning.html
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@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/contributors/phases.html b/docs/contributors/phases.html
index e669062975..df77dfdd5b 100644
--- a/docs/contributors/phases.html
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@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/contributors/runtime.html b/docs/contributors/runtime.html
index 4e08b66384..20019af73d 100644
--- a/docs/contributors/runtime.html
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@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/contributors/system_overview.html b/docs/contributors/system_overview.html
index 32ecb37e00..e103093f2a 100644
--- a/docs/contributors/system_overview.html
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@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/contributors/useful_links.html b/docs/contributors/useful_links.html
index 9d4dad487e..6ca5012842 100644
--- a/docs/contributors/useful_links.html
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@@ -199,7 +199,7 @@
               
               
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diff --git a/docs/contributors/writing_converters.html b/docs/contributors/writing_converters.html
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diff --git a/docs/genindex.html b/docs/genindex.html
index bc175152ae..7428831f21 100644
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@@ -196,7 +196,7 @@
               
               
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@@ -348,16 +348,7 @@
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- | <a href="#A"><strong>A</strong></a>
- | <a href="#C"><strong>C</strong></a>
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- | <a href="#E"><strong>E</strong></a>
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- | <a href="#I"><strong>I</strong></a>
- | <a href="#L"><strong>L</strong></a>
- | <a href="#M"><strong>M</strong></a>
+ <a href="#D"><strong>D</strong></a>
  | <a href="#P"><strong>P</strong></a>
  | <a href="#R"><strong>R</strong></a>
  | <a href="#S"><strong>S</strong></a>
@@ -366,197 +357,14 @@ <h1 id="index">Index</h1>
  | <a href="#X"><strong>X</strong></a>
  
 </div>
-<h2 id="_">_</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.__init__">__init__() (torch_tensorrt.Device method)</a>
-
-      <ul>
-        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">(torch_tensorrt.Input method)</a>
-</li>
-        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">(torch_tensorrt.ptq.CacheCalibrator method)</a>
-</li>
-        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">(torch_tensorrt.ptq.DataLoaderCalibrator method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
-<h2 id="A">A</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.allow_gpu_fallback">allow_gpu_fallback (torch_tensorrt.Device attribute)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#id1">CacheCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[1]</a>
-</li>
-      <li><a href="py_api/ptq.html#id2">CalibrationAlgo (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[1]</a>
-</li>
-      <li><a href="py_api/ts.html#torch_tensorrt.ts.check_method_op_support">check_method_op_support() (in module torch_tensorrt.ts)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.compile">compile() (in module torch_tensorrt)</a>
-
-      <ul>
-        <li><a href="py_api/ts.html#torch_tensorrt.ts.compile">(in module torch_tensorrt.ts)</a>
-</li>
-      </ul></li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">convert_method_to_trt_engine() (in module torch_tensorrt)</a>
-
-      <ul>
-        <li><a href="py_api/ts.html#torch_tensorrt.ts.convert_method_to_trt_engine">(in module torch_tensorrt.ts)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
 <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#id0">DataLoaderCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[1]</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.debug">debug (class in torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Debug">Debug (torch_tensorrt.logging.Level attribute)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device">Device (class in torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.device_type">device_type (torch_tensorrt.Device attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.DeviceType">DeviceType (class in torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.dla_core">dla_core (torch_tensorrt.Device attribute)</a>
-</li>
       <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Documentation"><strong>Documentation</strong></a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dtype">dtype (class in torch_tensorrt)</a>
-
-      <ul>
-        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.dtype">(torch_tensorrt.Input attribute)</a>
-</li>
-      </ul></li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dump_build_info">dump_build_info() (in module torch_tensorrt)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ts.html#torch_tensorrt.ts.embed_engine_in_new_module">embed_engine_in_new_module() (in module torch_tensorrt.ts)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.EngineCapability">EngineCapability (class in torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">ENTROPY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">ENTROPY_CALIBRATION_2 (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Error">Error (torch_tensorrt.logging.Level attribute)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.errors">errors (class in torch_tensorrt.logging)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="F">F</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.format">format (torch_tensorrt.Input attribute)</a>
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   </ul></td>
 </tr></table>
 
-<h2 id="G">G</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_batch">get_batch() (in module torch_tensorrt.ptq)</a>
-</li>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_batch_size">get_batch_size() (in module torch_tensorrt.ptq)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.get_build_info">get_build_info() (in module torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">get_cache_mode_batch() (in module torch_tensorrt.ptq)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_is_colored_output_on">get_is_colored_output_on() (in module torch_tensorrt.logging)</a>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_logging_prefix">get_logging_prefix() (in module torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_reportable_log_level">get_reportable_log_level() (in module torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.gpu_id">gpu_id (torch_tensorrt.Device attribute)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Graph">Graph (torch_tensorrt.logging.Level attribute)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.graphs">graphs (class in torch_tensorrt.logging)</a>
-</li>
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-<h2 id="I">I</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.info">info (class in torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Info">Info (torch_tensorrt.logging.Level attribute)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input">Input (class in torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.internal_errors">internal_errors (class in torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.InternalError">InternalError (torch_tensorrt.logging.Level attribute)</a>
-</li>
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-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-</li>
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-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level">Level (class in torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.log">log() (in module torch_tensorrt.logging)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">MINMAX_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
-</li>
-      <li>
-    module
-
-      <ul>
-        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">torch_tensorrt</a>
-</li>
-        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">torch_tensorrt.logging</a>
-</li>
-        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">torch_tensorrt.ptq</a>
-</li>
-        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">torch_tensorrt.ts</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -573,8 +381,6 @@ <h2 id="P">P</h2>
 <h2 id="R">R</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.read_calibration_cache">read_calibration_cache() (in module torch_tensorrt.ptq)</a>
-</li>
       <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Reading"><strong>Reading</strong></a>
 </li>
   </ul></td>
@@ -592,20 +398,6 @@ <h2 id="R">R</h2>
 <h2 id="S">S</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.set_device">set_device() (in module torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_is_colored_output_on">set_is_colored_output_on() (in module torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_logging_prefix">set_logging_prefix() (in module torch_tensorrt.logging)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_reportable_log_level">set_reportable_log_level() (in module torch_tensorrt.logging)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.shape">shape (torch_tensorrt.Input attribute)</a>
-</li>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.shape_mode">shape_mode (torch_tensorrt.Input attribute)</a>
-</li>
       <li><a href="_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#c.STR">STR (C macro)</a>
 </li>
   </ul></td>
@@ -614,38 +406,6 @@ <h2 id="S">S</h2>
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.TensorFormat">TensorFormat (class in torch_tensorrt)</a>
-</li>
-      <li><a href="py_api/ts.html#torch_tensorrt.ts.TensorRTCompileSpec">TensorRTCompileSpec() (in module torch_tensorrt.ts)</a>
-</li>
-      <li>
-    torch_tensorrt
-
-      <ul>
-        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">module</a>
-</li>
-      </ul></li>
-      <li>
-    torch_tensorrt.logging
-
-      <ul>
-        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">module</a>
-</li>
-      </ul></li>
-      <li>
-    torch_tensorrt.ptq
-
-      <ul>
-        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">module</a>
-</li>
-      </ul></li>
-      <li>
-    torch_tensorrt.ts
-
-      <ul>
-        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">module</a>
-</li>
-      </ul></li>
       <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE">torch_tensorrt::DataType (C++ class)</a>
 </li>
       <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value">torch_tensorrt::DataType::DataType (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeEv">[2]</a>
@@ -748,8 +508,6 @@ <h2 id="T">T</h2>
 </li>
       <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE">torch_tensorrt::logging::Level::kINTERNAL_ERROR (C++ enumerator)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE">torch_tensorrt::logging::Level::kWARNING (C++ enumerator)</a>
 </li>
       <li><a href="_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE">torch_tensorrt::logging::log (C++ function)</a>
@@ -760,6 +518,8 @@ <h2 id="T">T</h2>
 </li>
       <li><a href="_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level">torch_tensorrt::logging::set_reportable_log_level (C++ function)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE">torch_tensorrt::ptq::Int8CacheCalibrator (C++ class)</a>
 </li>
       <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci">torch_tensorrt::ptq::Int8CacheCalibrator::getBatch (C++ function)</a>
@@ -878,14 +638,6 @@ <h2 id="T">T</h2>
 <h2 id="W">W</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Warning">Warning (torch_tensorrt.logging.Level attribute)</a>
-</li>
-      <li><a href="py_api/logging.html#torch_tensorrt.logging.warnings">warnings (class in torch_tensorrt.logging)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.write_calibration_cache">write_calibration_cache() (in module torch_tensorrt.ptq)</a>
-</li>
       <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Writing"><strong>Writing</strong></a>
 </li>
   </ul></td>
diff --git a/docs/index.html b/docs/index.html
index 3543a93f12..4ab408acff 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -198,7 +198,7 @@
               
               
                 <div class="version">
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+                  master (1.2.0a0+3a8704db)
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diff --git a/docs/indices/supported_ops.html b/docs/indices/supported_ops.html
index 43b24f615e..0e995072a6 100644
--- a/docs/indices/supported_ops.html
+++ b/docs/indices/supported_ops.html
@@ -198,7 +198,7 @@
               
               
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diff --git a/docs/py_api/logging.html b/docs/py_api/logging.html
index acb919ee82..dd0441ea8d 100644
--- a/docs/py_api/logging.html
+++ b/docs/py_api/logging.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -353,207 +353,6 @@
               
   <section id="torch-tensorrt-logging">
 <span id="torch-tensorrt-logging-py"></span><h1>torch_tensorrt.logging<a class="headerlink" href="#torch-tensorrt-logging" title="Permalink to this headline">¶</a></h1>
-<span class="target" id="module-torch_tensorrt.logging"></span><dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">Level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#Level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.Level" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
-<p>Enum to set the minimum required logging level to print a message to stdout</p>
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Debug">
-<span class="sig-name descname"><span class="pre">Debug</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.DEBUG:</span> <span class="pre">4&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Debug" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Error">
-<span class="sig-name descname"><span class="pre">Error</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.ERROR:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Error" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Graph">
-<span class="sig-name descname"><span class="pre">Graph</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.GRAPH:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Graph" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Info">
-<span class="sig-name descname"><span class="pre">Info</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INFO:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Info" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.InternalError">
-<span class="sig-name descname"><span class="pre">InternalError</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INTERNAL_ERROR:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.InternalError" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Warning">
-<span class="sig-name descname"><span class="pre">Warning</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.WARNING:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Warning" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.debug">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">debug</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#debug"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.debug" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Context-manager to display full debug information through the logger</p>
-<p>Example:</p>
-<dl class="simple">
-<dt>with torch_tensorrt.logging.debug():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id1"><span class="problematic" id="id2">**</span></a>spec)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.errors">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.errors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Context-manager to limit displayed log messages to just errors and above</p>
-<p>Example:</p>
-<dl class="simple">
-<dt>with torch_tensorrt.logging.errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.get_is_colored_output_on">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_is_colored_output_on</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get if colored output is enabled for logging</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>If colored output is one</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>bool</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.get_logging_prefix">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_logging_prefix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_logging_prefix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the prefix set for logging messages</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>Prefix used for logger</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>str</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.get_reportable_log_level">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_reportable_log_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_reportable_log_level" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the level required for a message to be printed in the log</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>The enum representing the level required to print</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level">torch_tensorrt.logging.Level</a></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.graphs">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">graphs</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#graphs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.graphs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Context-manager to display the results of intermediate lowering passes
-as well as full debug information through the logger</p>
-<p>Example:</p>
-<dl class="simple">
-<dt>with torch_tensorrt.logging.graphs():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id3"><span class="problematic" id="id4">**</span></a>spec)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.info">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">info</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.info" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Context-manager to display all info and greater severity messages</p>
-<p>Example:</p>
-<dl class="simple">
-<dt>with torch_tensorrt.logging.info():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id5"><span class="problematic" id="id6">**</span></a>spec)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.internal_errors">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">internal_errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#internal_errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.internal_errors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Context-manager to limit displayed log messages to just internal errors</p>
-<p>Example:</p>
-<dl class="simple">
-<dt>with torch_tensorrt.logging.internal_errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.log">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.log" title="Permalink to this definition">¶</a></dt>
-<dd><p>Add a new message to the log</p>
-<p>Adds a new message to the log at a specified level. The message
-will only get printed out if Level &gt; reportable_log_level</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>torch_tensorrt.logging.Level</em></a>) – Severity of the message</p></li>
-<li><p><strong>msg</strong> (<em>str</em>) – Actual message text</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.set_is_colored_output_on">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_is_colored_output_on</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">colored_output_on</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
-<dd><p>Enable or disable color in the log output</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>colored_output_on</strong> (<em>bool</em>) – If colored output should be enabled or not</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.set_logging_prefix">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_logging_prefix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_logging_prefix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the prefix used when logging messages</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – Prefix to use for logging messages</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.set_reportable_log_level">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_reportable_log_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_reportable_log_level" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set the level required for a message to be printed to the log</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>torch_tensorrt.logging.Level</em></a>) – The enum representing the level required to print</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.logging.warnings">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">warnings</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.warnings" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Context-manager to limit displayed log messages to just warnings and above</p>
-<p>Example:</p>
-<dl class="simple">
-<dt>with torch_tensorrt.logging.warnings():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id7"><span class="problematic" id="id8">**</span></a>spec)</p>
-</dd>
-</dl>
-</dd></dl>
-
 </section>
 
 
diff --git a/docs/py_api/ptq.html b/docs/py_api/ptq.html
index b01d1294ff..0a06f36bff 100644
--- a/docs/py_api/ptq.html
+++ b/docs/py_api/ptq.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -353,121 +353,11 @@
               
   <section id="torch-tensorrt-ptq">
 <span id="torch-tensorrt-ptq-py"></span><h1>torch_tensorrt.ptq<a class="headerlink" href="#torch-tensorrt-ptq" title="Permalink to this headline">¶</a></h1>
-<span class="target" id="module-torch_tensorrt.ptq"></span><dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
-:param cache_file: path to cache file.
-:param algo_type: choice of calibration algorithm.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
-<p>An enumeration.</p>
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">
-<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">
-<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION_2</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION_2:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">
-<span class="sig-name descname"><span class="pre">LEGACY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.LEGACY_CALIBRATION:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">
-<span class="sig-name descname"><span class="pre">MINMAX_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.MINMAX_CALIBRATION:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
-data which is passed during calibration.
-:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
-:param algo_type: choice of calibration algorithm.
-:param cache_file: path to cache file.
-:param use_cache: flag which enables usage of pre-existing cache.
-:param device: device on which calibration data is copied to.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch_size">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch_size" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_cache_mode_batch">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_cache_mode_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_cache_mode_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_cache_mode_batch" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.read_calibration_cache">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">read_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#read_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.read_calibration_cache" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.write_calibration_cache">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">write_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#write_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.write_calibration_cache" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 <section id="classes">
 <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
-<dl class="py class">
-<dt class="sig sig-object py" id="id0">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
-data which is passed during calibration.
-:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
-:param algo_type: choice of calibration algorithm.
-:param cache_file: path to cache file.
-:param use_cache: flag which enables usage of pre-existing cache.
-:param device: device on which calibration data is copied to.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="id1">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id1" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
-:param cache_file: path to cache file.
-:param algo_type: choice of calibration algorithm.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
 </section>
 <section id="enums">
 <h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
-<dl class="py class">
-<dt class="sig sig-object py" id="id2">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id2" title="Permalink to this definition">¶</a></dt>
-<dd><p>An enumeration.</p>
-</dd></dl>
-
 </section>
 </section>
 
diff --git a/docs/py_api/torch_tensorrt.html b/docs/py_api/torch_tensorrt.html
index 91ded448bc..7bf4918d7c 100644
--- a/docs/py_api/torch_tensorrt.html
+++ b/docs/py_api/torch_tensorrt.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -351,350 +351,14 @@
               
