.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/export.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_tutorials_export.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_export.py:
Exporting tensordict modules
============================
**Author**: `Vincent Moens <https://github.com/vmoens>`_
Prerequisites
~~~~~~~~~~~~~
Reading the :ref:`TensorDictModule <tensordictmodule>` tutorial is preferable to fully benefit from this tutorial.
Once a module has been written using ``tensordict.nn``, it is often useful to isolate the computational graph and export
that graph. The goal of this may be to execute the model on hardware (e.g., robots, drones, edge devices) or eliminate
the dependency on tensordict altogether.
PyTorch provides multiple methods for exporting modules, including ``onnx`` and ``torch.export``, both of which are
compatible with ``tensordict``.
In this short tutorial, we will see how one can use ``torch.export`` to isolate the computational graph of a model.
``torch.onnx`` support follows the same logic.
Key learnings
~~~~~~~~~~~~~
- Executing a ``tensordict.nn`` module without :class:`~tensordict.TensorDict` inputs;
- Selecting the output(s) of a model;
- Handling stochstic models;
- Exporting such model using `torch.export`;
- Saving the model to a file;
- Isolating the pytorch model;
.. GENERATED FROM PYTHON SOURCE LINES 36-49
.. code-block:: Python
import time
import torch
from tensordict.nn import (
InteractionType,
NormalParamExtractor,
ProbabilisticTensorDictModule as Prob,
set_interaction_type,
TensorDictModule as Mod,
TensorDictSequential as Seq,
)
from torch import distributions as dists, nn
.. GENERATED FROM PYTHON SOURCE LINES 50-69
Designing the model
-------------------
In many applications, it is useful to work with stochastic models, i.e., models that output a variable that is not
deterministically defined but that is sampled according to a parametric distribution. For instance, generative AI
models will often generate different outputs when the same input if provided, because they sample the output based
on a distribution which parameters are defined by the input.
The ``tensordict`` library deals with this through the :class:`~tensordict.nn.ProbabilisticTensorDictModule` class.
This primitive is built using a distribtion class (:class:`~torch.distributions.Normal` in our case) and an indicator
of the input keys that will be used at execution time to build that distribution.
The network we are building is therefore going to be the combination of three main components:
- A network mapping the input to a latent parameter;
- A :class:`tensordict.nn.NormalParamExtractor` module splitting the input in a location `"loc"` and `"scale"`
parameters to be passed to the ``Normal`` distrbution;
- A distribution constructor module.
.. GENERATED FROM PYTHON SOURCE LINES 69-84
.. code-block:: Python
model = Seq(
# 1. A small network for embedding
Mod(nn.Linear(3, 4), in_keys=["x"], out_keys=["hidden"]),
Mod(nn.ReLU(), in_keys=["hidden"], out_keys=["hidden"]),
Mod(nn.Linear(4, 4), in_keys=["hidden"], out_keys=["latent"]),
# 2. Extracting params
Mod(NormalParamExtractor(), in_keys=["latent"], out_keys=["loc", "scale"]),
# 3. Probabilistic module
Prob(
in_keys=["loc", "scale"],
out_keys=["sample"],
distribution_class=dists.Normal,
),
)
.. GENERATED FROM PYTHON SOURCE LINES 85-87
Let us run this model and see what the output looks like:
.. GENERATED FROM PYTHON SOURCE LINES 87-91
.. code-block:: Python
x = torch.randn(1, 3)
print(model(x=x))
.. rst-class:: sphx-glr-script-out
.. code-block:: none
(tensor([[0.0214, 0.0000, 0.0000, 0.9495]], grad_fn=<ReluBackward0>), tensor([[-0.0966, 0.3627, 0.6197, -0.5036]], grad_fn=<AddmmBackward0>), tensor([[-0.0966, 0.3627]], grad_fn=<SplitBackward0>), tensor([[1.4314, 0.7141]], grad_fn=<ClampMinBackward0>), tensor([[-0.0966, 0.3627]], grad_fn=<SplitBackward0>))
.. GENERATED FROM PYTHON SOURCE LINES 92-107
As expected, running the model with a tensor input returns as many tensors as the module's output keys! For large
models, this can be quite annoying and wasteful. Later, we will see how we can limit the number of outputs of the
model to deal with this issue.