   <section id="torch-tensorrt">
 <span id="torch-tensorrt-py"></span><h1>torch_tensorrt<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</a></h1>
-<span class="target" id="module-torch_tensorrt"></span><section id="functions">
+<section id="functions">
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gpu_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.set_device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.compile">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.compile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compile a PyTorch module for NVIDIA GPUs using TensorRT</p>
-<p>Takes a existing PyTorch module and a set of settings to configure the compiler
-and using the path specified in <code class="docutils literal notranslate"><span class="pre">ir</span></code> lower and compile the module to TensorRT
-returning a PyTorch Module back</p>
-<p>Converts specifically the forward method of a Module</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
-torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
-to select device type.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
-    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
-    torch_tensorrt.Input(
-        min_shape=(1, 224, 224, 3),
-        opt_shape=(1, 512, 512, 3),
-        max_shape=(1, 1024, 1024, 3),
-        dtype=torch.int32
-        format=torch.channel_last
-    ), # Dynamic input shape for input #2
-    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
-]
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
-<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
-<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
-</ul>
-</dd>
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>Compiled Module, when run it will execute via TensorRT</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>torch.nn.Module</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.convert_method_to_trt_engine">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
-<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
-torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
-to select device type.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
-    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
-    torch_tensorrt.Input(
-        min_shape=(1, 224, 224, 3),
-        opt_shape=(1, 512, 512, 3),
-        max_shape=(1, 1024, 1024, 3),
-        dtype=torch.int32
-        format=torch.channel_last
-    ), # Dynamic input shape for input #2
-    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
-]
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
-<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
-<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
-</ul>
-</dd>
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>bytes</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.get_build_info">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">get_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#get_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.get_build_info" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a string containing the build information of torch_tensorrt distribution</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>String containing the build information for torch_tensorrt distribution</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>str</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.dump_build_info">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dump_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#dump_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dump_build_info" title="Permalink to this definition">¶</a></dt>
-<dd><p>Prints build information about the torch_tensorrt distribution to stdout</p>
-</dd></dl>
-
 </section>
 <section id="classes">
 <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.Input">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input" title="Permalink to this definition">¶</a></dt>
-<dd><p>Defines an input to a module in terms of expected shape, data type and tensor format.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Variables</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>shape_mode</strong> (<em>torch_tensorrt.Input._ShapeMode</em>) – Is input statically or dynamically shaped</p></li>
-<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>Dict</em>) – <p>Either a single Tuple or a dict of tuples defining the input shape.
-Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form
-<a href="#id1"><span class="problematic" id="id2">``</span></a>{</p>
-<blockquote>
-<div><p>”min_shape”: Tuple,
-“opt_shape”: Tuple,
-“max_shape”: Tuple</p>
-</div></blockquote>
-<p>}``</p>
-</p></li>
-<li><p><strong>dtype</strong> (<em>torch_tensorrt.dpython:type</em>) – The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</p></li>
-<li><p><strong>format</strong> (<a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="torch_tensorrt.Input.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>__init__ Method for torch_tensorrt.Input</p>
-<p>Input accepts one of a few construction patterns</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p></li>
-<li><p><strong>min_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Min size of input tensor’s shape range
-Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
-<li><p><strong>opt_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Opt size of input tensor’s shape range
-Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
-<li><p><strong>max_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Max size of input tensor’s shape range
-Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
-<li><p><strong>dtype</strong> (<em>torch.dpython:type</em><em> or </em><em>torch_tensorrt.dpython:type</em>) – Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</p></li>
-<li><p><strong>format</strong> (<em>torch.memory_format</em><em> or </em><a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
-</ul>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<ul class="simple">
-<li><p>Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</p></li>
-<li><p>Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</p></li>
-<li><p>Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</p></li>
-</ul>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Input.dtype">
-<span class="sig-name descname"><span class="pre">dtype</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;dtype.unknown:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.dtype" title="Permalink to this definition">¶</a></dt>
-<dd><p>torch_tensorrt.dtype.float32)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p>The expected data type of the input tensor (default</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Input.format">
-<span class="sig-name descname"><span class="pre">format</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;TensorFormat.contiguous:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.format" title="Permalink to this definition">¶</a></dt>
-<dd><p>torch_tensorrt.TensorFormat.NCHW)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p>The expected format of the input tensor (default</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Input.shape">
-<span class="sig-name descname"><span class="pre">shape</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape" title="Permalink to this definition">¶</a></dt>
-<dd><p>Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form <code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">&quot;min_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;opt_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;max_shape&quot;:</span> <span class="pre">Tuple</span> <span class="pre">}</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p>(Tuple or Dict)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Input.shape_mode">
-<span class="sig-name descname"><span class="pre">shape_mode</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape_mode" title="Permalink to this definition">¶</a></dt>
-<dd><p>Is input statically or dynamically shaped</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p>(torch_tensorrt.Input._ShapeMode)</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.Device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device" title="Permalink to this definition">¶</a></dt>
-<dd><p>Defines a device that can be used to specify target devices for engines</p>
-<dl class="field-list simple">
-<dt class="field-odd">Variables</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>device_type</strong> (<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType"><em>torch_tensorrt.DeviceType</em></a>) – Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p></li>
-<li><p><strong>gpu_id</strong> (<em>python:int</em>) – Device ID for target GPU</p></li>
-<li><p><strong>dla_core</strong> (<em>python:int</em>) – Core ID for target DLA core</p></li>
-<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Whether falling back to GPU if DLA cannot support an op should be allowed</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="torch_tensorrt.Device.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>__init__ Method for torch_tensorrt.Device</p>
-<p>Device accepts one of a few construction patterns</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>spec</strong> (<em>str</em>) – String with device spec e.g. “dla:0” for dla, core_id 0</p>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>gpu_id</strong> (<em>python:int</em>) – ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</p></li>
-<li><p><strong>dla_core</strong> (<em>python:int</em>) – ID of target DLA core. If specified, no positional arguments should be provided.</p></li>
-<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</p></li>
-</ul>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<ul class="simple">
-<li><p>Device(“gpu:1”)</p></li>
-<li><p>Device(“cuda:1”)</p></li>
-<li><p>Device(“dla:0”, allow_gpu_fallback=True)</p></li>
-<li><p>Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</p></li>
-<li><p>Device(dla_core=0, allow_gpu_fallback=True)</p></li>
-<li><p>Device(gpu_id=1)</p></li>
-</ul>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Device.allow_gpu_fallback">
-<span class="sig-name descname"><span class="pre">allow_gpu_fallback</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#torch_tensorrt.Device.allow_gpu_fallback" title="Permalink to this definition">¶</a></dt>
-<dd><p>(bool) Whether falling back to GPU if DLA cannot support an op should be allowed</p>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Device.device_type">
-<span class="sig-name descname"><span class="pre">device_type</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Device.device_type" title="Permalink to this definition">¶</a></dt>
-<dd><p>Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p>(<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType">torch_tensorrt.DeviceType</a>)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Device.dla_core">
-<span class="sig-name descname"><span class="pre">dla_core</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.dla_core" title="Permalink to this definition">¶</a></dt>
-<dd><p>(int) Core ID for target DLA core</p>
-</dd></dl>
-
-<dl class="py attribute">
-<dt class="sig sig-object py" id="torch_tensorrt.Device.gpu_id">
-<span class="sig-name descname"><span class="pre">gpu_id</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.gpu_id" title="Permalink to this definition">¶</a></dt>
-<dd><p>(int) Device ID for target GPU</p>
-</dd></dl>
-
-</dd></dl>
-
 </section>
 <section id="enums">
 <h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.dtype">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#torch_tensorrt.dtype" title="Permalink to this definition">¶</a></dt>
-<dd><p>Enum to specifiy operating precision for engine execution</p>
-<p>Members:</p>
-<blockquote>
-<div><p>float : 32 bit floating point number</p>
-<p>float32 : 32 bit floating point number</p>
-<p>half : 16 bit floating point number</p>
-<p>float16 : 16 bit floating point number</p>
-<p>int8 : 8 bit integer number</p>
-<p>int32 : 32 bit integer number</p>
-<p>bool : Boolean value</p>
-<p>unknown : Unknown data type</p>
-</div></blockquote>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.DeviceType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">DeviceType</span></span><a class="headerlink" href="#torch_tensorrt.DeviceType" title="Permalink to this definition">¶</a></dt>
-<dd><p>Enum to specify device kinds to build TensorRT engines for</p>
-<p>Members:</p>
-<blockquote>
-<div><p>GPU : Specify using GPU to execute TensorRT Engine</p>
-<p>DLA : Specify using DLA to execute TensorRT Engine (Jetson Only)</p>
-</div></blockquote>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.EngineCapability">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">EngineCapability</span></span><a class="headerlink" href="#torch_tensorrt.EngineCapability" title="Permalink to this definition">¶</a></dt>
-<dd><p>Enum to specify engine capability settings (selections of kernels to meet safety requirements)</p>
-<p>Members:</p>
-<blockquote>
-<div><p>safe_gpu : Use safety GPU kernels only</p>
-<p>safe_dla : Use safety DLA kernels only</p>
-<p>default : Use default behavior</p>
-</div></blockquote>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="torch_tensorrt.TensorFormat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">TensorFormat</span></span><a class="headerlink" href="#torch_tensorrt.TensorFormat" title="Permalink to this definition">¶</a></dt>
-<dd><p>Enum to specifiy the memory layout of tensors</p>
-<p>Members:</p>
-<blockquote>
-<div><p>contiguous : Contiguous memory layout (NCHW / Linear)</p>
-<p>channels_last : Channels last memory layout (NHWC)</p>
-</div></blockquote>
-</dd></dl>
-
 </section>
 <section id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
diff --git a/docs/py_api/ts.html b/docs/py_api/ts.html
index ce71414d68..f87429ebda 100644
--- a/docs/py_api/ts.html
+++ b/docs/py_api/ts.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
                 </div>
               
             
@@ -353,231 +353,8 @@
               
   <section id="torch-tensorrt-ts">
 <span id="torch-tensorrt-ts-py"></span><h1>torch_tensorrt.ts<a class="headerlink" href="#torch-tensorrt-ts" title="Permalink to this headline">¶</a></h1>
-<span class="target" id="module-torch_tensorrt.ts"></span><section id="functions">
+<section id="functions">
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ts.compile">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_min_timing_iters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules=[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
-<p>Takes a existing TorchScript module and a set of settings to configure the compiler
-and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
-<p>Converts specifically the forward method of a TorchScript Module</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
-<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
-torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
-to select device type.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
-    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
-    torch_tensorrt.Input(
-        min_shape=(1, 224, 224, 3),
-        opt_shape=(1, 512, 512, 3),
-        max_shape=(1, 1024, 1024, 3),
-        dtype=torch.int32
-        format=torch.channel_last
-    ), # Dynamic input shape for input #2
-    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
-]
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
-<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
-<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
-<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
-<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
-<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
-<li><p><strong>num_min_timing_iters</strong> (<em>python:int</em>) – Number of minimization timing iterations used to select kernels</p></li>
-<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
-<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
-<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
-<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
-<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
-<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</p></li>
-<li><p><strong>torch_executed_ops</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
-<li><p><strong>torch_executed_modules</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
-</ul>
-</dd>
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>Compiled TorchScript Module, when run it will execute via TensorRT</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>torch.jit.ScriptModule</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ts.convert_method_to_trt_engine">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_min_timing_iters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
-<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
-<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
-<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to convert</p></li>
-</ul>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
-torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
-to select device type.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
-    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
-    torch_tensorrt.Input(
-        min_shape=(1, 224, 224, 3),
-        opt_shape=(1, 512, 512, 3),
-        max_shape=(1, 1024, 1024, 3),
-        dtype=torch.int32
-        format=torch.channel_last
-    ), # Dynamic input shape for input #2
-    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
-]
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
-<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
-<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
-<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
-<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
-<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
-<li><p><strong>num_min_timing_iters</strong> (<em>python:int</em>) – Number of minimization timing iterations used to select kernels</p></li>
-<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
-<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
-<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
-<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
-</ul>
-</dd>
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>bytes</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ts.check_method_op_support">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">check_method_op_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#check_method_op_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.check_method_op_support" title="Permalink to this definition">¶</a></dt>
-<dd><p>Checks to see if a method is fully supported by torch_tensorrt</p>
-<p>Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators
-that are not supported are printed out and the function returns false, else true.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
-<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
-<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to check</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>True if supported Method</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>bool</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ts.embed_engine_in_new_module">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
-<dd><p>Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</p>
-<p>Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
-Registers the forward method to execute the TensorRT engine with the function signature:</p>
-<blockquote>
-<div><p>forward(Tensor[]) -&gt; Tensor[]</p>
-</div></blockquote>
-<dl>
-<dt>TensorRT bindings must have names with the following format:</dt><dd><ul class="simple">
-<li><p>[symbol].[index in input / output array]</p></li>
-</ul>
-<p>ex.
-- [x.0, x.1, x.2] -&gt; [y.0]</p>
-</dd>
-</dl>
-<p>Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>serialized_engine</strong> (<em>bytes</em>) – Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</p>
-</dd>
-<dt class="field-even">Keyword Arguments</dt>
-<dd class="field-even"><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – Target device to run engine on. Must be compatible with engine provided. Default: Current active device</p>
-</dd>
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>New TorchScript module with engine embedded</p>
-</dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>torch.jit.ScriptModule</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_min_timing_iters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch._C.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f9f791ec5b0&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
-<dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
-<dl class="field-list simple">
-<dt class="field-odd">Keyword Arguments</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
-torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
-to select device type.</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
-    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
-    torch_tensorrt.Input(
-        min_shape=(1, 224, 224, 3),
-        opt_shape=(1, 512, 512, 3),
-        max_shape=(1, 1024, 1024, 3),
-        dtype=torch.int32
-        format=torch.channel_last
-    ), # Dynamic input shape for input #2
-    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
-]
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
-</pre></div>
-</div>
-</p></li>
-<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
-<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
-<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
-<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
-<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
-<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
-<li><p><strong>num_min_timing_iters</strong> (<em>python:int</em>) – Number of minimization timing iterations used to select kernels</p></li>
-<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
-<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
-<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
-<li><p><strong>calibrator</strong> – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
 </section>
 </section>
 
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index 7673017417..882544e597 100644
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+++ b/docs/src/pytorch-sphinx-theme/docs/changelog.html
@@ -197,7 +197,7 @@
               
               
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diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html
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+++ b/docs/src/pytorch-sphinx-theme/docs/configuring.html
@@ -197,7 +197,7 @@
               
               
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index 4fe78280e0..98d7cdeadb 100644
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+++ b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
index 91b4265a83..657c9acefa 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
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@@ -546,7 +546,7 @@ <h3><a class="toc-backref" href="#id41"><span class="section-number">3.4.4. </sp
 <section id="code-blocks">
 <h3><a class="toc-backref" href="#id42"><span class="section-number">3.4.5. </span>Code Blocks</a><a class="headerlink" href="#code-blocks" title="Permalink to this headline">¶</a></h3>
 <pre class="literal-block"># parsed-literal test
-curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+ffedb78).tar-gz</pre>
+curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+3a8704db).tar-gz</pre>
 <div class="literal-block-wrapper docutils container" id="id23">
 <div class="code-block-caption"><span class="caption-text">Code Blocks can have captions.</span><a class="headerlink" href="#id23" title="Permalink to this code">¶</a></div>
 <div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="w"></span>
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
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+++ b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0a0+3a8704db)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
index 1ba6d32b98..fabfe99fd6 100644
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@@ -197,7 +197,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
index 116a3a8f76..6f6d6c0343 100644
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@@ -197,7 +197,7 @@
               
               
                 <div class="version">
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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 262205461f..4f2cecd555 100644
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+++ b/docs/src/pytorch-sphinx-theme/docs/index.html
@@ -197,7 +197,7 @@
               
               
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-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/src/pytorch-sphinx-theme/docs/installing.html b/docs/src/pytorch-sphinx-theme/docs/installing.html
index a957814bd1..35b193ef52 100644
--- a/docs/src/pytorch-sphinx-theme/docs/installing.html
+++ b/docs/src/pytorch-sphinx-theme/docs/installing.html
@@ -197,7 +197,7 @@
               
               
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-                  master (1.2.0a0+ffedb78)
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diff --git a/docs/tutorials/creating_torchscript_module_in_python.html b/docs/tutorials/creating_torchscript_module_in_python.html
index cd4f108032..22d0922ff1 100644
--- a/docs/tutorials/creating_torchscript_module_in_python.html
+++ b/docs/tutorials/creating_torchscript_module_in_python.html
@@ -199,7 +199,7 @@
               
               
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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 e5ccc14ec0..805d564f96 100644
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@@ -199,7 +199,7 @@
               