Using ``torch.export`` with a ``TensorDictModule``
--------------------------------------------------
Now that we have successfully built our model, we would like to extract its computational graph in a single object that
is independent of ``tensordict``. ``torch.export`` is a PyTorch module dedicated to isolate the graph of a module and
represent it in a standardized way. Its main entry point is :func:`~torch.export.export` which returns a ``ExportedProgram``
object. In turn, this object has several attributes of interest that we will explore below: a ``graph_module``,
which represents the FX graph captured by ``export``, a ``graph_signature`` with input, outputs etc of the graph,
and finally a ``module()`` that returns a callable that can be used in-place of the original module.
Although our module accepts both args and kwargs, we will focus on its usage with kwargs as this is clearer.
.. GENERATED FROM PYTHON SOURCE LINES 107-112
.. code-block:: Python
from torch.export import export
model_export = export(model, args=(), kwargs={"x": x})
.. GENERATED FROM PYTHON SOURCE LINES 113-115
Let us look at the module:
.. GENERATED FROM PYTHON SOURCE LINES 115-117
.. code-block:: Python
print("module:", model_export.module())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
module: GraphModule(
(module): Module(
(0): Module(
(module): Module()
)
(2): Module(
(module): Module()
)
)
)
def forward(self, x):
x, = fx_pytree.tree_flatten_spec(([], {'x':x}), self._in_spec)
module_0_module_weight = getattr(self.module, "0").module.weight
module_0_module_bias = getattr(self.module, "0").module.bias
module_2_module_weight = getattr(self.module, "2").module.weight
module_2_module_bias = getattr(self.module, "2").module.bias
linear = torch.ops.aten.linear.default(x, module_0_module_weight, module_0_module_bias); x = module_0_module_weight = module_0_module_bias = None
relu = torch.ops.aten.relu.default(linear); linear = None
linear_1 = torch.ops.aten.linear.default(relu, module_2_module_weight, module_2_module_bias); module_2_module_weight = module_2_module_bias = None
chunk = torch.ops.aten.chunk.default(linear_1, 2, -1)
getitem = chunk[0]
getitem_1 = chunk[1]; chunk = None
add = torch.ops.aten.add.Tensor(getitem_1, 0.5254586935043335); getitem_1 = None
softplus = torch.ops.aten.softplus.default(add); add = None
add_1 = torch.ops.aten.add.Tensor(softplus, 0.01); softplus = None
clamp_min = torch.ops.aten.clamp_min.default(add_1, 0.0001); add_1 = None
broadcast_tensors = torch.ops.aten.broadcast_tensors.default([getitem, clamp_min]); getitem = clamp_min = None
getitem_2 = broadcast_tensors[0]
getitem_3 = broadcast_tensors[1]; broadcast_tensors = None
return pytree.tree_unflatten((relu, linear_1, getitem_2, getitem_3, getitem_2), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
.. GENERATED FROM PYTHON SOURCE LINES 118-120
This module can be run exactly like our original module (with a lower overhead):
.. GENERATED FROM PYTHON SOURCE LINES 120-131
.. code-block:: Python
t0 = time.time()
model(x=x)
print(f"Time for TDModule: {(time.time() - t0) * 1e6: 4.2f} micro-seconds")
exported = model_export.module()
# Exported version
t0 = time.time()
exported(x=x)
print(f"Time for exported module: {(time.time() - t0) * 1e6: 4.2f} micro-seconds")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Time for TDModule: 544.07 micro-seconds
Time for exported module: 357.15 micro-seconds
.. GENERATED FROM PYTHON SOURCE LINES 132-133
and the FX graph:
.. GENERATED FROM PYTHON SOURCE LINES 133-135
.. code-block:: Python
print("fx graph:", model_export.graph_module.print_readable())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
class GraphModule(torch.nn.Module):
def forward(self, p_l__args___0_module_0_module_weight: "f32[4, 3]", p_l__args___0_module_0_module_bias: "f32[4]", p_l__args___0_module_2_module_weight: "f32[4, 4]", p_l__args___0_module_2_module_bias: "f32[4]", x: "f32[1, 3]"):
# File: /pytorch/tensordict/tensordict/nn/common.py:1149 in _call_module, code: out = self.module(*tensors, **kwargs)
linear: "f32[1, 4]" = torch.ops.aten.linear.default(x, p_l__args___0_module_0_module_weight, p_l__args___0_module_0_module_bias); x = p_l__args___0_module_0_module_weight = p_l__args___0_module_0_module_bias = None