               
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diff --git a/docs/tutorials/getting_started_with_fx_path.html b/docs/tutorials/getting_started_with_fx_path.html
new file mode 100644
index 0000000000..617792d8c0
--- /dev/null
+++ b/docs/tutorials/getting_started_with_fx_path.html
@@ -0,0 +1,916 @@
+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
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+  <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+  <title>Torch-TensorRT (FX Path) User Guide &mdash; Torch-TensorRT master documentation</title>
+  
+
+  
+  
+  
+  
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+  
+
+  
+  
+    
+
+  
+
+  <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+  <!-- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> -->
+  <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+  <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+  <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
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+</head>
+
+<div class="container-fluid header-holder tutorials-header" id="header-holder">
+  <div class="container">
+    <div class="header-container">
+      <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
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+          <li>
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+                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/docs/stable/index.html">
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+                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/audio/stable/index.html">
+                  <span class="dropdown-title">torchaudio</span>
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+  <section id="torch-tensorrt-fx-path-user-guide">
+<h1>Torch-TensorRT (FX Path) User Guide<a class="headerlink" href="#torch-tensorrt-fx-path-user-guide" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT (FX Path) is a tool that can convert a PyTorch model through torch.FX to an TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine developed by Nvidia which composed of various kinds of optimization including kernel fusion, graph optimization, low precision, etc..
+This tool is developed in Python environment providing most usability to researchers and engineers. There are a few stages that a user want to use this tool and we will introduce them here.</p>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Method 1. Follow the instrucions for Torch-TensorRT</p></li>
+<li><p>Method 2. To install FX path only (Python path) and avoid the C++ build for torchscript path</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ conda create --name python_env <span class="nv">python</span><span class="o">=</span><span class="m">3</span>.8
+$ conda activate python_env
+
+<span class="c1"># Recommend to install PyTorch 1.12 and later</span>
+$ conda install pytorch torchvision torchtext <span class="nv">cudatoolkit</span><span class="o">=</span><span class="m">11</span>.3 -c pytorch-nightly
+
+<span class="c1"># Install TensorRT python package</span>
+$ pip3 install nvidia-pyindex
+$ pip3 install nvidia-tensorrt<span class="o">==</span><span class="m">8</span>.2.4.2
+$ git clone https://github.com/pytorch/TensorRT.git
+$ <span class="nb">cd</span> TensorRT/py <span class="o">&amp;&amp;</span> python setup.py install --fx-only <span class="o">&amp;&amp;</span> <span class="nb">cd</span> ..
+
+$ pyton -c <span class="s2">&quot;import torch_tensorrt.fx&quot;</span>
+<span class="c1"># Test an example by</span>
+$ python py/torch_tensorrt/fx/example/lower_example.py
+</pre></div>
+</div>
+</section>
+<section id="converting-a-pytorch-model-to-tensorrt-engine">
+<h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#converting-a-pytorch-model-to-tensorrt-engine" title="Permalink to this headline">¶</a></h2>
+<p>We will go through an example to illustrate the major steps that FX path uses to</p>
+<ul class="simple">
+<li><p><strong>Step 1: Trace the model with acc_tracer</strong></p></li>
+</ul>
+<p>Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+
+<span class="c1"># Build the model which needs to be a PyTorch nn.Module.</span>
+<span class="nv">my_pytorch_model</span> <span class="o">=</span> build_model<span class="o">()</span>
+
+<span class="c1"># Prepare inputs to the model. Inputs have to be a List of Tensors</span>
+<span class="nv">inputs</span> <span class="o">=</span> <span class="o">[</span>Tensor, Tensor, ...<span class="o">]</span>
+
+<span class="c1"># Trace the model with acc_tracer.</span>
+<span class="nv">acc_mod</span> <span class="o">=</span> acc_tracer.trace<span class="o">(</span>my_pytorch_model, inputs<span class="o">)</span>
+</pre></div>
+</div>
+<p><em>Common Errors:</em></p>
+<p>symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in <a class="reference external" href="https://pytorch.org/docs/stable/fx.html#dynamic-control-flow">FX guide</a>.</p>
+<ul class="simple">
+<li><p><strong>Step 2: Build TensorRT engine</strong></p></li>
+</ul>
+<p>There are <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch">two different modes</a> for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set <code class="docutils literal notranslate"><span class="pre">explicit_batch_dimension=False</span></code> in <code class="docutils literal notranslate"><span class="pre">lower_to_trt()</span></code>. We do not recommend to use it since it will lack of support in future TensorRT versions.</p>
+<p>Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable <code class="docutils literal notranslate"><span class="pre">dynamic_batch</span></code> in <code class="docutils literal notranslate"><span class="pre">lower_to_trt()</span></code> if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).</p>
+<p>For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import deeplearning.trt.fx2trt.converter.converters
+from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
+
+<span class="c1"># InputTensorSpec is a dataclass we use to store input information.</span>
+<span class="c1"># There&#39;re two ways we can build input_specs.</span>
+<span class="c1"># Option 1, build it manually.</span>
+<span class="nv">input_specs</span> <span class="o">=</span> <span class="o">[</span>
+  InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+  InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>, <span class="m">4</span>, <span class="m">5</span><span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="o">]</span>
+<span class="c1"># Option 2, build it using sample_inputs where user provide a sample</span>
+<span class="nv">inputs</span> <span class="o">=</span> <span class="o">[</span>
+torch.rand<span class="o">((</span><span class="m">1</span>,2,3<span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+torch.rand<span class="o">((</span><span class="m">1</span>,4,5<span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="o">]</span>
+<span class="nv">input_specs</span> <span class="o">=</span> InputTensorSpec.from_tensors<span class="o">(</span>inputs<span class="o">)</span>
+
+<span class="c1"># IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.</span>
+<span class="nv">input_specs</span> <span class="o">=</span> <span class="o">[</span>
+    InputTensorSpec<span class="o">(</span>
+        <span class="nv">shape</span><span class="o">=(</span>-1, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>,
+        <span class="nv">dtype</span><span class="o">=</span>torch.float32,
+        <span class="c1"># Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models</span>
+        <span class="c1"># (min_shape, optimize_target_shape, max_shape)</span>
+        <span class="c1"># For more information refer to fx/input_tensor_spec.py</span>
+        <span class="nv">shape_ranges</span> <span class="o">=</span> <span class="o">[</span>
+            <span class="o">((</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>, <span class="o">(</span><span class="m">4</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>, <span class="o">(</span><span class="m">100</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">))</span>,
+        <span class="o">]</span>,
+    <span class="o">)</span>,
+    InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>, <span class="m">4</span>, <span class="m">5</span><span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="o">]</span>
+
+<span class="c1"># Build a TRT interpreter. Set explicit_batch_dimension accordingly.</span>
+<span class="nv">interpreter</span> <span class="o">=</span> TRTInterpreter<span class="o">(</span>
+    acc_mod, input_specs, <span class="nv">explicit_batch_dimension</span><span class="o">=</span>True/False
+<span class="o">)</span>
+
+<span class="c1"># The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.</span>
+<span class="c1"># The TRTInterpreterResult contains required parameter to build TRTModule,</span>
+<span class="c1"># and other informational output from TRTInterpreter run.</span>
+class TRTInterpreterResult<span class="o">(</span>NamedTuple<span class="o">)</span>:
+    engine: Any
+    input_names: Sequence<span class="o">[</span>str<span class="o">]</span>
+    output_names: Sequence<span class="o">[</span>str<span class="o">]</span>
+    serialized_cache: bytearray
+
+<span class="c1">#max_batch_size: set accordingly for maximum batch size you will use.</span>
+<span class="c1">#max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
+<span class="c1">#lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
+<span class="c1">#sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
+<span class="c1">#force_fp32_output: force output to be fp32</span>
+<span class="c1">#strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.</span>
+<span class="c1">#algorithm_selector: set up algorithm selection for certain layer</span>
+<span class="c1">#timing_cache: enable timing cache for TensorRT</span>
+<span class="c1">#profiling_verbosity: TensorRT logging level</span>
+<span class="nv">trt_interpreter_result</span> <span class="o">=</span> interpreter.run<span class="o">(</span>
+    <span class="nv">max_batch_size</span><span class="o">=</span><span class="m">64</span>,
+    <span class="nv">max_workspace_size</span><span class="o">=</span><span class="m">1</span> <span class="s">&lt;&lt; 25,</span>
+<span class="s">    sparse_weights=False,</span>
+<span class="s">    force_fp32</span>_output<span class="o">=</span>False,
+    <span class="nv">strict_type_constraints</span><span class="o">=</span>False,
+    <span class="nv">algorithm_selector</span><span class="o">=</span>None,
+    <span class="nv">timing_cache</span><span class="o">=</span>None,
+    <span class="nv">profiling_verbosity</span><span class="o">=</span>None,
+<span class="o">)</span>
+</pre></div>
+</div>
+<p><em>Common Errors:</em></p>
+<p>RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.</p>
+<ul class="simple">
+<li><p><strong>Step 3: Run the model</strong></p></li>
+</ul>
+<p>One way is using TRTModule, which is basically a PyTorch nn.Module.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>from torch_tensorrt.fx import TRTModule
+<span class="nv">mod</span> <span class="o">=</span> TRTModule<span class="o">(</span>
+    trt_interpreter_result.engine,
+    trt_interpreter_result.input_names,
+    trt_interpreter_result.output_names<span class="o">)</span>
+<span class="c1"># Just like all other PyTorch modules</span>
+<span class="nv">outputs</span> <span class="o">=</span> mod<span class="o">(</span>*inputs<span class="o">)</span>
+torch.save<span class="o">(</span>mod, <span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
+<span class="nv">reload_trt_mod</span> <span class="o">=</span> torch.load<span class="o">(</span><span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
+<span class="nv">reload_model_output</span> <span class="o">=</span> reload_trt_mod<span class="o">(</span>*inputs<span class="o">)</span>
+</pre></div>
+</div>
+<p>So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph’s operation to corresponding TensorRT operation and build up the TensoRT engine for it.</p>
+</section>
+<section id="acc-tracer">
+<h2>Acc Tracer<a class="headerlink" href="#acc-tracer" title="Permalink to this headline">¶</a></h2>
+<p>Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:</p>
+<ol class="arabic simple">
+<li><p>there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.</p></li>
+<li><p>acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.</p></li>
+</ol>
+</section>
+<section id="fx2trt">
+<h2>FX2TRT<a class="headerlink" href="#fx2trt" title="Permalink to this headline">¶</a></h2>
+<p>After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).</p>
+<p>Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
+def acc_ops_sigmoid<span class="o">(</span>network, target, args, kwargs, name<span class="o">)</span>:
+    <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="s2">    The rest arguments are attributes of fx node.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+    <span class="nv">input_val</span> <span class="o">=</span> kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
+
+    <span class="k">if</span> not isinstance<span class="o">(</span>input_val, trt.tensorrt.ITensor<span class="o">)</span>:
+        raise RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
+                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
+
+    <span class="nv">layer</span> <span class="o">=</span> network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val, <span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
+    layer.name <span class="o">=</span> name
+    <span class="k">return</span> layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
+</pre></div>
+</div>
+<section id="how-to-add-a-missing-op">
+<h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op" title="Permalink to this headline">¶</a></h3>
+<p>You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer.</p>
+<ul class="simple">
+<li><p><strong>Step 1. Add a new acc op</strong></p></li>
+</ul>
+<p>TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.</p>
+<p>In <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py">acc tracer</a>, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.</p>
+<p>In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.</p>
+<p>Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py">acc_normalizer.py</a>. e.g. the following code adds an acc op named foo() which adds two given inputs.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># NOTE: all acc ops should only take kwargs as inputs, therefore we need the &quot;*&quot;</span>
+<span class="c1"># at the beginning.</span>
+@register_acc_op
+def foo<span class="o">(</span>*, input, other, alpha<span class="o">)</span>:
+    <span class="k">return</span> input + alpha * other
+</pre></div>
+</div>
+<p>There’re two ways to register a mapping. One is <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164">register_acc_op_mapping()</a>. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">this_arg_is_optional</span> <span class="o">=</span> True
+
+@register_acc_op_mapping<span class="o">(</span>
+    <span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>, torch.add<span class="o">)</span>,
+    <span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
+        <span class="o">(</span><span class="s2">&quot;input&quot;</span>, <span class="s2">&quot;input&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;other&quot;</span>, <span class="s2">&quot;other&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;alpha&quot;</span>, <span class="s2">&quot;alpha&quot;</span>, this_arg_is_optional<span class="o">)</span>,
+    <span class="o">]</span>,
+<span class="o">)</span>
+@register_acc_op
+def foo<span class="o">(</span>*, input, other, <span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
+    <span class="k">return</span> input + alpha * other
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">op_and_target</span></code> determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the <code class="docutils literal notranslate"><span class="pre">op_and_target</span></code>, we will trigger the mapping. Since we want to map from <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>, then op would be call_function and target would be <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>. <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in <em>original node</em>. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.</p>
+<ol class="arabic simple">
+<li><p>kwargs of original nodes and acc op nodes have different name.</p></li>
+<li><p>there’re optional arguments.</p></li>
+</ol>
+<p>The other way to register a mapping is through <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206">register_custom_acc_mapper_fn()</a>. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@register_acc_op
+def foo<span class="o">(</span>*, input, other, <span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
+    <span class="k">return</span> input + alpha * other
+
+@register_custom_acc_mapper_fn<span class="o">(</span>
+    <span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>, torch.add<span class="o">)</span>,
+    <span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
+        <span class="o">(</span><span class="s2">&quot;input&quot;</span>, <span class="s2">&quot;input&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;other&quot;</span>, <span class="s2">&quot;other&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;alpha&quot;</span>, <span class="s2">&quot;alpha&quot;</span>, this_arg_is_optional<span class="o">)</span>,
+    <span class="o">]</span>,
+<span class="o">)</span>
+def custom_mapper<span class="o">(</span>node: torch.fx.Node, _: nn.Module<span class="o">)</span> -&gt; torch.fx.Node:
+    <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    `node` is original node, which is a call_function node with target</span>
+<span class="s2">    being torch.add.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+    <span class="nv">alpha</span> <span class="o">=</span> <span class="m">1</span>
+    <span class="k">if</span> <span class="s2">&quot;alpha&quot;</span> <span class="k">in</span> node.kwargs:
+        <span class="nv">alpha</span> <span class="o">=</span> node.kwargs<span class="o">[</span><span class="s2">&quot;alpha&quot;</span><span class="o">]</span>
+    <span class="nv">foo_kwargs</span> <span class="o">=</span> <span class="o">{</span><span class="s2">&quot;input&quot;</span>: node<span class="o">[</span><span class="s2">&quot;input&quot;</span><span class="o">]</span>, <span class="s2">&quot;other&quot;</span>: node<span class="o">[</span><span class="s2">&quot;other&quot;</span><span class="o">]</span>, <span class="s2">&quot;alpha&quot;</span>: alpha<span class="o">}</span>
+    with node.graph.inserting_before<span class="o">(</span>node<span class="o">)</span>:
+        <span class="nv">foo_node</span> <span class="o">=</span> node.graph.call_function<span class="o">(</span>foo, <span class="nv">kwargs</span><span class="o">=</span>foo_kwargs<span class="o">)</span>
+        foo_node.meta <span class="o">=</span> node.meta.copy<span class="o">()</span>
+        <span class="k">return</span> foo_node
+</pre></div>
+</div>
+<p>In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379">acc_normalizer.py</a>.</p>
+<p>The last step would be <em>adding unit test</em> for the new acc op or mapper function we added. The place to add the unit test is here <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py">test_acc_tracer.py</a>.</p>
+<ul class="simple">
+<li><p><strong>Step 2. Add a new fx2trt converter</strong></p></li>
+</ul>
+<p>All the developed converters for acc ops are all in <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py">acc_op_converter.py</a>. It could give you a good example of how the converter is added.</p>
+<p>Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html">TensorRT APIs</a>.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
+def acc_ops_sigmoid<span class="o">(</span>network, target, args, kwargs, name<span class="o">)</span>:
+    <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="s2">    The rest arguments are attributes of fx node.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+    <span class="nv">input_val</span> <span class="o">=</span> kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
+
+    <span class="k">if</span> not isinstance<span class="o">(</span>input_val, trt.tensorrt.ITensor<span class="o">)</span>:
+        raise RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
+                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
+
+    <span class="nv">layer</span> <span class="o">=</span> network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val, <span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
+    layer.name <span class="o">=</span> name
+    <span class="k">return</span> layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
+</pre></div>
+</div>
+<p>We need to use <code class="docutils literal notranslate"><span class="pre">tensorrt_converter</span></code> decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is <cite>acc_ops.sigmoid</cite> which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.</p>
+<p><strong>What if we can not find corresponding layers in TensorRT that do the same thing as the node.</strong></p>
+<p>In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. <em>We have not implement this feature yet. We will update once it is enabled</em>.</p>
+<p>Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op">folder</a>.</p>
+</section>
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+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
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+<li><a class="reference internal" href="#converting-a-pytorch-model-to-tensorrt-engine">Converting a PyTorch Model to TensorRT Engine</a></li>
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+              <a href="https://pytorch.org/xla">PyTorch on XLA Devices</a>
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+              <a href="https://pytorch.org/hub">Models (Beta)</a>
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From f44658bea01b2bdeab6ad93e762bc18d5eb88977 Mon Sep 17 00:00:00 2001
From: Wei Wei <wwei6@fb.com>
Date: Tue, 28 Jun 2022 15:08:33 -0700
Subject: [PATCH 09/10] Revert "make html"

This reverts commit 7db85f3111d02ccfd6aa615d663c4db61940f598.
---
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 102 files changed, 1271 insertions(+), 3452 deletions(-)
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@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="058c2a32b62b4b52a6bf5f2233a2258a" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="2acf632451c2482b8eee3d1e461fb6c5" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT-Getting-Started---CitriNet">
 <h1>Torch-TensorRT Getting Started - CitriNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---CitriNet" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -640,9 +640,7 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <li><p><a class="reference external" href="#4">Benchmark Torch-TensorRT models</a></p></li>
 <li><p><a class="reference external" href="#5">Conclusion</a></p></li>
 </ol>
-<blockquote>
-<div><p>## 1. Requirements</p>
-</div></blockquote>
+<p>## 1. Requirements</p>
 <p>Follow the steps in <a class="reference external" href="README.md">README</a> to prepare a Docker container, within which you can run this notebook. This notebook assumes that you are within a Jupyter environment in a docker container with Torch-TensorRT installed, such as an NGC monthly release of <code class="docutils literal notranslate"><span class="pre">nvcr.io/nvidia/pytorch:&lt;yy.mm&gt;-py3</span></code> (where <code class="docutils literal notranslate"><span class="pre">yy</span></code> indicates the last two numbers of a calendar year, and <code class="docutils literal notranslate"><span class="pre">mm</span></code> indicates the month in two-digit numerical form)</p>
 <p>Now that you are in the docker, the next step is to install the required dependencies.</p>
 <div class="nbinput docutils container">
@@ -915,9 +913,7 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 2. Download Citrinet model</p>
-</div></blockquote>
+<p>## 2. Download Citrinet model</p>
 <p>Next, we download a pretrained Nemo Citrinet model and convert it to a Torchscript module:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -1224,9 +1220,7 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 +-----------------------------------------------------------------------------+
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 3. Create Torch-TensorRT modules</p>
-</div></blockquote>
+<p>## 3. Create Torch-TensorRT modules</p>
 <p>In this step, we optimize the Citrinet Torchscript module with Torch-TensorRT with various precisions and batch sizes.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -1272,9 +1266,7 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 Generating Torchscript-TensorRT module for batchsize 128 precision torch.float16
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 4. Benchmark Torch-TensorRT models</p>
-</div></blockquote>
+<p>## 4. Benchmark Torch-TensorRT models</p>
 <p>Finally, we are ready to benchmark the Torch-TensorRT optimized Citrinet models.</p>
 </section>
 <section id="FP32-(single-precision)">
@@ -1425,9 +1417,7 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 5. Conclusion</p>
-</div></blockquote>
+<p>## 5. Conclusion</p>
 <p>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 ~<strong>2.4X</strong> with FP32, and ~<strong>2.9X</strong> with FP16 at batchsize of 128.</p>
 </section>
 <section id="What’s-next">
diff --git a/docs/_notebooks/EfficientNet-example.html b/docs/_notebooks/EfficientNet-example.html
index 2e91f44e38..05393fa342 100644
--- a/docs/_notebooks/EfficientNet-example.html
+++ b/docs/_notebooks/EfficientNet-example.html
@@ -199,7 +199,7 @@
               