relu: "f32[1, 4]" = torch.ops.aten.relu.default(linear); linear = None
linear_1: "f32[1, 4]" = torch.ops.aten.linear.default(relu, p_l__args___0_module_2_module_weight, p_l__args___0_module_2_module_bias); p_l__args___0_module_2_module_weight = p_l__args___0_module_2_module_bias = None
# File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:85 in forward, code: loc, scale = tensor.chunk(2, -1)
chunk = torch.ops.aten.chunk.default(linear_1, 2, -1)
getitem: "f32[1, 2]" = chunk[0]
getitem_1: "f32[1, 2]" = chunk[1]; chunk = None
# File: /pytorch/tensordict/tensordict/nn/utils.py:71 in forward, code: return torch.nn.functional.softplus(x + self.bias) + self.min_val
add: "f32[1, 2]" = torch.ops.aten.add.Tensor(getitem_1, 0.5254586935043335); getitem_1 = None
softplus: "f32[1, 2]" = torch.ops.aten.softplus.default(add); add = None
add_1: "f32[1, 2]" = torch.ops.aten.add.Tensor(softplus, 0.01); softplus = None
# File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:86 in forward, code: scale = self.scale_mapping(scale).clamp_min(self.scale_lb)
clamp_min: "f32[1, 2]" = torch.ops.aten.clamp_min.default(add_1, 0.0001); add_1 = None
# File: /pytorch/tensordict/env/lib/python3.10/site-packages/torch/distributions/utils.py:57 in broadcast_all, code: return torch.broadcast_tensors(*values)
broadcast_tensors = torch.ops.aten.broadcast_tensors.default([getitem, clamp_min]); getitem = clamp_min = None
getitem_2: "f32[1, 2]" = broadcast_tensors[0]
getitem_3: "f32[1, 2]" = broadcast_tensors[1]; broadcast_tensors = None
return (relu, linear_1, getitem_2, getitem_3, getitem_2)
fx graph: class GraphModule(torch.nn.Module):
def forward(self, p_l__args___0_module_0_module_weight: "f32[4, 3]", p_l__args___0_module_0_module_bias: "f32[4]", p_l__args___0_module_2_module_weight: "f32[4, 4]", p_l__args___0_module_2_module_bias: "f32[4]", x: "f32[1, 3]"):
# File: /pytorch/tensordict/tensordict/nn/common.py:1149 in _call_module, code: out = self.module(*tensors, **kwargs)
linear: "f32[1, 4]" = torch.ops.aten.linear.default(x, p_l__args___0_module_0_module_weight, p_l__args___0_module_0_module_bias); x = p_l__args___0_module_0_module_weight = p_l__args___0_module_0_module_bias = None
relu: "f32[1, 4]" = torch.ops.aten.relu.default(linear); linear = None
linear_1: "f32[1, 4]" = torch.ops.aten.linear.default(relu, p_l__args___0_module_2_module_weight, p_l__args___0_module_2_module_bias); p_l__args___0_module_2_module_weight = p_l__args___0_module_2_module_bias = None
# File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:85 in forward, code: loc, scale = tensor.chunk(2, -1)
chunk = torch.ops.aten.chunk.default(linear_1, 2, -1)
getitem: "f32[1, 2]" = chunk[0]
getitem_1: "f32[1, 2]" = chunk[1]; chunk = None
# File: /pytorch/tensordict/tensordict/nn/utils.py:71 in forward, code: return torch.nn.functional.softplus(x + self.bias) + self.min_val
add: "f32[1, 2]" = torch.ops.aten.add.Tensor(getitem_1, 0.5254586935043335); getitem_1 = None
softplus: "f32[1, 2]" = torch.ops.aten.softplus.default(add); add = None
add_1: "f32[1, 2]" = torch.ops.aten.add.Tensor(softplus, 0.01); softplus = None
# File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:86 in forward, code: scale = self.scale_mapping(scale).clamp_min(self.scale_lb)
clamp_min: "f32[1, 2]" = torch.ops.aten.clamp_min.default(add_1, 0.0001); add_1 = None
# File: /pytorch/tensordict/env/lib/python3.10/site-packages/torch/distributions/utils.py:57 in broadcast_all, code: return torch.broadcast_tensors(*values)
broadcast_tensors = torch.ops.aten.broadcast_tensors.default([getitem, clamp_min]); getitem = clamp_min = None
getitem_2: "f32[1, 2]" = broadcast_tensors[0]
getitem_3: "f32[1, 2]" = broadcast_tensors[1]; broadcast_tensors = None
return (relu, linear_1, getitem_2, getitem_3, getitem_2)
.. GENERATED FROM PYTHON SOURCE LINES 136-144
Working with nested keys
~~~~~~~~~~~~~~~~~~~~~~~~
Nested keys are a core feature of the tensordict library, and being able to export modules that read and write
nested entries is therefore an important feature to support.