               
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@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="41bd2c3185814f14a40caa4b3963533a" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="783b2cf2fc5d4e06a7736325633fdd88" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
 <h1>Torch-TensorRT Getting Started - EfficientNet-B0<a class="headerlink" href="#Torch-TensorRT-Getting-Started---EfficientNet-B0" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -691,22 +691,16 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 +-----------------------------------------------------------------------------+
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 1. Requirements</p>
-</div></blockquote>
+<p>## 1. Requirements</p>
 <p>NVIDIA’s NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
 <p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
-<blockquote>
-<div><p>## 2. EfficientNet Overview</p>
-</div></blockquote>
+<p>## 2. EfficientNet Overview</p>
 <p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
 <section id="Model-Description">
 <h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
 <p>This model is based on the <a class="reference external" href="https://arxiv.org/abs/1905.11946">EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks</a> paper.</p>
 <p><img alt="alt" class="no-scaled-link" src="https://1.bp.blogspot.com/-Cdtb97FtgdA/XO3BHsB7oEI/AAAAAAAAEKE/bmtkonwgs8cmWyI5esVo8wJPnhPLQ5bGQCLcBGAs/s1600/image4.png" style="width: 100%;" /></p>
-<blockquote>
-<div><p>## 3. Running the model without optimizations</p>
-</div></blockquote>
+<p>## 3. Running the model without optimizations</p>
 <p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -981,9 +975,7 @@ <h3>Model Description<a class="headerlink" href="#Model-Description" title="Perm
 Average throughput: 3393.46 images/second
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 4. Accelerating with Torch-TensorRT</p>
-</div></blockquote>
+<p>## 4. Accelerating with Torch-TensorRT</p>
 <p>Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a>.</p>
 </section>
 <section id="FP32-(single-precision)">
@@ -1134,9 +1126,7 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 Average throughput: 10362.23 images/second
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 5. Conclusion</p>
-</div></blockquote>
+<p>## 5. Conclusion</p>
 <p>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 <strong>1.35x</strong> with FP32, and <strong>3.13x</strong> 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.</p>
 </section>
diff --git a/docs/_notebooks/Hugging-Face-BERT.html b/docs/_notebooks/Hugging-Face-BERT.html
index 5fd0d4ca30..42c58b5cef 100644
--- a/docs/_notebooks/Hugging-Face-BERT.html
+++ b/docs/_notebooks/Hugging-Face-BERT.html
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@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="8ebcdec6af2d48ce9991ef016c10f3fd" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="2e8a7944f7a54c9a93c9656fc33a0ab3" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer">
 <h1>Masked Language Modeling (MLM) with Hugging Face BERT Transformer<a class="headerlink" href="#Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer" title="Permalink to this headline">¶</a></h1>
 <section id="Learning-objectives">
@@ -635,9 +635,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 <li><p><a class="reference external" href="#5">Benchmarking</a></p></li>
 <li><p><a class="reference external" href="#6">Conclusion</a></p></li>
 </ol>
-<blockquote>
-<div><p>## 1. Requirements</p>
-</div></blockquote>
+<p>## 1. Requirements</p>
 <p>NVIDIA’s NGC provides a PyTorch Docker Container which contains PyTorch and Torch-TensorRT. Starting with version <code class="docutils literal notranslate"><span class="pre">22.05-py3</span></code>, we can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
 <p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
 <div class="nbinput docutils container">
@@ -690,17 +688,13 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<blockquote>
-<div><p>## 2. BERT Overview</p>
-</div></blockquote>
+<p>## 2. BERT Overview</p>
 <p>Transformers comprise a class of deep learning algorithms employing self-attention; broadly speaking, the models learn large matrices of numbers, each element of which denotes how important one component of input data is to another. Since their introduction in 2017, transformers have enjoyed widespread adoption, particularly in natural language processing, but also in computer vision problems. This is largely because they are easier to parallelize than the sequence models which attention
 mechanisms were originally designed to augment.</p>
 <p>Hugging Face is a company that maintains a huge respository of pre-trained transformer models. The company also provides tools for integrating those models into PyTorch code and running inference with them.</p>
 <p>One of the most popular transformer models is BERT (Bidirectional Encoder Representations from Transformers). First developed at Google and released in 2018, it has become the backbone of Google’s search engine and a standard benchmark for NLP experiments. BERT was originally trained for next sentence prediction and masked language modeling (MLM), which aims to predict hidden words in sentences. In this notebook, we will use Hugging Face’s <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> model (BERT’s smallest and
 simplest form, which does not employ text capitalization) for MLM.</p>
-<blockquote>
-<div><p>## 3. Creating TorchScript modules</p>
-</div></blockquote>
+<p>## 3. Creating TorchScript modules</p>
 <p>First, create a pretrained BERT tokenizer from the <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> model</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -815,9 +809,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 4. Compiling with Torch-TensorRT</p>
-</div></blockquote>
+<p>## 4. Compiling with Torch-TensorRT</p>
 <p>Change the logging level to avoid long printouts</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -887,9 +879,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<blockquote>
-<div><p>## 5. Benchmarking</p>
-</div></blockquote>
+<p>## 5. Benchmarking</p>
 <p>In developing this notebook, we conducted our benchmarking on a single NVIDIA A100 GPU. Your results may differ from those shown, particularly on a different GPU.</p>
 <p>This function passes the inputs into the model and runs inference <code class="docutils literal notranslate"><span class="pre">num_loops</span></code> times, then returns a list of length containing the amount of time in seconds that each instance of inference took.</p>
 <div class="nbinput nblast docutils container">
@@ -1059,9 +1049,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 6. Conclusion</p>
-</div></blockquote>
+<p>## 6. Conclusion</p>
 <p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for Masked Language Modeling with Hugging Face’s <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> transformer and testing the performance impact of the optimization. With Torch-TensorRT on an NVIDIA A100 GPU, we observe the speedups indicated below. 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.</p>
 <p>Scripted (GPU): 1.0x Traced (GPU): 1.62x Torch-TensorRT (FP32): 2.14x Torch-TensorRT (FP16): 3.15x</p>
diff --git a/docs/_notebooks/Resnet50-example.html b/docs/_notebooks/Resnet50-example.html
index 32e209e3d6..f5ef38075d 100644
--- a/docs/_notebooks/Resnet50-example.html
+++ b/docs/_notebooks/Resnet50-example.html
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@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="44c689c3c3824ebd9639c100f7c30cd4" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
+<p><img alt="fdd5160d4757402ea245c7fbb8dc0f03" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
 <section id="Torch-TensorRT-Getting-Started---ResNet-50">
 <h1>Torch-TensorRT Getting Started - ResNet 50<a class="headerlink" href="#Torch-TensorRT-Getting-Started---ResNet-50" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -744,22 +744,16 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 1. Requirements</p>
-</div></blockquote>
+<p>## 1. Requirements</p>
 <p>NVIDIA’s NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
 <p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
-<blockquote>
-<div><p>## 2. ResNet-50 Overview</p>
-</div></blockquote>
+<p>## 2. ResNet-50 Overview</p>
 <p>PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.</p>
 <section id="Model-Description">
 <h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
 <p>This ResNet-50 model is based on the <a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a> paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network”. The input size is fixed to 32x32.</p>
 <p><img alt="alt" class="no-scaled-link" src="https://pytorch.org/assets/images/resnet.png" style="width: 50%;" /></p>
-<blockquote>
-<div><p>## 3. Running the model without optimizations</p>
-</div></blockquote>
+<p>## 3. Running the model without optimizations</p>
 <p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -1228,9 +1222,7 @@ <h3>Model Description<a class="headerlink" href="#Model-Description" title="Perm
 Average batch time: 75.41 ms
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 4. Accelerating with Torch-TensorRT</p>
-</div></blockquote>
+<p>## 4. Accelerating with Torch-TensorRT</p>
 <p>Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a>.</p>
 </section>
 <section id="FP32-(single-precision)">
@@ -1353,9 +1345,7 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 Average batch time: 14.69 ms
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 5. Conclusion</p>
-</div></blockquote>
+<p>## 5. Conclusion</p>
 <p>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 <strong>1.84x</strong> with FP32, and <strong>5.2x</strong> 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.</p>
 </section>
diff --git a/docs/_notebooks/dynamic-shapes.html b/docs/_notebooks/dynamic-shapes.html
index b8694b4eec..172b61fbce 100644
--- a/docs/_notebooks/dynamic-shapes.html
+++ b/docs/_notebooks/dynamic-shapes.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="22724bc77cb446a7944bbacf7074a017" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="195ac3794a674b1a8c327ebde46a3be4" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT---Using-Dynamic-Shapes">
 <h1>Torch-TensorRT - Using Dynamic Shapes<a class="headerlink" href="#Torch-TensorRT---Using-Dynamic-Shapes" title="Permalink to this headline">¶</a></h1>
 <p>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
diff --git a/docs/_notebooks/getting_started_with_fx_path_module.html b/docs/_notebooks/getting_started_with_fx_path_module.html
deleted file mode 100644
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-</style>
-<p>The purpose of this example is to demonstrate the overall flow of lowering a PyTorch model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like: 1. Use splitter to split the model if there’re ops in the model that we don’t want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance. 2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path. If
-we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
-import torch.fx
-import torch.nn as nn
-from torch_tensorrt.fx.utils import LowerPrecision
-import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
-from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule
-from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.linear = nn.Linear(10, 10)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.linear(x)
-        x = self.relu(x)
-        x = torch.linalg.norm(x, ord=2, dim=1)
-        x = self.relu(x)
-        return x
-
-
-inputs = [torch.randn((1, 10), device=torch.device(&#39;cuda&#39;))]
-model = Model().cuda().eval()
-</pre></div>
-</div>
-</div>
-<p>acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators to acc ops.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>traced = acc_tracer.trace(model, inputs)
-</pre></div>
-</div>
-</div>
-<p>Splitter will split the model into several submodules. The name of submodules will be either <code class="docutils literal notranslate"><span class="pre">run_on_acc_{}</span></code> or <code class="docutils literal notranslate"><span class="pre">run_on_gpu_{}</span></code>. Submodules named <code class="docutils literal notranslate"><span class="pre">run_on_acc_{}</span></code> can be fully lowered to TensorRT via fx2trt while submodules named <code class="docutils literal notranslate"><span class="pre">run_on_gpu_{}</span></code> has unsupported ops and can’t be lowered by fx2trt. We can still run <code class="docutils literal notranslate"><span class="pre">run_on_gpu_{}</span></code> submodules on GPU if ops there have cuda implementation.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>splitter = TRTSplitter(traced, inputs)
-</pre></div>
-</div>
-</div>
-<p>Preview functionality allows us to see what are the supported ops and unsupported ops. We can optionally the dot graph which will color supported ops and unsupported ops differently.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>splitter.node_support_preview()
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-
-Supported node types in the model:
-acc_ops.linear: ((), {&#39;input&#39;: torch.float32, &#39;weight&#39;: torch.float32, &#39;bias&#39;: torch.float32})
-acc_ops.relu: ((), {&#39;input&#39;: torch.float32})
-
-Unsupported node types in the model:
-acc_ops.linalg_norm: ((), {&#39;input&#39;: torch.float32})
-
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&#34;\nSupported node types in the model:\nacc_ops.linear: ((), {&#39;input&#39;: torch.float32, &#39;weight&#39;: torch.float32, &#39;bias&#39;: torch.float32})\nacc_ops.relu: ((), {&#39;input&#39;: torch.float32})\n\nUnsupported node types in the model:\nacc_ops.linalg_norm: ((), {&#39;input&#39;: torch.float32})\n&#34;
-</pre></div></div>
-</div>
-<p>After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>split_mod = splitter()
-print(split_mod.graph)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Got 2 acc subgraphs and 1 non-acc subgraphs
-graph():
-    %x : [#users=1] = placeholder[target=x]
-    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})
-    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})
-    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})
-    return _run_on_acc_2
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(split_mod._run_on_acc_0.graph)
-print(split_mod._run_on_gpu_1.graph)
-print(split_mod._run_on_acc_2.graph)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-graph():
-    %x : [#users=1] = placeholder[target=x]
-    %linear_weight : [#users=1] = get_attr[target=linear.weight]
-    %linear_bias : [#users=1] = get_attr[target=linear.bias]
-    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})
-    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})
-    return relu_2
-graph():
-    %relu_2 : [#users=1] = placeholder[target=relu_2]
-    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})
-    return linalg_norm_1
-graph():
-    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]
-    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})
-    return relu_3
-</pre></div></div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">split_mod</span></code> contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def get_submod_inputs(mod, submod, inputs):
-    acc_inputs = None
-
-    def get_input(self, inputs):
-        nonlocal acc_inputs
-        acc_inputs = inputs
-
-    handle = submod.register_forward_pre_hook(get_input)
-    mod(*inputs)
-    handle.remove()
-    return acc_inputs
-
-# Since the model is splitted into three segments. We need to lower each TRT eligible segment.
-# If we know the model can be fully lowered, we can skip the splitter part.
-for name, _ in split_mod.named_children():
-    if &quot;_run_on_acc&quot; in name:
-        submod = getattr(split_mod, name)
-        # Get submodule inputs for fx2trt
-        acc_inputs = get_submod_inputs(split_mod, submod, inputs)
-
-        # fx2trt replacement
-        interp = TRTInterpreter(
-            submod,
-            InputTensorSpec.from_tensors(acc_inputs),
-            explicit_batch_dimension=True,
-        )
-        r = interp.run(lower_precision=LowerPrecision.FP32)
-        trt_mod = TRTModule(*r)
-        setattr(split_mod, name, trt_mod)
-
-lowered_model_output = split_mod(*inputs)
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965
-I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029
-I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302
-I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192
-</pre></div></div>
-</div>
-<p>Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>torch.save(split_mod, &quot;trt.pt&quot;)
-reload_trt_mod = torch.load(&quot;trt.pt&quot;)
-reload_model_output = reload_trt_mod(*inputs)
-
-# Make sure the results match
-regular_model_output = model(*inputs)
-torch.testing.assert_close(
-    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2
-)
-</pre></div>
-</div>
-</div>
-
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\ No newline at end of file
diff --git a/docs/_notebooks/getting_started_with_fx_path_module.ipynb b/docs/_notebooks/getting_started_with_fx_path_module.ipynb
deleted file mode 100644
index f17ad14e03..0000000000
--- a/docs/_notebooks/getting_started_with_fx_path_module.ipynb
+++ /dev/null
@@ -1,469 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "code_folding": [],
-    "customInput": null,
-    "hidden_ranges": [],
-    "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
-    "showInput": false
-   },
-   "source": [
-    "The purpose of this example is to demonstrate the overall flow of lowering a PyTorch\n",
-    "model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like:\n",
-    "1. Use splitter to split the model if there're ops in the model that we don't want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance.\n",
-    "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path.\n",
-    "If we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367410991,
-    "executionStopTime": 1656367412604,
-    "originalKey": "ca68b029-68a6-42d6-968e-95bb7c1aae73",
-    "requestMsgId": "f56944ff-ade2-4041-bdd6-3bce44b1405f",
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.fx\n",
-    "import torch.nn as nn\n",
-    "from torch_tensorrt.fx.utils import LowerPrecision\n",
-    "import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer\n",
-    "from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule\n",
-    "from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "code_folding": [],
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367414494,
-    "executionStopTime": 1656367422756,
-    "hidden_ranges": [],
-    "originalKey": "8f974ab2-d187-4ffe-a09b-16cd85949be4",
-    "requestMsgId": "564359f5-ac69-4666-91e1-41b299495ed1",
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "class Model(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super().__init__()\n",
-    "        self.linear = nn.Linear(10, 10)\n",
-    "        self.relu = nn.ReLU()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.linear(x)\n",
-    "        x = self.relu(x)\n",
-    "        x = torch.linalg.norm(x, ord=2, dim=1)\n",
-    "        x = self.relu(x)\n",
-    "        return x\n",
-    "\n",
-    "\n",
-    "inputs = [torch.randn((1, 10), device=torch.device('cuda'))]\n",
-    "model = Model().cuda().eval()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "code_folding": [],
-    "customInput": null,
-    "hidden_ranges": [],
-    "originalKey": "0d407e92-e9e7-48aa-9c9e-1c21a9b5fd8f",
-    "showInput": false
-   },
-   "source": [
-    "acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators\n",
-    "to acc ops."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "code_folding": [],
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367480626,
-    "executionStopTime": 1656367482881,
-    "hidden_ranges": [],
-    "originalKey": "a1d9c8c2-8ec7-425a-8518-6f7e53ab1e67",
-    "requestMsgId": "ee2da608-5f1c-4f63-9927-544717e84e8a",
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "traced = acc_tracer.trace(model, inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "code_folding": [],
-    "customInput": null,
-    "hidden_ranges": [],
-    "originalKey": "246613eb-14b5-488e-9aae-35306fc99db1",
-    "showInput": false
-   },
-   "source": [
-    "Splitter will split the model into several submodules. The name of submodules will\n",
-    "be either `run_on_acc_{}` or `run_on_gpu_{}`. Submodules named `run_on_acc_{}` can\n",
-    "be fully lowered to TensorRT via fx2trt while submodules named `run_on_gpu_{}` has\n",
-    "unsupported ops and can't be lowered by fx2trt. We can still run `run_on_gpu_{}`\n",
-    "submodules on GPU if ops there have cuda implementation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367487073,
-    "executionStopTime": 1656367487154,
-    "originalKey": "1103c70a-3766-4d89-ad2f-cdcb1c3891e0",
-    "requestMsgId": "feb888ea-ef9c-4577-b0c6-cf95bc1dd25e",
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "splitter = TRTSplitter(traced, inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "code_folding": [],
-    "customInput": null,
-    "hidden_ranges": [],
-    "originalKey": "3d65e07e-57ed-47d5-adb9-4685c69c9c6b",
-    "showInput": false
-   },
-   "source": [
-    "Preview functionality allows us to see what are the supported ops and unsupported\n",
-    "ops. We can optionally the dot graph which will color supported ops and unsupported\n",
-    "ops differently."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367489373,
-    "executionStopTime": 1656367489556,
-    "originalKey": "6aaed2d5-61b7-438e-a72a-63f91d0709e2",
-    "requestMsgId": "2948c2f8-854b-4bc2-b399-321469da320c",
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Supported node types in the model:\n",
-      "acc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\n",
-      "acc_ops.relu: ((), {'input': torch.float32})\n",
-      "\n",
-      "Unsupported node types in the model:\n",
-      "acc_ops.linalg_norm: ((), {'input': torch.float32})\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nSupported node types in the model:\\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\\nacc_ops.relu: ((), {'input': torch.float32})\\n\\nUnsupported node types in the model:\\nacc_ops.linalg_norm: ((), {'input': torch.float32})\\n\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {
-      "bento_obj_id": "139812830161136"
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splitter.node_support_preview()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "customInput": null,
-    "originalKey": "8d8035ab-869e-4096-b8e1-3539a0cfe1af",
-    "showInput": false
-   },
-   "source": [
-    "After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "code_folding": [],
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367495077,
-    "executionStopTime": 1656367495250,
-    "hidden_ranges": [],
-    "originalKey": "80e03730-955a-4cc8-b071-7f92a2cff3df",
-    "requestMsgId": "2ca46574-7176-4699-a809-2a2e2d5ffda0",
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Got 2 acc subgraphs and 1 non-acc subgraphs\n",
-      "graph():\n",
-      "    %x : [#users=1] = placeholder[target=x]\n",
-      "    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})\n",
-      "    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})\n",
-      "    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})\n",
-      "    return _run_on_acc_2\n"
-     ]
-    }
-   ],
-   "source": [
-    "split_mod = splitter()\n",
-    "print(split_mod.graph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367496353,
-    "executionStopTime": 1656367496452,
-    "originalKey": "9ce75161-978e-468e-9989-ecdbc9af0d5b",
-    "requestMsgId": "0370de27-39ec-4be0-826b-9aec90df1155",
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "graph():\n",
-      "    %x : [#users=1] = placeholder[target=x]\n",
-      "    %linear_weight : [#users=1] = get_attr[target=linear.weight]\n",
-      "    %linear_bias : [#users=1] = get_attr[target=linear.bias]\n",
-      "    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})\n",
-      "    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})\n",
-      "    return relu_2\n",
-      "graph():\n",
-      "    %relu_2 : [#users=1] = placeholder[target=relu_2]\n",
-      "    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})\n",
-      "    return linalg_norm_1\n",
-      "graph():\n",
-      "    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]\n",
-      "    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})\n",
-      "    return relu_3\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(split_mod._run_on_acc_0.graph)\n",
-    "print(split_mod._run_on_gpu_1.graph)\n",
-    "print(split_mod._run_on_acc_2.graph)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "code_folding": [],
-    "customInput": null,
-    "hidden_ranges": [],
-    "originalKey": "7a6857bc-fedd-4847-ba17-a5d114de34f3",
-    "showInput": false
-   },
-   "source": [
-    "The `split_mod` contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "code_folding": [],
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367502837,
-    "executionStopTime": 1656367510024,
-    "hidden_ranges": [],
-    "originalKey": "174fd2eb-a864-49cf-a204-6d24a8e2849d",
-    "requestMsgId": "cf7fdfe4-e781-47c8-9a9a-85b5664c10f7",
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_submod_inputs(mod, submod, inputs):\n",
-    "    acc_inputs = None\n",
-    "\n",
-    "    def get_input(self, inputs):\n",
-    "        nonlocal acc_inputs\n",
-    "        acc_inputs = inputs\n",
-    "\n",
-    "    handle = submod.register_forward_pre_hook(get_input)\n",
-    "    mod(*inputs)\n",
-    "    handle.remove()\n",
-    "    return acc_inputs\n",
-    "\n",
-    "# Since the model is splitted into three segments. We need to lower each TRT eligible segment.\n",
-    "# If we know the model can be fully lowered, we can skip the splitter part.\n",
-    "for name, _ in split_mod.named_children():\n",
-    "    if \"_run_on_acc\" in name:\n",
-    "        submod = getattr(split_mod, name)\n",
-    "        # Get submodule inputs for fx2trt\n",
-    "        acc_inputs = get_submod_inputs(split_mod, submod, inputs)\n",
-    "\n",
-    "        # fx2trt replacement\n",
-    "        interp = TRTInterpreter(\n",
-    "            submod,\n",
-    "            InputTensorSpec.from_tensors(acc_inputs),\n",
-    "            explicit_batch_dimension=True,\n",
-    "        )\n",
-    "        r = interp.run(lower_precision=LowerPrecision.FP32)\n",
-    "        trt_mod = TRTModule(*r)\n",
-    "        setattr(split_mod, name, trt_mod)\n",
-    "\n",
-    "lowered_model_output = split_mod(*inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "code_folding": [],
-    "customInput": null,
-    "hidden_ranges": [],
-    "originalKey": "f1db3e1e-3a70-4735-a403-baa557b0f8a6",
-    "showInput": false
-   },
-   "source": [
-    "Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "customOutput": null,
-    "executionStartTime": 1656367515833,
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-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "torch.save(split_mod, \"trt.pt\")\n",
-    "reload_trt_mod = torch.load(\"trt.pt\")\n",
-    "reload_model_output = reload_trt_mod(*inputs)\n",
-    "\n",
-    "# Make sure the results match\n",
-    "regular_model_output = model(*inputs)\n",
-    "torch.testing.assert_close(\n",
-    "    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2\n",
-    ")"
-   ]
-  }
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diff --git a/docs/_notebooks/lenet-getting-started.html b/docs/_notebooks/lenet-getting-started.html
index 16ce55d61d..27012a117b 100644
--- a/docs/_notebooks/lenet-getting-started.html
+++ b/docs/_notebooks/lenet-getting-started.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="2f280e0eb74d46ecafdf6432a54d6ea6" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="e35ce4ca215c4513810fb9d533909cc9" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT-Getting-Started---LeNet">
 <h1>Torch-TensorRT Getting Started - LeNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---LeNet" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -641,9 +641,7 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 <li><p><a class="reference external" href="#2">Creating TorchScript modules</a></p></li>
 <li><p><a class="reference external" href="#3">Compiling with Torch-TensorRT</a></p></li>
 </ol>
-<blockquote>
-<div><p>## 1. Requirements</p>
-</div></blockquote>
+<p>## 1. Requirements</p>
 <p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this notebook.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
@@ -746,9 +744,7 @@ <h2>Content<a class="headerlink" href="#Content" title="Permalink to this headli
 +-----------------------------------------------------------------------------+
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 2. Creating TorchScript modules</p>
-</div></blockquote>
+<p>## 2. Creating TorchScript modules</p>
 <p>Here we create two submodules for a feature extractor and a classifier and stitch them together in a single LeNet module. In this case this is overkill but modules give us granular control over our program including where we decide to optimize and where we don’t. It is also the unit that the TorchScript compiler operates on. So you can decide to only convert/optimize the feature extractor and leave the classifier in standard PyTorch or you can convert the whole thing. When compiling your module
 to TorchScript, there are two paths: Tracing and Scripting.</p>
 <div class="nbinput nblast docutils container">
@@ -1037,9 +1033,7 @@ <h3>Scripting<a class="headerlink" href="#Scripting" title="Permalink to this he
 Average batch time: 5.56 ms
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 3. Compiling with Torch-TensorRT</p>
-</div></blockquote>
+<p>## 3. Compiling with Torch-TensorRT</p>
 </section>
 <section id="TorchScript-traced-model">
 <h3>TorchScript traced model<a class="headerlink" href="#TorchScript-traced-model" title="Permalink to this headline">¶</a></h3>
diff --git a/docs/_notebooks/ssd-object-detection-demo.html b/docs/_notebooks/ssd-object-detection-demo.html
index 8e7ad225a9..ad0fcb17bb 100644
--- a/docs/_notebooks/ssd-object-detection-demo.html
+++ b/docs/_notebooks/ssd-object-detection-demo.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="3185e4b1941b4216b8d84f22f38f5cf7" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="aa77691fd7754170ac652a03d651d0b9" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Object-Detection-with-Torch-TensorRT-(SSD)">
 <h1>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#Object-Detection-with-Torch-TensorRT-(SSD)" title="Permalink to this headline">¶</a></h1>
 <hr class="docutils" />
@@ -647,9 +647,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 <li><p><a class="reference external" href="#7">Conclusion</a></p></li>
 </ol>
 <hr class="docutils" />
-<blockquote>
-<div><p>## 1. Requirements</p>
-</div></blockquote>
+<p>## 1. Requirements</p>
 <p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this demo notebook.</p>
 <p>In addition to that, run the following cell to obtain additional libraries specific to this demo.</p>
 <div class="nbinput docutils container">
@@ -767,9 +765,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<blockquote>
-<div><p>## 2. SSD</p>
-</div></blockquote>
+<p>## 2. SSD</p>
 <section id="Single-Shot-MultiBox-Detector-model-for-object-detection">
 <h3>Single Shot MultiBox Detector model for object detection<a class="headerlink" href="#Single-Shot-MultiBox-Detector-model-for-object-detection" title="Permalink to this headline">¶</a></h3>
 <table class="docutils align-default">
@@ -1082,9 +1078,7 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<blockquote>
-<div><p>## 3. Creating TorchScript modules</p>
-</div></blockquote>
+<p>## 3. Creating TorchScript modules</p>
 <p>To compile with Torch-TensorRT, the model must first be in <strong>TorchScript</strong>. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: <strong>Tracing</strong> and <strong>Scripting</strong>. - Tracing follows execution of PyTorch generating ops in TorchScript corresponding to
 what it sees. - Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do.</p>
 <p>Tracing however due to its simplicity is more likely to compile successfully with Torch-TensorRT (though both systems are supported).</p>
@@ -1140,9 +1134,7 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<blockquote>
-<div><p>## 4. Compiling with Torch-TensorRT TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.</p>
-</div></blockquote>
+<p>## 4. Compiling with Torch-TensorRT TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
@@ -1181,9 +1173,7 @@ <h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Perm
 </pre></div></div>
 </div>
 <hr class="docutils" />
-<blockquote>
-<div><p>## 5. Running Inference</p>
-</div></blockquote>
+<p>## 5. Running Inference</p>
 <p>Next, we run object detection</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
diff --git a/docs/_notebooks/vgg-qat.html b/docs/_notebooks/vgg-qat.html
index 588e574d5c..6e29fe90a0 100644
--- a/docs/_notebooks/vgg-qat.html
+++ b/docs/_notebooks/vgg-qat.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="f4642ae2930e4f81bafc0d2cea4c2431" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="ea58b17a1b2146f390fb14f89011e298" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT">
 <h1>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -635,9 +635,7 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 <li><p><a class="reference external" href="#8">Inference using Torch-TensorRT</a></p></li>
 <li><p><a class="reference external" href="#8">References</a></p></li>
 </ol>
-<blockquote>
-<div><p>## 1. Requirements Please install the required dependencies and import these libraries accordingly</p>
-</div></blockquote>
+<p>## 1. Requirements Please install the required dependencies and import these libraries accordingly</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
@@ -685,12 +683,8 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 2.1.0
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="67bfeaf143e44c928a91cc8c029d57f2" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
-</div></blockquote>
-<blockquote>
-<div><p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
-</div></blockquote>
+<p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="0f5a0c4917974040a2d4b6035bb1c98a" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
+<p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -986,9 +980,7 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 Checkpoint saved
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 4. Apply Quantization</p>
-</div></blockquote>
+<p>## 4. Apply Quantization</p>
 <p><code class="docutils literal notranslate"><span class="pre">quant_modules.initialize()</span></code> will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code>, <code class="docutils literal notranslate"><span class="pre">QuantLinear</span></code> and <code class="docutils literal notranslate"><span class="pre">QuantPooling</span></code> will be called. <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> basically wraps quantizer nodes around inputs and weights of regular <code class="docutils literal notranslate"><span class="pre">Conv2d</span></code>. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. A <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> is represented in
 <code class="docutils literal notranslate"><span class="pre">pytorch-quantization</span></code> toolkit as follows.</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def forward(self, input):
@@ -1046,9 +1038,7 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<blockquote>
-<div><p>## 5. Model Calibration</p>
-</div></blockquote>
+<p>## 5. Model Calibration</p>
 <p>The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration: <code class="docutils literal notranslate"><span class="pre">max</span></code>, <code class="docutils literal notranslate"><span class="pre">histogram</span></code> and <code class="docutils literal notranslate"><span class="pre">entropy</span></code>. We use <code class="docutils literal notranslate"><span class="pre">max</span></code> calibration technique as it is simple and effective.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -1276,9 +1266,7 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 classifier.6._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.3877, 0.5620](10) calibrator=MaxCalibrator scale=1.0 quant)
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 6. Quantization Aware Training</p>
-</div></blockquote>
+<p>## 6. Quantization Aware Training</p>
 <p>In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an
 annealing learning-rate. Please refer to Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. During finetuning with QAT, the quantization is applied as a composition of <code class="docutils literal notranslate"><span class="pre">max</span></code>, <code class="docutils literal notranslate"><span class="pre">clamp</span></code>, <code class="docutils literal notranslate"><span class="pre">round</span></code> and <code class="docutils literal notranslate"><span class="pre">mul</span></code> ops.</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span># amax is absolute maximum value for an input
@@ -1328,10 +1316,8 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 Checkpoint saved
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">https://pytorch.org/docs/stable/jit.html</a>. Setting <code class="docutils literal notranslate"><span class="pre">quant_nn.TensorQuantizer.use_fb_fake_quant</span> <span class="pre">=</span> <span class="pre">True</span></code> enables the QAT model to use <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_channel_affine</span></code> operators instead of <code class="docutils literal notranslate"><span class="pre">tensor_quant</span></code> function to export quantization operators. In torchscript, they</p>
-</div></blockquote>
-<p>are represented as <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_channel_affine</span></code>.</p>
+<p>## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">https://pytorch.org/docs/stable/jit.html</a>. Setting <code class="docutils literal notranslate"><span class="pre">quant_nn.TensorQuantizer.use_fb_fake_quant</span> <span class="pre">=</span> <span class="pre">True</span></code> enables the QAT model to use <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_channel_affine</span></code> operators instead of <code class="docutils literal notranslate"><span class="pre">tensor_quant</span></code> function to export quantization operators. In torchscript, they
+are represented as <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_channel_affine</span></code>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
@@ -1489,10 +1475,8 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 E1109 04:02:37.488986 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 8. Inference using Torch-TensorRT In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; <code class="docutils literal notranslate"><span class="pre">QuantizeLayer</span></code> and <code class="docutils literal notranslate"><span class="pre">DequantizeLayer</span></code>. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set</p>
-</div></blockquote>
-<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)</span></code>. For example, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> layer from <code class="docutils literal notranslate"><span class="pre">pytorch_quantization</span></code> toolkit is represented as follows in Torchscript</p>
+<p>## 8. Inference using Torch-TensorRT In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; <code class="docutils literal notranslate"><span class="pre">QuantizeLayer</span></code> and <code class="docutils literal notranslate"><span class="pre">DequantizeLayer</span></code>. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set
+<code class="docutils literal notranslate"><span class="pre">torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)</span></code>. For example, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> layer from <code class="docutils literal notranslate"><span class="pre">pytorch_quantization</span></code> toolkit is represented as follows in Torchscript</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)
 %quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)
 %input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)
@@ -1639,9 +1623,7 @@ <h3>Performance benchmarking<a class="headerlink" href="#Performance-benchmarkin
 Average batch time: 1.79 ms
 </pre></div></div>
 </div>
-<blockquote>
-<div><p>## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using Torch-TensorRT * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics</p>
-</div></blockquote>
+<p>## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using Torch-TensorRT * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics</p>
 </section>
 </section>
 </section>
diff --git a/docs/_sources/_notebooks/getting_started_with_fx_path_module.ipynb.txt b/docs/_sources/_notebooks/getting_started_with_fx_path_module.ipynb.txt
deleted file mode 100644
index 5ee8884790..0000000000
--- a/docs/_sources/_notebooks/getting_started_with_fx_path_module.ipynb.txt
+++ /dev/null
@@ -1,441 +0,0 @@
-{
-  "metadata": {
-    "dataExplorerConfig": {},
-    "bento_stylesheets": {
-      "bento/extensions/flow/main.css": true,
-      "bento/extensions/kernel_selector/main.css": true,
-      "bento/extensions/kernel_ui/main.css": true,
-      "bento/extensions/new_kernel/main.css": true,
-      "bento/extensions/system_usage/main.css": true,
-      "bento/extensions/theme/main.css": true
-    },
-    "kernelspec": {
-      "display_name": "accelerators",
-      "language": "python",
-      "name": "bento_kernel_accelerators",
-      "metadata": {
-        "kernel_name": "bento_kernel_accelerators",
-        "nightly_builds": true,
-        "fbpkg_supported": true,
-        "cinder_runtime": false,
-        "is_prebuilt": true
-      }
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3"
-    },
-    "last_server_session_id": "42b65868-6af0-4f04-bf2f-b7e2511f23dd",
-    "last_kernel_id": "a08a7dfc-0fcc-4486-a2d5-604483260888",
-    "last_base_url": "https://devgpu005.ftw6.facebook.com:8093/",
-    "last_msg_id": "3f4cd9a4-65001843cf56aec954e05889_80",
-    "outputWidgetContext": {}
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "aac0295c-e26e-45cb-b1b6-7796ee860152",
-        "showInput": false,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "The purpose of this example is to demonstrate the overall flow of lowering a PyTorch\n",
-        "model to TensorRT via FX with existing FX based tooling. The general lowering flow would be like:\n",
-        "1. Use splitter to split the model if there're ops in the model that we don't want to lower to TensorRT for some reasons like the ops are not supported in TensorRT or running them on other backends provides better performance.\n",
-        "2. Lower the model (or part of the model if splitter is used) to TensorRT via fx path.\n",
-        "If we know the model is fully supported by fx path (without op unsupported) then we can skip the splitter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "ca68b029-68a6-42d6-968e-95bb7c1aae73",
-        "showInput": true,
-        "customInput": null,
-        "collapsed": false,
-        "requestMsgId": "f56944ff-ade2-4041-bdd6-3bce44b1405f",
-        "customOutput": null,
-        "executionStartTime": 1656367410991,
-        "executionStopTime": 1656367412604
-      },
-      "source": [
-        "import torch\n",
-        "import torch.fx\n",
-        "import torch.nn as nn\n",
-        "from torch_tensorrt.fx.utils import LowerPrecision\n",
-        "import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer\n",
-        "from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule\n",
-        "from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter"
-      ],
-      "execution_count": 1,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "8f974ab2-d187-4ffe-a09b-16cd85949be4",
-        "showInput": true,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": [],
-        "collapsed": false,
-        "requestMsgId": "564359f5-ac69-4666-91e1-41b299495ed1",
-        "customOutput": null,
-        "executionStartTime": 1656367414494,
-        "executionStopTime": 1656367422756
-      },
-      "source": [
-        "class Model(nn.Module):\n",
-        "    def __init__(self):\n",
-        "        super().__init__()\n",
-        "        self.linear = nn.Linear(10, 10)\n",
-        "        self.relu = nn.ReLU()\n",
-        "\n",
-        "    def forward(self, x):\n",
-        "        x = self.linear(x)\n",
-        "        x = self.relu(x)\n",
-        "        x = torch.linalg.norm(x, ord=2, dim=1)\n",
-        "        x = self.relu(x)\n",
-        "        return x\n",
-        "\n",
-        "\n",
-        "inputs = [torch.randn((1, 10), device=torch.device('cuda'))]\n",
-        "model = Model().cuda().eval()"
-      ],
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "0d407e92-e9e7-48aa-9c9e-1c21a9b5fd8f",
-        "showInput": false,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "acc_tracer is a custom fx tracer that maps nodes whose targets are PyTorch operators\n",
-        "to acc ops."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "a1d9c8c2-8ec7-425a-8518-6f7e53ab1e67",
-        "showInput": true,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": [],
-        "collapsed": false,
-        "requestMsgId": "ee2da608-5f1c-4f63-9927-544717e84e8a",
-        "customOutput": null,
-        "executionStartTime": 1656367480626,
-        "executionStopTime": 1656367482881
-      },
-      "source": [
-        "traced = acc_tracer.trace(model, inputs)"
-      ],
-      "execution_count": 3,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "246613eb-14b5-488e-9aae-35306fc99db1",
-        "showInput": false,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "Splitter will split the model into several submodules. The name of submodules will\n",
-        "be either `run_on_acc_{}` or `run_on_gpu_{}`. Submodules named `run_on_acc_{}` can\n",
-        "be fully lowered to TensorRT via fx2trt while submodules named `run_on_gpu_{}` has\n",
-        "unsupported ops and can't be lowered by fx2trt. We can still run `run_on_gpu_{}`\n",
-        "submodules on GPU if ops there have cuda implementation.\n",
-        ""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "1103c70a-3766-4d89-ad2f-cdcb1c3891e0",
-        "showInput": true,
-        "customInput": null,
-        "collapsed": false,
-        "requestMsgId": "feb888ea-ef9c-4577-b0c6-cf95bc1dd25e",
-        "customOutput": null,
-        "executionStartTime": 1656367487073,
-        "executionStopTime": 1656367487154
-      },
-      "source": [
-        "splitter = TRTSplitter(traced, inputs)"
-      ],
-      "execution_count": 4,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "3d65e07e-57ed-47d5-adb9-4685c69c9c6b",
-        "showInput": false,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "Preview functionality allows us to see what are the supported ops and unsupported\n",
-        "ops. We can optionally the dot graph which will color supported ops and unsupported\n",
-        "ops differently."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "6aaed2d5-61b7-438e-a72a-63f91d0709e2",
-        "showInput": true,
-        "customInput": null,
-        "collapsed": false,
-        "requestMsgId": "2948c2f8-854b-4bc2-b399-321469da320c",
-        "customOutput": null,
-        "executionStartTime": 1656367489373,
-        "executionStopTime": 1656367489556
-      },
-      "source": [
-        "splitter.node_support_preview()"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\nSupported node types in the model:\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\nacc_ops.relu: ((), {'input': torch.float32})\n\nUnsupported node types in the model:\nacc_ops.linalg_norm: ((), {'input': torch.float32})\n\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "\"\\nSupported node types in the model:\\nacc_ops.linear: ((), {'input': torch.float32, 'weight': torch.float32, 'bias': torch.float32})\\nacc_ops.relu: ((), {'input': torch.float32})\\n\\nUnsupported node types in the model:\\nacc_ops.linalg_norm: ((), {'input': torch.float32})\\n\""
-          },
-          "metadata": {
-            "bento_obj_id": "139812830161136"
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "8d8035ab-869e-4096-b8e1-3539a0cfe1af",
-        "showInput": false,
-        "customInput": null
-      },
-      "source": [
-        "After split, there are three submodules, _run_on_acc_0 and _run_on_gpu_1. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "80e03730-955a-4cc8-b071-7f92a2cff3df",
-        "showInput": true,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": [],
-        "collapsed": false,
-        "requestMsgId": "2ca46574-7176-4699-a809-2a2e2d5ffda0",
-        "customOutput": null,
-        "executionStartTime": 1656367495077,
-        "executionStopTime": 1656367495250
-      },
-      "source": [
-        "split_mod = splitter()\n",
-        "print(split_mod.graph)"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Got 2 acc subgraphs and 1 non-acc subgraphs\ngraph():\n    %x : [#users=1] = placeholder[target=x]\n    %_run_on_acc_0 : [#users=1] = call_module[target=_run_on_acc_0](args = (%x,), kwargs = {})\n    %_run_on_gpu_1 : [#users=1] = call_module[target=_run_on_gpu_1](args = (%_run_on_acc_0,), kwargs = {})\n    %_run_on_acc_2 : [#users=1] = call_module[target=_run_on_acc_2](args = (%_run_on_gpu_1,), kwargs = {})\n    return _run_on_acc_2\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "9ce75161-978e-468e-9989-ecdbc9af0d5b",
-        "showInput": true,
-        "customInput": null,
-        "collapsed": false,
-        "requestMsgId": "0370de27-39ec-4be0-826b-9aec90df1155",
-        "customOutput": null,
-        "executionStartTime": 1656367496353,
-        "executionStopTime": 1656367496452
-      },
-      "source": [
-        "print(split_mod._run_on_acc_0.graph)\n",
-        "print(split_mod._run_on_gpu_1.graph)\n",
-        "print(split_mod._run_on_acc_2.graph)"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "graph():\n    %x : [#users=1] = placeholder[target=x]\n    %linear_weight : [#users=1] = get_attr[target=linear.weight]\n    %linear_bias : [#users=1] = get_attr[target=linear.bias]\n    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), kwargs = {input: %x, weight: %linear_weight, bias: %linear_bias})\n    %relu_2 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linear_1, inplace: False})\n    return relu_2\ngraph():\n    %relu_2 : [#users=1] = placeholder[target=relu_2]\n    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), kwargs = {input: %relu_2, ord: 2, dim: 1, keepdim: False})\n    return linalg_norm_1\ngraph():\n    %linalg_norm_1 : [#users=1] = placeholder[target=linalg_norm_1]\n    %relu_3 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), kwargs = {input: %linalg_norm_1, inplace: False})\n    return relu_3\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "7a6857bc-fedd-4847-ba17-a5d114de34f3",
-        "showInput": false,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "The `split_mod` contains the child modules supported by TRT or eager gpu. We can iterate them to transform the module into TRT engine."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "174fd2eb-a864-49cf-a204-6d24a8e2849d",
-        "showInput": true,
-        "customInput": null,
-        "collapsed": false,
-        "requestMsgId": "cf7fdfe4-e781-47c8-9a9a-85b5664c10f7",
-        "customOutput": null,
-        "executionStartTime": 1656367502837,
-        "executionStopTime": 1656367510024,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "def get_submod_inputs(mod, submod, inputs):\n",
-        "    acc_inputs = None\n",
-        "\n",
-        "    def get_input(self, inputs):\n",
-        "        nonlocal acc_inputs\n",
-        "        acc_inputs = inputs\n",
-        "\n",
-        "    handle = submod.register_forward_pre_hook(get_input)\n",
-        "    mod(*inputs)\n",
-        "    handle.remove()\n",
-        "    return acc_inputs\n",
-        "\n",
-        "# Since the model is splitted into three segments. We need to lower each TRT eligible segment.\n",
-        "# If we know the model can be fully lowered, we can skip the splitter part.\n",
-        "for name, _ in split_mod.named_children():\n",
-        "    if \"_run_on_acc\" in name:\n",
-        "        submod = getattr(split_mod, name)\n",
-        "        # Get submodule inputs for fx2trt\n",
-        "        acc_inputs = get_submod_inputs(split_mod, submod, inputs)\n",
-        "\n",
-        "        # fx2trt replacement\n",
-        "        interp = TRTInterpreter(\n",
-        "            submod,\n",
-        "            InputTensorSpec.from_tensors(acc_inputs),\n",
-        "            explicit_batch_dimension=True,\n",
-        "        )\n",
-        "        r = interp.run(lower_precision=LowerPrecision.FP32)\n",
-        "        trt_mod = TRTModule(*r)\n",
-        "        setattr(split_mod, name, trt_mod)\n",
-        "\n",
-        "lowered_model_output = split_mod(*inputs)"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "I0627 150503.073 fx2trt.py:190] Run Module elapsed time: 0:00:00.014965\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "I0627 150504.996 fx2trt.py:241] Build TRT engine elapsed time: 0:00:01.922029\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "I0627 150505.026 fx2trt.py:190] Run Module elapsed time: 0:00:00.000302\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "I0627 150509.953 fx2trt.py:241] Build TRT engine elapsed time: 0:00:04.925192\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "originalKey": "f1db3e1e-3a70-4735-a403-baa557b0f8a6",
-        "showInput": false,
-        "customInput": null,
-        "code_folding": [],
-        "hidden_ranges": []
-      },
-      "source": [
-        "Model can be saved by torch.save and loaded with torch.load. Then we can compare the results with eager mode inference. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "originalKey": "a7c4fa0f-cac6-4959-8fa6-13b3455137d3",
-        "showInput": true,
-        "customInput": null,
-        "collapsed": false,
-        "requestMsgId": "f0c264ac-2bda-4c8e-a236-e2bd475e601e",
-        "customOutput": null,
-        "executionStartTime": 1656367515833,
-        "executionStopTime": 1656367516184
-      },
-      "source": [
-        "torch.save(split_mod, \"trt.pt\")\n",
-        "reload_trt_mod = torch.load(\"trt.pt\")\n",
-        "reload_model_output = reload_trt_mod(*inputs)\n",
-        "\n",
-        "# Make sure the results match\n",
-        "regular_model_output = model(*inputs)\n",
-        "torch.testing.assert_close(\n",
-        "    reload_model_output, regular_model_output, atol=3e-3, rtol=1e-2\n",
-        ")"
-      ],
-      "execution_count": 9,
-      "outputs": []
-    }
-  ]
-}
diff --git a/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt b/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
deleted file mode 100644
index 9f15616e28..0000000000
--- a/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
+++ /dev/null
@@ -1,304 +0,0 @@
-.. user_guide:
-Torch-TensorRT (FX Path) User Guide 
-========================
-Torch-TensorRT (FX Path) is a tool that can convert a PyTorch model through torch.FX to an TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine developed by Nvidia which composed of various kinds of optimization including kernel fusion, graph optimization, low precision, etc..
-This tool is developed in Python environment providing most usability to researchers and engineers. There are a few stages that a user want to use this tool and we will introduce them here. 
- 
-Installation
-------------
-* Method 1. Follow the instrucions for Torch-TensorRT
-* Method 2. To install FX path only (Python path) and avoid the C++ build for torchscript path
-
-.. code-block:: shell
-
-    $ conda create --name python_env python=3.8
-    $ conda activate python_env
-
-    # Recommend to install PyTorch 1.12 and later
-    $ conda install pytorch torchvision torchtext cudatoolkit=11.3 -c pytorch-nightly
-
-    # Install TensorRT python package
-    $ pip3 install nvidia-pyindex
-    $ pip3 install nvidia-tensorrt==8.2.4.2
-    $ git clone https://github.com/pytorch/TensorRT.git
-    $ cd TensorRT/py && python setup.py install --fx-only && cd ..
-
-    $ pyton -c "import torch_tensorrt.fx"
-    # Test an example by 
-    $ python py/torch_tensorrt/fx/example/lower_example.py
- 
-
-Converting a PyTorch Model to TensorRT Engine
----------------------------------------------
-We will go through an example to illustrate the major steps that FX path uses to 
-
-* **Step 1: Trace the model with acc_tracer**
-Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
-
-.. code-block:: shell
-
-   import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
-
-   # Build the model which needs to be a PyTorch nn.Module. 
-   my_pytorch_model = build_model()
-
-   # Prepare inputs to the model. Inputs have to be a List of Tensors
-   inputs = [Tensor, Tensor, ...]
-
-   # Trace the model with acc_tracer.
-   acc_mod = acc_tracer.trace(my_pytorch_model, inputs)
-
-*Common Errors:*
-  
-symbolically traced variables cannot be used as inputs to control flow
-This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in `FX guide <https://pytorch.org/docs/stable/fx.html#dynamic-control-flow>`_.
-
-* **Step 2: Build TensorRT engine**
-There are `two different modes <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch>`_ for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set ``explicit_batch_dimension=False`` in ``lower_to_trt()``. We do not recommend to use it since it will lack of support in future TensorRT versions. 
-
-Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable ``dynamic_batch`` in ``lower_to_trt()`` if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
-1. Shapes of inputs, outputs and activations are fixed except batch dimension.
-2. Inputs, outputs and activations have batch dimension as the major dimension.
-3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).
-
-For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range. 
-
-.. code-block:: shell
-
-    import deeplearning.trt.fx2trt.converter.converters
-    from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
-
-    # InputTensorSpec is a dataclass we use to store input information.
-    # There're two ways we can build input_specs.
-    # Option 1, build it manually.
-    input_specs = [
-      InputTensorSpec(shape=(1, 2, 3), dtype=torch.float32),
-      InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
-    ]
-    # Option 2, build it using sample_inputs where user provide a sample
-    inputs = [
-    torch.rand((1,2,3), dtype=torch.float32),
-    torch.rand((1,4,5), dtype=torch.float32),
-    ]
-    input_specs = InputTensorSpec.from_tensors(inputs)
-
-    # IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.
-    input_specs = [
-        InputTensorSpec(
-            shape=(-1, 2, 3), 
-            dtype=torch.float32,
-            # Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models
-            # (min_shape, optimize_target_shape, max_shape)
-            # For more information refer to fx/input_tensor_spec.py
-            shape_ranges = [
-                ((1, 2, 3), (4, 2, 3), (100, 2, 3)),
-            ],
-        ),
-        InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
-    ]
-
-    # Build a TRT interpreter. Set explicit_batch_dimension accordingly.
-    interpreter = TRTInterpreter(
-        acc_mod, input_specs, explicit_batch_dimension=True/False
-    )
-
-    # The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.
-    # The TRTInterpreterResult contains required parameter to build TRTModule, 
-    # and other informational output from TRTInterpreter run.
-    class TRTInterpreterResult(NamedTuple):
-        engine: Any
-        input_names: Sequence[str]
-        output_names: Sequence[str]
-        serialized_cache: bytearray
-
-    #max_batch_size: set accordingly for maximum batch size you will use.
-    #max_workspace_size: set to the maximum size we can afford for temporary buffer
-    #lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).
-    #sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity
-    #force_fp32_output: force output to be fp32
-    #strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.
-    #algorithm_selector: set up algorithm selection for certain layer
-    #timing_cache: enable timing cache for TensorRT
-    #profiling_verbosity: TensorRT logging level
-    trt_interpreter_result = interpreter.run(
-        max_batch_size=64,
-        max_workspace_size=1 << 25,     
-        sparse_weights=False,
-        force_fp32_output=False,
-        strict_type_constraints=False,
-        algorithm_selector=None,
-        timing_cache=None,
-        profiling_verbosity=None,
-    )
-
-
-*Common Errors:*
-
-RuntimeError: Conversion of function xxx not currently supported!
-- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.
-
-* **Step 3: Run the model**
-One way is using TRTModule, which is basically a PyTorch nn.Module.
-
-.. code-block:: shell
-
-    from torch_tensorrt.fx import TRTModule
-    mod = TRTModule(
-        trt_interpreter_result.engine, 
-        trt_interpreter_result.input_names, 
-        trt_interpreter_result.output_names)
-    # Just like all other PyTorch modules
-    outputs = mod(*inputs)
-    torch.save(mod, "trt.pt")
-    reload_trt_mod = torch.load("trt.pt")
-    reload_model_output = reload_trt_mod(*inputs)
-
-So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph's operation to corresponding TensorRT operation and build up the TensoRT engine for it. 
-
-Acc Tracer
----------
-
-Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:
-
-1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write. 
-2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
-
-FX2TRT
---------
-After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
-
-Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
-
-.. code-block:: shell
-
-    @tensorrt_converter(acc_ops.sigmoid)
-    def acc_ops_sigmoid(network, target, args, kwargs, name):
-        """
-        network: TensorRT network. We'll be adding layers to it.
-        
-        The rest arguments are attributes of fx node.
-        """
-        input_val = kwargs['input']
-
-        if not isinstance(input_val, trt.tensorrt.ITensor):
-            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
-                            'of the TensorRT region!')
-
-        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
-        layer.name = name
-        return layer.get_output(0)
-
-How to Add a Missing Op
-****************
-
-You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer. 
-
-* **Step 1. Add a new acc op**
-
-TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.
-
-In `acc tracer <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py>`_, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.  
-
-In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.
-
-Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py>`_. e.g. the following code adds an acc op named foo() which adds two given inputs.
-
-.. code-block:: shell
-
-    # NOTE: all acc ops should only take kwargs as inputs, therefore we need the "*"
-    # at the beginning.
-    @register_acc_op
-    def foo(*, input, other, alpha):
-        return input + alpha * other
- 
-There’re two ways to register a mapping. One is `register_acc_op_mapping() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164>`_. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.
- 
-.. code-block:: shell
-
-    this_arg_is_optional = True
-
-    @register_acc_op_mapping(
-        op_and_target=("call_function", torch.add),
-        arg_replacement_tuples=[
-            ("input", "input"),
-            ("other", "other"),
-            ("alpha", "alpha", this_arg_is_optional),
-        ],
-    )
-    @register_acc_op
-    def foo(*, input, other, alpha=1.0):
-        return input + alpha * other    
- 
-``op_and_target`` determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the ``op_and_target``, we will trigger the mapping. Since we want to map from ``torch.add``, then op would be call_function and target would be ``torch.add``. ``arg_replacement_tuples`` determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in ``arg_replacement_tuples`` represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in *original node*. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
-We don’t have to specify arg_replacement_tuples if none of the followings are true.
-
-1. kwargs of original nodes and acc op nodes have different name.
-2. there’re optional arguments.
-
-The other way to register a mapping is through `register_custom_acc_mapper_fn() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206>`_. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.
-
-.. code-block:: shell
-
-    @register_acc_op
-    def foo(*, input, other, alpha=1.0):
-        return input + alpha * other
-
-    @register_custom_acc_mapper_fn(
-        op_and_target=("call_function", torch.add),
-        arg_replacement_tuples=[
-            ("input", "input"),
-            ("other", "other"),
-            ("alpha", "alpha", this_arg_is_optional),
-        ],
-    )
-    def custom_mapper(node: torch.fx.Node, _: nn.Module) -> torch.fx.Node:
-        """
-        `node` is original node, which is a call_function node with target
-        being torch.add.
-        """
-        alpha = 1
-        if "alpha" in node.kwargs:
-            alpha = node.kwargs["alpha"]
-        foo_kwargs = {"input": node["input"], "other": node["other"], "alpha": alpha}
-        with node.graph.inserting_before(node):
-            foo_node = node.graph.call_function(foo, kwargs=foo_kwargs)
-            foo_node.meta = node.meta.copy()
-            return foo_node
- 
-
-In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379>`_.
-
-The last step would be *adding unit test* for the new acc op or mapper function we added. The place to add the unit test is here `test_acc_tracer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py>`_.
-
-* **Step 2. Add a new fx2trt converter**
-
-All the developed converters for acc ops are all in `acc_op_converter.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py>`_. It could give you a good example of how the converter is added. 
-
-Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using `TensorRT APIs <https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html>`_.
-
-.. code-block:: shell
-
-    @tensorrt_converter(acc_ops.sigmoid)
-    def acc_ops_sigmoid(network, target, args, kwargs, name):
-        """
-        network: TensorRT network. We'll be adding layers to it.
-        
-        The rest arguments are attributes of fx node.
-        """
-        input_val = kwargs['input']
-
-        if not isinstance(input_val, trt.tensorrt.ITensor):
-            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
-                            'of the TensorRT region!')
-
-        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
-        layer.name = name
-        return layer.get_output(0)
- 
-We need to use ``tensorrt_converter`` decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is `acc_ops.sigmoid` which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.
-
-**What if we can not find corresponding layers in TensorRT that do the same thing as the node.**
-
-In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. *We have not implement this feature yet. We will update once it is enabled*.
-
-Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this `folder <https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op>`_. 
diff --git a/docs/contributors/conversion.html b/docs/contributors/conversion.html
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+++ b/docs/contributors/conversion.html
@@ -199,7 +199,7 @@
               