Because keyword arguments must be regualar strings, it is not possible for :class:`~tensordict.nn.dispatch` to work
directly with them. Instead, ``dispatch`` will unpack nested keys joined with a regular underscore (`"_"`), as the
following example shows.
.. GENERATED FROM PYTHON SOURCE LINES 144-154
.. code-block:: Python
model_nested = Seq(
Mod(lambda x: x + 1, in_keys=[("some", "key")], out_keys=["hidden"]),
Mod(lambda x: x - 1, in_keys=["hidden"], out_keys=[("some", "output")]),
).select_out_keys(("some", "output"))
model_nested_export = export(model_nested, args=(), kwargs={"some_key": x})
print("exported module with nested input:", model_nested_export.module())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
exported module with nested input: GraphModule()
def forward(self, some_key):
some_key, = fx_pytree.tree_flatten_spec(([], {'some_key':some_key}), self._in_spec)
add = torch.ops.aten.add.Tensor(some_key, 1); some_key = None
sub = torch.ops.aten.sub.Tensor(add, 1); add = None
return pytree.tree_unflatten((sub,), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
.. GENERATED FROM PYTHON SOURCE LINES 155-192
Note that the callable returned by `module()` is a pure python callable that can be in turn compiled using
:func:`~torch.compile`.
Saving the exported module
~~~~~~~~~~~~~~~~~~~~~~~~~~
``torch.export`` has its own serialization protocol, :func:`~torch.export.save` and :func:`~torch.export.load`.
Conventionally, the `".pt2"` extension is to be used:
>>> torch.export.save(model_export, "model.pt2")
Selecting the outputs
---------------------
Recall that the ``tensordict.nn`` is to keep every intermediate value in the output, unless the user specifically asks
for only a specific value. During training, this can be very useful: one can easily log intermediate values of the
graph, or use them for other purposes (e.g., reconstruct a distribution based on its saved parameters, rather than
saving the :class:`~torch.distributions.Distribution` object itself). One could also argue that, during training, the
impact on memory of registering intermediate values is negligeable since they are part of the computational graph
used by ``torch.autograd`` to compute the parameter gradients.
During inference, though, we most likely are only interested in the final sample of the model.
Because we want to extract the model for usages that are independent of the ``tensordict`` library, it makes sense to
isolate the only output we desire.
To do this, we have several options:
1. Build the :meth:`~tensordict.nn.TensorDictSequential` with the ``selected_out_keys`` keyword argument, which will
induce the selection of the desired entries during calls to the module;
2. Using the :meth:`~tensordict.nn.TensorDictModule.select_out_keys` method, which will modify the ``out_keys``
attribute in-place (this can be reverted through :meth:`~tensordict.nn.TensorDictModule.reset_out_keys`).
3. Wrap the existing instance in a :meth:`~tensordict.nn.TensorDictSequential` that will filter out the unwanted keys:
>>> module_filtered = Seq(module, selected_out_keys=["sample"])
Let us test the model after selecting its output keys.
When an `x` input is provided, we expect our model to output a single tensor corresponding to a sample of the
distribution:
.. GENERATED FROM PYTHON SOURCE LINES 192-196
.. code-block:: Python
model.select_out_keys("sample")
print(model(x=x))
.. rst-class:: sphx-glr-script-out
.. code-block:: none
tensor([[-0.0966, 0.3627]], grad_fn=<SplitBackward0>)
.. GENERATED FROM PYTHON SOURCE LINES 197-199
We see that the output is now a single tensor, corresponding to the sample of the distribution.