               
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-                  master (1.2.0a0+3a8704db)
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@@ -348,7 +348,16 @@
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
- <a href="#D"><strong>D</strong></a>
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
  | <a href="#P"><strong>P</strong></a>
  | <a href="#R"><strong>R</strong></a>
  | <a href="#S"><strong>S</strong></a>
@@ -357,14 +366,197 @@ <h1 id="index">Index</h1>
  | <a href="#X"><strong>X</strong></a>
  
 </div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.__init__">__init__() (torch_tensorrt.Device method)</a>
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">(torch_tensorrt.Input method)</a>
+</li>
+        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">(torch_tensorrt.ptq.CacheCalibrator method)</a>
+</li>
+        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">(torch_tensorrt.ptq.DataLoaderCalibrator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.allow_gpu_fallback">allow_gpu_fallback (torch_tensorrt.Device attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#id1">CacheCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[1]</a>
+</li>
+      <li><a href="py_api/ptq.html#id2">CalibrationAlgo (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[1]</a>
+</li>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.check_method_op_support">check_method_op_support() (in module torch_tensorrt.ts)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.compile">compile() (in module torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/ts.html#torch_tensorrt.ts.compile">(in module torch_tensorrt.ts)</a>
+</li>
+      </ul></li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">convert_method_to_trt_engine() (in module torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/ts.html#torch_tensorrt.ts.convert_method_to_trt_engine">(in module torch_tensorrt.ts)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
 <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#id0">DataLoaderCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[1]</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.debug">debug (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Debug">Debug (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device">Device (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.device_type">device_type (torch_tensorrt.Device attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.DeviceType">DeviceType (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.dla_core">dla_core (torch_tensorrt.Device attribute)</a>
+</li>
       <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Documentation"><strong>Documentation</strong></a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dtype">dtype (class in torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.dtype">(torch_tensorrt.Input attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dump_build_info">dump_build_info() (in module torch_tensorrt)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.embed_engine_in_new_module">embed_engine_in_new_module() (in module torch_tensorrt.ts)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.EngineCapability">EngineCapability (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">ENTROPY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
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+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">ENTROPY_CALIBRATION_2 (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
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+</li>
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+
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+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.get_build_info">get_build_info() (in module torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">get_cache_mode_batch() (in module torch_tensorrt.ptq)</a>
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+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_is_colored_output_on">get_is_colored_output_on() (in module torch_tensorrt.logging)</a>
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+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_logging_prefix">get_logging_prefix() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_reportable_log_level">get_reportable_log_level() (in module torch_tensorrt.logging)</a>
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+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.gpu_id">gpu_id (torch_tensorrt.Device attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Graph">Graph (torch_tensorrt.logging.Level attribute)</a>
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+      <li><a href="py_api/logging.html#torch_tensorrt.logging.graphs">graphs (class in torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
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+<h2 id="I">I</h2>
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+      <li><a href="py_api/logging.html#torch_tensorrt.logging.info">info (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Info">Info (torch_tensorrt.logging.Level attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input">Input (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.internal_errors">internal_errors (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.InternalError">InternalError (torch_tensorrt.logging.Level attribute)</a>
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+  </ul></td>
+</tr></table>
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+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
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+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">LEGACY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
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+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level">Level (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.log">log() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
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+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">MINMAX_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
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+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">torch_tensorrt</a>
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+        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">torch_tensorrt.ptq</a>
+</li>
+        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">torch_tensorrt.ts</a>
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+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.set_device">set_device() (in module torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_is_colored_output_on">set_is_colored_output_on() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_logging_prefix">set_logging_prefix() (in module torch_tensorrt.logging)</a>
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+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_reportable_log_level">set_reportable_log_level() (in module torch_tensorrt.logging)</a>
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+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.shape">shape (torch_tensorrt.Input attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.shape_mode">shape_mode (torch_tensorrt.Input attribute)</a>
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+</li>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.TensorRTCompileSpec">TensorRTCompileSpec() (in module torch_tensorrt.ts)</a>
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+      <li>
+    torch_tensorrt
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">module</a>
+</li>
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+    torch_tensorrt.logging
+
+      <ul>
+        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">module</a>
+</li>
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+      <li>
+    torch_tensorrt.ptq
+
+      <ul>
+        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.ts
+
+      <ul>
+        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">module</a>
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 </li>
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 <table style="width: 100%" class="indextable genindextable"><tr>
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+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Warning">Warning (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.warnings">warnings (class in torch_tensorrt.logging)</a>
+</li>
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+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.write_calibration_cache">write_calibration_cache() (in module torch_tensorrt.ptq)</a>
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diff --git a/docs/py_api/logging.html b/docs/py_api/logging.html
index dd0441ea8d..acb919ee82 100644
--- a/docs/py_api/logging.html
+++ b/docs/py_api/logging.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -353,6 +353,207 @@
               