We can create a new exported graph from this. Its computational graph should be simplified:
.. GENERATED FROM PYTHON SOURCE LINES 199-203
.. code-block:: Python
model_export = export(model, args=(), kwargs={"x": x})
print("module:", model_export.module())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
module: GraphModule(
(module): Module(
(0): Module(
(module): Module()
)
(2): Module(
(module): Module()
)
)
)
def forward(self, x):
x, = fx_pytree.tree_flatten_spec(([], {'x':x}), self._in_spec)
module_0_module_weight = getattr(self.module, "0").module.weight
module_0_module_bias = getattr(self.module, "0").module.bias
module_2_module_weight = getattr(self.module, "2").module.weight
module_2_module_bias = getattr(self.module, "2").module.bias
linear = torch.ops.aten.linear.default(x, module_0_module_weight, module_0_module_bias); x = module_0_module_weight = module_0_module_bias = None
relu = torch.ops.aten.relu.default(linear); linear = None
linear_1 = torch.ops.aten.linear.default(relu, module_2_module_weight, module_2_module_bias); relu = module_2_module_weight = module_2_module_bias = None
chunk = torch.ops.aten.chunk.default(linear_1, 2, -1); linear_1 = None
getitem = chunk[0]
getitem_1 = chunk[1]; chunk = None
add = torch.ops.aten.add.Tensor(getitem_1, 0.5254586935043335); getitem_1 = None
softplus = torch.ops.aten.softplus.default(add); add = None
add_1 = torch.ops.aten.add.Tensor(softplus, 0.01); softplus = None
clamp_min = torch.ops.aten.clamp_min.default(add_1, 0.0001); add_1 = None
broadcast_tensors = torch.ops.aten.broadcast_tensors.default([getitem, clamp_min]); getitem = clamp_min = None
getitem_2 = broadcast_tensors[0]
getitem_3 = broadcast_tensors[1]; broadcast_tensors = getitem_3 = None
return pytree.tree_unflatten((getitem_2,), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
.. GENERATED FROM PYTHON SOURCE LINES 204-226
Controlling the Sampling Strategy
---------------------------------
We have not yet discussed how the :class:`~tensordict.nn.ProbabilisticTensorDictModule` samples from the distribution.
By sampling, we mean obtaining a value within the space defined by the distribution according to a specific strategy.
For instance, one may desire to get stochastic samples during training but deterministic samples (e.g., the mean or
the mode) at inference time. To address this, ``tensordict`` utilizes the :class:`~tensordict.nn.set_interaction_type`
decorator and context manager, which accepts ``InteractionType`` Enum inputs:
>>> with set_interaction_type(InteractionType.MEAN):
... output = module(input) # takes the input of the distribution, if ProbabilisticTensorDictModule is invoked
The default ``InteractionType`` is ``InteractionType.DETERMINISTIC``, which, if not implemented directly, is either
the mean of distributions with a real domain, or the mode of distributions with a discrete domain. This default value
can be changed using the ``default_interaction_type`` keyword argument of ``ProbabilisticTensorDictModule``.
Let us recap: to control the sampling strategy of our network, we can either define a default sampling strategy in the
constructor, or override it at runtime through the ``set_interaction_type`` context manager.