   <section id="torch-tensorrt-logging">
 <span id="torch-tensorrt-logging-py"></span><h1>torch_tensorrt.logging<a class="headerlink" href="#torch-tensorrt-logging" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.logging"></span><dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">Level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#Level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.Level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
+<p>Enum to set the minimum required logging level to print a message to stdout</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Debug">
+<span class="sig-name descname"><span class="pre">Debug</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.DEBUG:</span> <span class="pre">4&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Debug" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Error">
+<span class="sig-name descname"><span class="pre">Error</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.ERROR:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Error" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Graph">
+<span class="sig-name descname"><span class="pre">Graph</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.GRAPH:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Graph" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Info">
+<span class="sig-name descname"><span class="pre">Info</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INFO:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Info" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.InternalError">
+<span class="sig-name descname"><span class="pre">InternalError</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INTERNAL_ERROR:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.InternalError" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Warning">
+<span class="sig-name descname"><span class="pre">Warning</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.WARNING:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Warning" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.debug">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">debug</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#debug"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.debug" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display full debug information through the logger</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.debug():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id1"><span class="problematic" id="id2">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.errors">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.errors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just errors and above</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_is_colored_output_on">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_is_colored_output_on</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get if colored output is enabled for logging</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>If colored output is one</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_logging_prefix">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_logging_prefix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_logging_prefix" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the prefix set for logging messages</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Prefix used for logger</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_reportable_log_level">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_reportable_log_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_reportable_log_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the level required for a message to be printed in the log</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>The enum representing the level required to print</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level">torch_tensorrt.logging.Level</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.graphs">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">graphs</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#graphs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.graphs" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display the results of intermediate lowering passes
+as well as full debug information through the logger</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.graphs():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id3"><span class="problematic" id="id4">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.info">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">info</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display all info and greater severity messages</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.info():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id5"><span class="problematic" id="id6">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.internal_errors">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">internal_errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#internal_errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.internal_errors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just internal errors</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.internal_errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.log">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.log" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add a new message to the log</p>
+<p>Adds a new message to the log at a specified level. The message
+will only get printed out if Level &gt; reportable_log_level</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>torch_tensorrt.logging.Level</em></a>) – Severity of the message</p></li>
+<li><p><strong>msg</strong> (<em>str</em>) – Actual message text</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_is_colored_output_on">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_is_colored_output_on</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">colored_output_on</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enable or disable color in the log output</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>colored_output_on</strong> (<em>bool</em>) – If colored output should be enabled or not</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_logging_prefix">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_logging_prefix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_logging_prefix" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the prefix used when logging messages</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – Prefix to use for logging messages</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_reportable_log_level">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_reportable_log_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_reportable_log_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the level required for a message to be printed to the log</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>torch_tensorrt.logging.Level</em></a>) – The enum representing the level required to print</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.warnings">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">warnings</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.warnings" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just warnings and above</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.warnings():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id7"><span class="problematic" id="id8">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
 </section>
 
 
diff --git a/docs/py_api/ptq.html b/docs/py_api/ptq.html
index 0a06f36bff..b01d1294ff 100644
--- a/docs/py_api/ptq.html
+++ b/docs/py_api/ptq.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -353,11 +353,121 @@
               
   <section id="torch-tensorrt-ptq">
 <span id="torch-tensorrt-ptq-py"></span><h1>torch_tensorrt.ptq<a class="headerlink" href="#torch-tensorrt-ptq" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.ptq"></span><dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
+:param cache_file: path to cache file.
+:param algo_type: choice of calibration algorithm.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
+<p>An enumeration.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">
+<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">
+<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION_2</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION_2:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">
+<span class="sig-name descname"><span class="pre">LEGACY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.LEGACY_CALIBRATION:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">
+<span class="sig-name descname"><span class="pre">MINMAX_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.MINMAX_CALIBRATION:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
+data which is passed during calibration.
+:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
+:param algo_type: choice of calibration algorithm.
+:param cache_file: path to cache file.
+:param use_cache: flag which enables usage of pre-existing cache.
+:param device: device on which calibration data is copied to.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch_size">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch_size" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_cache_mode_batch">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_cache_mode_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_cache_mode_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_cache_mode_batch" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.read_calibration_cache">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">read_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#read_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.read_calibration_cache" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.write_calibration_cache">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">write_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#write_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.write_calibration_cache" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <section id="classes">
 <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="id0">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
+data which is passed during calibration.
+:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
+:param algo_type: choice of calibration algorithm.
+:param cache_file: path to cache file.
+:param use_cache: flag which enables usage of pre-existing cache.
+:param device: device on which calibration data is copied to.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="id1">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id1" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
+:param cache_file: path to cache file.
+:param algo_type: choice of calibration algorithm.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </section>
 <section id="enums">
 <h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="id2">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id2" title="Permalink to this definition">¶</a></dt>
+<dd><p>An enumeration.</p>
+</dd></dl>
+
 </section>
 </section>
 
diff --git a/docs/py_api/torch_tensorrt.html b/docs/py_api/torch_tensorrt.html
index 7bf4918d7c..91ded448bc 100644
--- a/docs/py_api/torch_tensorrt.html
+++ b/docs/py_api/torch_tensorrt.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -351,14 +351,350 @@
               
   <section id="torch-tensorrt">
 <span id="torch-tensorrt-py"></span><h1>torch_tensorrt<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</a></h1>
-<section id="functions">
+<span class="target" id="module-torch_tensorrt"></span><section id="functions">
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.set_device">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gpu_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.set_device" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a PyTorch module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing PyTorch module and a set of settings to configure the compiler
+and using the path specified in <code class="docutils literal notranslate"><span class="pre">ir</span></code> lower and compile the module to TensorRT
+returning a PyTorch Module back</p>
+<p>Converts specifically the forward method of a Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
+<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.nn.Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.convert_method_to_trt_engine">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
+<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
+<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>bytes</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.get_build_info">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">get_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#get_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.get_build_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a string containing the build information of torch_tensorrt distribution</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>String containing the build information for torch_tensorrt distribution</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.dump_build_info">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dump_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#dump_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dump_build_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Prints build information about the torch_tensorrt distribution to stdout</p>
+</dd></dl>
+
 </section>
 <section id="classes">
 <h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.Input">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input" title="Permalink to this definition">¶</a></dt>
+<dd><p>Defines an input to a module in terms of expected shape, data type and tensor format.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Variables</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape_mode</strong> (<em>torch_tensorrt.Input._ShapeMode</em>) – Is input statically or dynamically shaped</p></li>
+<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>Dict</em>) – <p>Either a single Tuple or a dict of tuples defining the input shape.
+Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form
+<a href="#id1"><span class="problematic" id="id2">``</span></a>{</p>
+<blockquote>
+<div><p>”min_shape”: Tuple,
+“opt_shape”: Tuple,
+“max_shape”: Tuple</p>
+</div></blockquote>
+<p>}``</p>
+</p></li>
+<li><p><strong>dtype</strong> (<em>torch_tensorrt.dpython:type</em>) – The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</p></li>
+<li><p><strong>format</strong> (<a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.__init__" title="Permalink to this definition">¶</a></dt>
+<dd><p>__init__ Method for torch_tensorrt.Input</p>
+<p>Input accepts one of a few construction patterns</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p></li>
+<li><p><strong>min_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Min size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>opt_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Opt size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>max_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Max size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>dtype</strong> (<em>torch.dpython:type</em><em> or </em><em>torch_tensorrt.dpython:type</em>) – Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</p></li>
+<li><p><strong>format</strong> (<em>torch.memory_format</em><em> or </em><a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<ul class="simple">
+<li><p>Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</p></li>
+<li><p>Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</p></li>
+<li><p>Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.dtype">
+<span class="sig-name descname"><span class="pre">dtype</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;dtype.unknown:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.dtype" title="Permalink to this definition">¶</a></dt>
+<dd><p>torch_tensorrt.dtype.float32)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>The expected data type of the input tensor (default</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.format">
+<span class="sig-name descname"><span class="pre">format</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;TensorFormat.contiguous:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.format" title="Permalink to this definition">¶</a></dt>
+<dd><p>torch_tensorrt.TensorFormat.NCHW)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>The expected format of the input tensor (default</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.shape">
+<span class="sig-name descname"><span class="pre">shape</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape" title="Permalink to this definition">¶</a></dt>
+<dd><p>Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form <code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">&quot;min_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;opt_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;max_shape&quot;:</span> <span class="pre">Tuple</span> <span class="pre">}</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>(Tuple or Dict)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.shape_mode">
+<span class="sig-name descname"><span class="pre">shape_mode</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape_mode" title="Permalink to this definition">¶</a></dt>
+<dd><p>Is input statically or dynamically shaped</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>(torch_tensorrt.Input._ShapeMode)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.Device">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device" title="Permalink to this definition">¶</a></dt>
+<dd><p>Defines a device that can be used to specify target devices for engines</p>
+<dl class="field-list simple">
+<dt class="field-odd">Variables</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device_type</strong> (<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType"><em>torch_tensorrt.DeviceType</em></a>) – Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p></li>
+<li><p><strong>gpu_id</strong> (<em>python:int</em>) – Device ID for target GPU</p></li>
+<li><p><strong>dla_core</strong> (<em>python:int</em>) – Core ID for target DLA core</p></li>
+<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Whether falling back to GPU if DLA cannot support an op should be allowed</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device.__init__" title="Permalink to this definition">¶</a></dt>
+<dd><p>__init__ Method for torch_tensorrt.Device</p>
+<p>Device accepts one of a few construction patterns</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>spec</strong> (<em>str</em>) – String with device spec e.g. “dla:0” for dla, core_id 0</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>gpu_id</strong> (<em>python:int</em>) – ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</p></li>
+<li><p><strong>dla_core</strong> (<em>python:int</em>) – ID of target DLA core. If specified, no positional arguments should be provided.</p></li>
+<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<ul class="simple">
+<li><p>Device(“gpu:1”)</p></li>
+<li><p>Device(“cuda:1”)</p></li>
+<li><p>Device(“dla:0”, allow_gpu_fallback=True)</p></li>
+<li><p>Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</p></li>
+<li><p>Device(dla_core=0, allow_gpu_fallback=True)</p></li>
+<li><p>Device(gpu_id=1)</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.allow_gpu_fallback">
+<span class="sig-name descname"><span class="pre">allow_gpu_fallback</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#torch_tensorrt.Device.allow_gpu_fallback" title="Permalink to this definition">¶</a></dt>
+<dd><p>(bool) Whether falling back to GPU if DLA cannot support an op should be allowed</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.device_type">
+<span class="sig-name descname"><span class="pre">device_type</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Device.device_type" title="Permalink to this definition">¶</a></dt>
+<dd><p>Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>(<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType">torch_tensorrt.DeviceType</a>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.dla_core">
+<span class="sig-name descname"><span class="pre">dla_core</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.dla_core" title="Permalink to this definition">¶</a></dt>
+<dd><p>(int) Core ID for target DLA core</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.gpu_id">
+<span class="sig-name descname"><span class="pre">gpu_id</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.gpu_id" title="Permalink to this definition">¶</a></dt>
+<dd><p>(int) Device ID for target GPU</p>
+</dd></dl>
+
+</dd></dl>
+
 </section>
 <section id="enums">
 <h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.dtype">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#torch_tensorrt.dtype" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specifiy operating precision for engine execution</p>
+<p>Members:</p>
+<blockquote>
+<div><p>float : 32 bit floating point number</p>
+<p>float32 : 32 bit floating point number</p>
+<p>half : 16 bit floating point number</p>
+<p>float16 : 16 bit floating point number</p>
+<p>int8 : 8 bit integer number</p>
+<p>int32 : 32 bit integer number</p>
+<p>bool : Boolean value</p>
+<p>unknown : Unknown data type</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.DeviceType">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">DeviceType</span></span><a class="headerlink" href="#torch_tensorrt.DeviceType" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specify device kinds to build TensorRT engines for</p>
+<p>Members:</p>
+<blockquote>
+<div><p>GPU : Specify using GPU to execute TensorRT Engine</p>
+<p>DLA : Specify using DLA to execute TensorRT Engine (Jetson Only)</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.EngineCapability">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">EngineCapability</span></span><a class="headerlink" href="#torch_tensorrt.EngineCapability" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specify engine capability settings (selections of kernels to meet safety requirements)</p>
+<p>Members:</p>
+<blockquote>
+<div><p>safe_gpu : Use safety GPU kernels only</p>
+<p>safe_dla : Use safety DLA kernels only</p>
+<p>default : Use default behavior</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.TensorFormat">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">TensorFormat</span></span><a class="headerlink" href="#torch_tensorrt.TensorFormat" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specifiy the memory layout of tensors</p>
+<p>Members:</p>
+<blockquote>
+<div><p>contiguous : Contiguous memory layout (NCHW / Linear)</p>
+<p>channels_last : Channels last memory layout (NHWC)</p>
+</div></blockquote>
+</dd></dl>
+
 </section>
 <section id="submodules">
 <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
diff --git a/docs/py_api/ts.html b/docs/py_api/ts.html
index f87429ebda..ce71414d68 100644
--- a/docs/py_api/ts.html
+++ b/docs/py_api/ts.html
@@ -199,7 +199,7 @@
               
               
                 <div class="version">
-                  master (1.2.0a0+3a8704db)
+                  master (1.2.0a0+ffedb78)
                 </div>
               
             
@@ -353,8 +353,231 @@
               
   <section id="torch-tensorrt-ts">
 <span id="torch-tensorrt-ts-py"></span><h1>torch_tensorrt.ts<a class="headerlink" href="#torch-tensorrt-ts" title="Permalink to this headline">¶</a></h1>
-<section id="functions">
+<span class="target" id="module-torch_tensorrt.ts"></span><section id="functions">
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_min_timing_iters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules=[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing TorchScript module and a set of settings to configure the compiler
+and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
+<p>Converts specifically the forward method of a TorchScript Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_min_timing_iters</strong> (<em>python:int</em>) – Number of minimization timing iterations used to select kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
+<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</p></li>
+<li><p><strong>torch_executed_ops</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+<li><p><strong>torch_executed_modules</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled TorchScript Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.jit.ScriptModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.convert_method_to_trt_engine">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_min_timing_iters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
+<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
+<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to convert</p></li>
+</ul>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_min_timing_iters</strong> (<em>python:int</em>) – Number of minimization timing iterations used to select kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>bytes</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.check_method_op_support">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">check_method_op_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#check_method_op_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.check_method_op_support" title="Permalink to this definition">¶</a></dt>
+<dd><p>Checks to see if a method is fully supported by torch_tensorrt</p>
+<p>Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators
+that are not supported are printed out and the function returns false, else true.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
+<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to check</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>True if supported Method</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.embed_engine_in_new_module">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
+<dd><p>Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</p>
+<p>Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
+Registers the forward method to execute the TensorRT engine with the function signature:</p>
+<blockquote>
+<div><p>forward(Tensor[]) -&gt; Tensor[]</p>
+</div></blockquote>
+<dl>
+<dt>TensorRT bindings must have names with the following format:</dt><dd><ul class="simple">
+<li><p>[symbol].[index in input / output array]</p></li>
+</ul>
+<p>ex.
+- [x.0, x.1, x.2] -&gt; [y.0]</p>
+</dd>
+</dl>
+<p>Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>serialized_engine</strong> (<em>bytes</em>) – Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – Target device to run engine on. Must be compatible with engine provided. Default: Current active device</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>New TorchScript module with engine embedded</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.jit.ScriptModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_min_timing_iters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch._C.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f9f791ec5b0&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
+<dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
+<dl class="field-list simple">
+<dt class="field-odd">Keyword Arguments</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_min_timing_iters</strong> (<em>python:int</em>) – Number of minimization timing iterations used to select kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
 </section>
 </section>
 
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@@ -197,7 +197,7 @@
               
               
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diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
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 <section id="code-blocks">
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 <pre class="literal-block"># parsed-literal test
-curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+3a8704db).tar-gz</pre>
+curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+ffedb78).tar-gz</pre>
 <div class="literal-block-wrapper docutils container" id="id23">
 <div class="code-block-caption"><span class="caption-text">Code Blocks can have captions.</span><a class="headerlink" href="#id23" title="Permalink to this code">¶</a></div>
 <div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="w"></span>
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
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-  <section id="torch-tensorrt-fx-path-user-guide">
-<h1>Torch-TensorRT (FX Path) User Guide<a class="headerlink" href="#torch-tensorrt-fx-path-user-guide" title="Permalink to this headline">¶</a></h1>
-<p>Torch-TensorRT (FX Path) is a tool that can convert a PyTorch model through torch.FX to an TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine developed by Nvidia which composed of various kinds of optimization including kernel fusion, graph optimization, low precision, etc..
-This tool is developed in Python environment providing most usability to researchers and engineers. There are a few stages that a user want to use this tool and we will introduce them here.</p>
-<section id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
-<ul class="simple">
-<li><p>Method 1. Follow the instrucions for Torch-TensorRT</p></li>
-<li><p>Method 2. To install FX path only (Python path) and avoid the C++ build for torchscript path</p></li>
-</ul>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ conda create --name python_env <span class="nv">python</span><span class="o">=</span><span class="m">3</span>.8
-$ conda activate python_env
-
-<span class="c1"># Recommend to install PyTorch 1.12 and later</span>
-$ conda install pytorch torchvision torchtext <span class="nv">cudatoolkit</span><span class="o">=</span><span class="m">11</span>.3 -c pytorch-nightly
-
-<span class="c1"># Install TensorRT python package</span>
-$ pip3 install nvidia-pyindex
-$ pip3 install nvidia-tensorrt<span class="o">==</span><span class="m">8</span>.2.4.2
-$ git clone https://github.com/pytorch/TensorRT.git
-$ <span class="nb">cd</span> TensorRT/py <span class="o">&amp;&amp;</span> python setup.py install --fx-only <span class="o">&amp;&amp;</span> <span class="nb">cd</span> ..
-
-$ pyton -c <span class="s2">&quot;import torch_tensorrt.fx&quot;</span>
-<span class="c1"># Test an example by</span>
-$ python py/torch_tensorrt/fx/example/lower_example.py
-</pre></div>
-</div>
-</section>
-<section id="converting-a-pytorch-model-to-tensorrt-engine">
-<h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#converting-a-pytorch-model-to-tensorrt-engine" title="Permalink to this headline">¶</a></h2>
-<p>We will go through an example to illustrate the major steps that FX path uses to</p>
-<ul class="simple">
-<li><p><strong>Step 1: Trace the model with acc_tracer</strong></p></li>
-</ul>
-<p>Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
-
-<span class="c1"># Build the model which needs to be a PyTorch nn.Module.</span>
-<span class="nv">my_pytorch_model</span> <span class="o">=</span> build_model<span class="o">()</span>
-
-<span class="c1"># Prepare inputs to the model. Inputs have to be a List of Tensors</span>
-<span class="nv">inputs</span> <span class="o">=</span> <span class="o">[</span>Tensor, Tensor, ...<span class="o">]</span>
-
-<span class="c1"># Trace the model with acc_tracer.</span>
-<span class="nv">acc_mod</span> <span class="o">=</span> acc_tracer.trace<span class="o">(</span>my_pytorch_model, inputs<span class="o">)</span>
-</pre></div>
-</div>
-<p><em>Common Errors:</em></p>
-<p>symbolically traced variables cannot be used as inputs to control flow
-This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in <a class="reference external" href="https://pytorch.org/docs/stable/fx.html#dynamic-control-flow">FX guide</a>.</p>
-<ul class="simple">
-<li><p><strong>Step 2: Build TensorRT engine</strong></p></li>
-</ul>
-<p>There are <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch">two different modes</a> for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set <code class="docutils literal notranslate"><span class="pre">explicit_batch_dimension=False</span></code> in <code class="docutils literal notranslate"><span class="pre">lower_to_trt()</span></code>. We do not recommend to use it since it will lack of support in future TensorRT versions.</p>
-<p>Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable <code class="docutils literal notranslate"><span class="pre">dynamic_batch</span></code> in <code class="docutils literal notranslate"><span class="pre">lower_to_trt()</span></code> if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
-1. Shapes of inputs, outputs and activations are fixed except batch dimension.
-2. Inputs, outputs and activations have batch dimension as the major dimension.
-3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).</p>
-<p>For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import deeplearning.trt.fx2trt.converter.converters
-from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
-
-<span class="c1"># InputTensorSpec is a dataclass we use to store input information.</span>
-<span class="c1"># There&#39;re two ways we can build input_specs.</span>
-<span class="c1"># Option 1, build it manually.</span>
-<span class="nv">input_specs</span> <span class="o">=</span> <span class="o">[</span>
-  InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
-  InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>, <span class="m">4</span>, <span class="m">5</span><span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
-<span class="o">]</span>
-<span class="c1"># Option 2, build it using sample_inputs where user provide a sample</span>
-<span class="nv">inputs</span> <span class="o">=</span> <span class="o">[</span>
-torch.rand<span class="o">((</span><span class="m">1</span>,2,3<span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
-torch.rand<span class="o">((</span><span class="m">1</span>,4,5<span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
-<span class="o">]</span>
-<span class="nv">input_specs</span> <span class="o">=</span> InputTensorSpec.from_tensors<span class="o">(</span>inputs<span class="o">)</span>
-
-<span class="c1"># IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.</span>
-<span class="nv">input_specs</span> <span class="o">=</span> <span class="o">[</span>
-    InputTensorSpec<span class="o">(</span>
-        <span class="nv">shape</span><span class="o">=(</span>-1, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>,
-        <span class="nv">dtype</span><span class="o">=</span>torch.float32,
-        <span class="c1"># Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models</span>
-        <span class="c1"># (min_shape, optimize_target_shape, max_shape)</span>
-        <span class="c1"># For more information refer to fx/input_tensor_spec.py</span>
-        <span class="nv">shape_ranges</span> <span class="o">=</span> <span class="o">[</span>
-            <span class="o">((</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>, <span class="o">(</span><span class="m">4</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">)</span>, <span class="o">(</span><span class="m">100</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">))</span>,
-        <span class="o">]</span>,
-    <span class="o">)</span>,
-    InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>, <span class="m">4</span>, <span class="m">5</span><span class="o">)</span>, <span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
-<span class="o">]</span>
-
-<span class="c1"># Build a TRT interpreter. Set explicit_batch_dimension accordingly.</span>
-<span class="nv">interpreter</span> <span class="o">=</span> TRTInterpreter<span class="o">(</span>
-    acc_mod, input_specs, <span class="nv">explicit_batch_dimension</span><span class="o">=</span>True/False
-<span class="o">)</span>
-
-<span class="c1"># The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.</span>
-<span class="c1"># The TRTInterpreterResult contains required parameter to build TRTModule,</span>
-<span class="c1"># and other informational output from TRTInterpreter run.</span>
-class TRTInterpreterResult<span class="o">(</span>NamedTuple<span class="o">)</span>:
-    engine: Any
-    input_names: Sequence<span class="o">[</span>str<span class="o">]</span>
-    output_names: Sequence<span class="o">[</span>str<span class="o">]</span>
-    serialized_cache: bytearray
-
-<span class="c1">#max_batch_size: set accordingly for maximum batch size you will use.</span>
-<span class="c1">#max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
-<span class="c1">#lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
-<span class="c1">#sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
-<span class="c1">#force_fp32_output: force output to be fp32</span>
-<span class="c1">#strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.</span>
-<span class="c1">#algorithm_selector: set up algorithm selection for certain layer</span>
-<span class="c1">#timing_cache: enable timing cache for TensorRT</span>
-<span class="c1">#profiling_verbosity: TensorRT logging level</span>
-<span class="nv">trt_interpreter_result</span> <span class="o">=</span> interpreter.run<span class="o">(</span>
-    <span class="nv">max_batch_size</span><span class="o">=</span><span class="m">64</span>,
-    <span class="nv">max_workspace_size</span><span class="o">=</span><span class="m">1</span> <span class="s">&lt;&lt; 25,</span>
-<span class="s">    sparse_weights=False,</span>
-<span class="s">    force_fp32</span>_output<span class="o">=</span>False,
-    <span class="nv">strict_type_constraints</span><span class="o">=</span>False,
-    <span class="nv">algorithm_selector</span><span class="o">=</span>None,
-    <span class="nv">timing_cache</span><span class="o">=</span>None,
-    <span class="nv">profiling_verbosity</span><span class="o">=</span>None,
-<span class="o">)</span>
-</pre></div>
-</div>
-<p><em>Common Errors:</em></p>
-<p>RuntimeError: Conversion of function xxx not currently supported!
-- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.</p>
-<ul class="simple">
-<li><p><strong>Step 3: Run the model</strong></p></li>
-</ul>
-<p>One way is using TRTModule, which is basically a PyTorch nn.Module.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>from torch_tensorrt.fx import TRTModule
-<span class="nv">mod</span> <span class="o">=</span> TRTModule<span class="o">(</span>
-    trt_interpreter_result.engine,
-    trt_interpreter_result.input_names,
-    trt_interpreter_result.output_names<span class="o">)</span>
-<span class="c1"># Just like all other PyTorch modules</span>
-<span class="nv">outputs</span> <span class="o">=</span> mod<span class="o">(</span>*inputs<span class="o">)</span>
-torch.save<span class="o">(</span>mod, <span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
-<span class="nv">reload_trt_mod</span> <span class="o">=</span> torch.load<span class="o">(</span><span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
-<span class="nv">reload_model_output</span> <span class="o">=</span> reload_trt_mod<span class="o">(</span>*inputs<span class="o">)</span>
-</pre></div>
-</div>
-<p>So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph’s operation to corresponding TensorRT operation and build up the TensoRT engine for it.</p>
-</section>
-<section id="acc-tracer">
-<h2>Acc Tracer<a class="headerlink" href="#acc-tracer" title="Permalink to this headline">¶</a></h2>
-<p>Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:</p>
-<ol class="arabic simple">
-<li><p>there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.</p></li>
-<li><p>acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.</p></li>
-</ol>
-</section>
-<section id="fx2trt">
-<h2>FX2TRT<a class="headerlink" href="#fx2trt" title="Permalink to this headline">¶</a></h2>
-<p>After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).</p>
-<p>Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
-def acc_ops_sigmoid<span class="o">(</span>network, target, args, kwargs, name<span class="o">)</span>:
-    <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
-
-<span class="s2">    The rest arguments are attributes of fx node.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
-    <span class="nv">input_val</span> <span class="o">=</span> kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
-
-    <span class="k">if</span> not isinstance<span class="o">(</span>input_val, trt.tensorrt.ITensor<span class="o">)</span>:
-        raise RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
-                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
-
-    <span class="nv">layer</span> <span class="o">=</span> network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val, <span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
-    layer.name <span class="o">=</span> name
-    <span class="k">return</span> layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
-</pre></div>
-</div>
-<section id="how-to-add-a-missing-op">
-<h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op" title="Permalink to this headline">¶</a></h3>
-<p>You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer.</p>
-<ul class="simple">
-<li><p><strong>Step 1. Add a new acc op</strong></p></li>
-</ul>
-<p>TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.</p>
-<p>In <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py">acc tracer</a>, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.</p>
-<p>In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.</p>
-<p>Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py">acc_normalizer.py</a>. e.g. the following code adds an acc op named foo() which adds two given inputs.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># NOTE: all acc ops should only take kwargs as inputs, therefore we need the &quot;*&quot;</span>
-<span class="c1"># at the beginning.</span>
-@register_acc_op
-def foo<span class="o">(</span>*, input, other, alpha<span class="o">)</span>:
-    <span class="k">return</span> input + alpha * other
-</pre></div>
-</div>
-<p>There’re two ways to register a mapping. One is <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164">register_acc_op_mapping()</a>. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">this_arg_is_optional</span> <span class="o">=</span> True
-
-@register_acc_op_mapping<span class="o">(</span>
-    <span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>, torch.add<span class="o">)</span>,
-    <span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
-        <span class="o">(</span><span class="s2">&quot;input&quot;</span>, <span class="s2">&quot;input&quot;</span><span class="o">)</span>,
-        <span class="o">(</span><span class="s2">&quot;other&quot;</span>, <span class="s2">&quot;other&quot;</span><span class="o">)</span>,
-        <span class="o">(</span><span class="s2">&quot;alpha&quot;</span>, <span class="s2">&quot;alpha&quot;</span>, this_arg_is_optional<span class="o">)</span>,
-    <span class="o">]</span>,
-<span class="o">)</span>
-@register_acc_op
-def foo<span class="o">(</span>*, input, other, <span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
-    <span class="k">return</span> input + alpha * other
-</pre></div>
-</div>
-<p><code class="docutils literal notranslate"><span class="pre">op_and_target</span></code> determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the <code class="docutils literal notranslate"><span class="pre">op_and_target</span></code>, we will trigger the mapping. Since we want to map from <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>, then op would be call_function and target would be <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>. <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in <em>original node</em>. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
-We don’t have to specify arg_replacement_tuples if none of the followings are true.</p>
-<ol class="arabic simple">
-<li><p>kwargs of original nodes and acc op nodes have different name.</p></li>
-<li><p>there’re optional arguments.</p></li>
-</ol>
-<p>The other way to register a mapping is through <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206">register_custom_acc_mapper_fn()</a>. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@register_acc_op
-def foo<span class="o">(</span>*, input, other, <span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
-    <span class="k">return</span> input + alpha * other
-
-@register_custom_acc_mapper_fn<span class="o">(</span>
-    <span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>, torch.add<span class="o">)</span>,
-    <span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
-        <span class="o">(</span><span class="s2">&quot;input&quot;</span>, <span class="s2">&quot;input&quot;</span><span class="o">)</span>,
-        <span class="o">(</span><span class="s2">&quot;other&quot;</span>, <span class="s2">&quot;other&quot;</span><span class="o">)</span>,
-        <span class="o">(</span><span class="s2">&quot;alpha&quot;</span>, <span class="s2">&quot;alpha&quot;</span>, this_arg_is_optional<span class="o">)</span>,
-    <span class="o">]</span>,
-<span class="o">)</span>
-def custom_mapper<span class="o">(</span>node: torch.fx.Node, _: nn.Module<span class="o">)</span> -&gt; torch.fx.Node:
-    <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    `node` is original node, which is a call_function node with target</span>
-<span class="s2">    being torch.add.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
-    <span class="nv">alpha</span> <span class="o">=</span> <span class="m">1</span>
-    <span class="k">if</span> <span class="s2">&quot;alpha&quot;</span> <span class="k">in</span> node.kwargs:
-        <span class="nv">alpha</span> <span class="o">=</span> node.kwargs<span class="o">[</span><span class="s2">&quot;alpha&quot;</span><span class="o">]</span>
-    <span class="nv">foo_kwargs</span> <span class="o">=</span> <span class="o">{</span><span class="s2">&quot;input&quot;</span>: node<span class="o">[</span><span class="s2">&quot;input&quot;</span><span class="o">]</span>, <span class="s2">&quot;other&quot;</span>: node<span class="o">[</span><span class="s2">&quot;other&quot;</span><span class="o">]</span>, <span class="s2">&quot;alpha&quot;</span>: alpha<span class="o">}</span>
-    with node.graph.inserting_before<span class="o">(</span>node<span class="o">)</span>:
-        <span class="nv">foo_node</span> <span class="o">=</span> node.graph.call_function<span class="o">(</span>foo, <span class="nv">kwargs</span><span class="o">=</span>foo_kwargs<span class="o">)</span>
-        foo_node.meta <span class="o">=</span> node.meta.copy<span class="o">()</span>
-        <span class="k">return</span> foo_node
-</pre></div>
-</div>
-<p>In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379">acc_normalizer.py</a>.</p>
-<p>The last step would be <em>adding unit test</em> for the new acc op or mapper function we added. The place to add the unit test is here <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py">test_acc_tracer.py</a>.</p>
-<ul class="simple">
-<li><p><strong>Step 2. Add a new fx2trt converter</strong></p></li>
-</ul>
-<p>All the developed converters for acc ops are all in <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py">acc_op_converter.py</a>. It could give you a good example of how the converter is added.</p>
-<p>Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html">TensorRT APIs</a>.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
-def acc_ops_sigmoid<span class="o">(</span>network, target, args, kwargs, name<span class="o">)</span>:
-    <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
-
-<span class="s2">    The rest arguments are attributes of fx node.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
-    <span class="nv">input_val</span> <span class="o">=</span> kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
-
-    <span class="k">if</span> not isinstance<span class="o">(</span>input_val, trt.tensorrt.ITensor<span class="o">)</span>:
-        raise RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
-                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
-
-    <span class="nv">layer</span> <span class="o">=</span> network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val, <span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
-    layer.name <span class="o">=</span> name
-    <span class="k">return</span> layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
-</pre></div>
-</div>
-<p>We need to use <code class="docutils literal notranslate"><span class="pre">tensorrt_converter</span></code> decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is <cite>acc_ops.sigmoid</cite> which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.</p>
-<p><strong>What if we can not find corresponding layers in TensorRT that do the same thing as the node.</strong></p>
-<p>In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. <em>We have not implement this feature yet. We will update once it is enabled</em>.</p>
-<p>Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op">folder</a>.</p>
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diff --git a/docs/tutorials/getting_started_with_python_api.html b/docs/tutorials/getting_started_with_python_api.html
index d2a00024d2..e2a0aa0d6f 100644
--- a/docs/tutorials/getting_started_with_python_api.html
+++ b/docs/tutorials/getting_started_with_python_api.html
@@ -199,7 +199,7 @@
               
               
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diff --git a/docs/tutorials/installation.html b/docs/tutorials/installation.html
index 18bdc09fe3..9b486dde84 100644
--- a/docs/tutorials/installation.html
+++ b/docs/tutorials/installation.html
@@ -199,7 +199,7 @@
               
               
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diff --git a/docs/tutorials/ptq.html b/docs/tutorials/ptq.html
index 0da616e6b0..1d24461f41 100644
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+++ b/docs/tutorials/ptq.html
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diff --git a/docs/tutorials/using_dla.html b/docs/tutorials/using_dla.html
index ed054d3e0f..79a04f4c23 100644
--- a/docs/tutorials/using_dla.html
+++ b/docs/tutorials/using_dla.html
@@ -199,7 +199,7 @@
               
               
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diff --git a/docsrc/conf.py b/docsrc/conf.py
index 4e65020672..b386657e8c 100644
--- a/docsrc/conf.py
+++ b/docsrc/conf.py
@@ -206,4 +206,4 @@ def handle_item(fieldarg, content):
     return nodes.field('', fieldname, fieldbody)
 
 
-TypedField.make_field = patched_make_field
+TypedField.make_field = patched_make_field
\ No newline at end of file

From ac238447b9d2e2183498894f9d2ac2bb71571d89 Mon Sep 17 00:00:00 2001
From: Wei Wei <wwei6@fb.com>
Date: Tue, 28 Jun 2022 15:15:44 -0700
Subject: [PATCH 10/10] update index.rst

---
 docsrc/index.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docsrc/index.rst b/docsrc/index.rst
index 9e9c87d23b..b12d6cc1f4 100644
--- a/docsrc/index.rst
+++ b/docsrc/index.rst
@@ -29,6 +29,7 @@ Getting Started
 * :ref:`runtime`
 * :ref:`using_dla`
 * :ref:`serving_torch_tensorrt_with_triton`
+* :ref:`user_guide`
 
 .. toctree::
    :caption: Getting Started
@@ -45,6 +46,7 @@ Getting Started
    tutorials/runtime
    tutorials/using_dla
    tutorials/serving_torch_tensorrt_with_triton
+   tutorials/getting_started_with_fx_path
 
 .. toctree::
    :caption: Notebooks