As we can see from the following example, ``torch.export`` respond correctly the usage of the decorator: if we ask for
a random sample, the output is different than if we ask for the mean:
.. GENERATED FROM PYTHON SOURCE LINES 226-235
.. code-block:: Python
with set_interaction_type(InteractionType.RANDOM):
model_export = export(model, args=(), kwargs={"x": x})
print(model_export.module())
with set_interaction_type(InteractionType.MEAN):
model_export = export(model, args=(), kwargs={"x": x})
print(model_export.module())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
GraphModule(
(module): Module(
(0): Module(
(module): Module()
)
(2): Module(
(module): Module()
)
)
)
def forward(self, x):
x, = fx_pytree.tree_flatten_spec(([], {'x':x}), self._in_spec)
module_0_module_weight = getattr(self.module, "0").module.weight
module_0_module_bias = getattr(self.module, "0").module.bias
module_2_module_weight = getattr(self.module, "2").module.weight
module_2_module_bias = getattr(self.module, "2").module.bias
linear = torch.ops.aten.linear.default(x, module_0_module_weight, module_0_module_bias); x = module_0_module_weight = module_0_module_bias = None
relu = torch.ops.aten.relu.default(linear); linear = None
linear_1 = torch.ops.aten.linear.default(relu, module_2_module_weight, module_2_module_bias); relu = module_2_module_weight = module_2_module_bias = None
chunk = torch.ops.aten.chunk.default(linear_1, 2, -1); linear_1 = None
getitem = chunk[0]
getitem_1 = chunk[1]; chunk = None
add = torch.ops.aten.add.Tensor(getitem_1, 0.5254586935043335); getitem_1 = None
softplus = torch.ops.aten.softplus.default(add); add = None
add_1 = torch.ops.aten.add.Tensor(softplus, 0.01); softplus = None
clamp_min = torch.ops.aten.clamp_min.default(add_1, 0.0001); add_1 = None
broadcast_tensors = torch.ops.aten.broadcast_tensors.default([getitem, clamp_min]); getitem = clamp_min = None
getitem_2 = broadcast_tensors[0]
getitem_3 = broadcast_tensors[1]; broadcast_tensors = None
empty = torch.ops.aten.empty.memory_format([1, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
normal_ = torch.ops.aten.normal_.default(empty); empty = None
mul = torch.ops.aten.mul.Tensor(normal_, getitem_3); normal_ = getitem_3 = None
add_2 = torch.ops.aten.add.Tensor(getitem_2, mul); getitem_2 = mul = None
return pytree.tree_unflatten((add_2,), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
GraphModule(
(module): Module(
(0): Module(
(module): Module()
)
(2): Module(
(module): Module()
)
)
)
def forward(self, x):
x, = fx_pytree.tree_flatten_spec(([], {'x':x}), self._in_spec)
module_0_module_weight = getattr(self.module, "0").module.weight
module_0_module_bias = getattr(self.module, "0").module.bias
module_2_module_weight = getattr(self.module, "2").module.weight
module_2_module_bias = getattr(self.module, "2").module.bias
linear = torch.ops.aten.linear.default(x, module_0_module_weight, module_0_module_bias); x = module_0_module_weight = module_0_module_bias = None
relu = torch.ops.aten.relu.default(linear); linear = None
linear_1 = torch.ops.aten.linear.default(relu, module_2_module_weight, module_2_module_bias); relu = module_2_module_weight = module_2_module_bias = None
chunk = torch.ops.aten.chunk.default(linear_1, 2, -1); linear_1 = None
getitem = chunk[0]
getitem_1 = chunk[1]; chunk = None
add = torch.ops.aten.add.Tensor(getitem_1, 0.5254586935043335); getitem_1 = None
softplus = torch.ops.aten.softplus.default(add); add = None
add_1 = torch.ops.aten.add.Tensor(softplus, 0.01); softplus = None
clamp_min = torch.ops.aten.clamp_min.default(add_1, 0.0001); add_1 = None
broadcast_tensors = torch.ops.aten.broadcast_tensors.default([getitem, clamp_min]); getitem = clamp_min = None
getitem_2 = broadcast_tensors[0]
getitem_3 = broadcast_tensors[1]; broadcast_tensors = getitem_3 = None
return pytree.tree_unflatten((getitem_2,), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
.. GENERATED FROM PYTHON SOURCE LINES 236-248
This is all you need to know to use ``torch.export``. Please refer to the
`official documentation <https://pytorch.org/docs/stable/export>`_ for more info.
Next steps and further reading
------------------------------
- Check the ``torch.export`` tutorial, available `here <https://pytorch.org/tutorials/intermediate/torch_export_tutorial.html>`__;
- ONNX support: check the `ONNX tutorials <https://pytorch.org/tutorials/beginner/onnx/export_simple_model_to_onnx_tutorial.html>`_
to learn more about this feature. Exporting to ONNX is very similar to `torch.export` explained here.
- For deployment of PyTorch code on servers without python environment, check the
`AOTInductor <https://pytorch.org/docs/main/torch.compiler_aot_inductor.html>`_ documentation.
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 1.580 seconds)
.. _sphx_glr_download_tutorials_export.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: export.ipynb <export.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: export.py <export.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: export.zip <export.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_