.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/export-to-executorch-tutorial.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-to-executorch-tutorial.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_export-to-executorch-tutorial.py:
Exporting to ExecuTorch Tutorial
================================
**Author:** `Angela Yi <https://github.com/angelayi>`__
.. GENERATED FROM PYTHON SOURCE LINES 16-29
ExecuTorch is a unified ML stack for lowering PyTorch models to edge devices.
It introduces improved entry points to perform model, device, and/or use-case
specific optimizations such as backend delegation, user-defined compiler
transformations, default or user-defined memory planning, and more.
At a high level, the workflow looks as follows:
.. image:: ../executorch_stack.png
:width: 560
In this tutorial, we will cover the APIs in the "Program preparation" steps to
lower a PyTorch model to a format which can be loaded to device and run on the
ExecuTorch runtime.
.. GENERATED FROM PYTHON SOURCE LINES 31-36
Prerequisites
-------------
To run this tutorial, you’ll first need to
`Set up your ExecuTorch environment <../getting-started-setup.html>`__.
.. GENERATED FROM PYTHON SOURCE LINES 38-50
Exporting a Model
-----------------
Note: The Export APIs are still undergoing changes to align better with the
longer term state of export. Please refer to this
`issue <https://github.com/pytorch/executorch/issues/290>`__ for more details.
The first step of lowering to ExecuTorch is to export the given model (any
callable or ``torch.nn.Module``) to a graph representation. This is done via
``torch.export``, which takes in an ``torch.nn.Module``, a tuple of
positional arguments, optionally a dictionary of keyword arguments (not shown
in the example), and a list of dynamic shapes (covered later).
.. GENERATED FROM PYTHON SOURCE LINES 50-72
.. code-block:: default
import torch
from torch.export import export, ExportedProgram
class SimpleConv(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv = torch.nn.Conv2d(
in_channels=3, out_channels=16, kernel_size=3, padding=1
)
self.relu = torch.nn.ReLU()
def forward(self, x: torch.Tensor) -> torch.Tensor:
a = self.conv(x)
return self.relu(a)
example_args = (torch.randn(1, 3, 256, 256),)
aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
print(aten_dialect)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
conv2d: "f32[1, 16, 256, 256]" = torch.ops.aten.conv2d.default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1]); x = p_conv_weight = p_conv_bias = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
relu: "f32[1, 16, 256, 256]" = torch.ops.aten.relu.default(conv2d); conv2d = None
return (relu,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='relu'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 73-83
The output of ``torch.export.export`` is a fully flattened graph (meaning the
graph does not contain any module hierarchy, except in the case of control
flow operators). Additionally, the graph is purely functional, meaning it does
not contain operations with side effects such as mutations or aliasing.
More specifications about the result of ``torch.export`` can be found
`here <https://pytorch.org/docs/main/export.html>`__ .
The graph returned by ``torch.export`` only contains functional ATen operators
(~2000 ops), which we will call the ``ATen Dialect``.
.. GENERATED FROM PYTHON SOURCE LINES 85-91
Expressing Dynamism
^^^^^^^^^^^^^^^^^^^
By default, the exporting flow will trace the program assuming that all input
shapes are static, so if we run the program with inputs shapes that are
different than the ones we used while tracing, we will run into an error:
.. GENERATED FROM PYTHON SOURCE LINES 91-115
.. code-block:: default
import traceback as tb
class Basic(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return x + y
example_args = (torch.randn(3, 3), torch.randn(3, 3))
aten_dialect: ExportedProgram = export(Basic(), example_args)
# Works correctly
print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 3)))
# Errors
try:
print(aten_dialect.module()(torch.ones(3, 2), torch.ones(3, 2)))
except Exception:
tb.print_exc()
.. rst-class:: sphx-glr-script-out
.. code-block:: none
tensor([[2., 2., 2.],
[2., 2., 2.],
[2., 2., 2.]])
Traceback (most recent call last):
File "/pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py", line 111, in <module>
print(aten_dialect.module()(torch.ones(3, 2), torch.ones(3, 2)))
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 822, in call_wrapped
return self._wrapped_call(self, *args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 400, in __call__
raise e
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 387, in __call__
return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc]
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1845, in _call_impl
return inner()
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1772, in inner
args_kwargs_result = hook(self, args, kwargs) # type: ignore[misc]
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 745, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 49, in _check_input_constraints_pre_hook
_check_input_constraints_for_graph(
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 360, in _check_input_constraints_for_graph
raise RuntimeError(
RuntimeError: Expected input at *args[0].shape[1] to be equal to 3, but got 2
.. GENERATED FROM PYTHON SOURCE LINES 116-118
To express that some input shapes are dynamic, we can insert dynamic
shapes to the exporting flow. This is done through the ``Dim`` API:
.. GENERATED FROM PYTHON SOURCE LINES 118-138
.. code-block:: default
from torch.export import Dim
class Basic(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return x + y
example_args = (torch.randn(3, 3), torch.randn(3, 3))
dim1_x = Dim("dim1_x", min=1, max=10)
dynamic_shapes = {"x": {1: dim1_x}, "y": {1: dim1_x}}
aten_dialect: ExportedProgram = export(
Basic(), example_args, dynamic_shapes=dynamic_shapes
)
print(aten_dialect)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[3, s0]", y: "f32[3, s0]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:127 in forward, code: return x + y
add: "f32[3, s0]" = torch.ops.aten.add.Tensor(x, y); x = y = None
return (add,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {s0: VR[1, 10]}
.. GENERATED FROM PYTHON SOURCE LINES 139-147
Note that that the inputs ``arg0_1`` and ``arg1_1`` now have shapes (3, s0),
with ``s0`` being a symbol representing that this dimension can be a range
of values.
Additionally, we can see in the **Range constraints** that value of ``s0`` has
the range [1, 10], which was specified by our dynamic shapes.
Now let's try running the model with different shapes:
.. GENERATED FROM PYTHON SOURCE LINES 147-165
.. code-block:: default
# Works correctly
print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 3)))
print(aten_dialect.module()(torch.ones(3, 2), torch.ones(3, 2)))
# Errors because it violates our constraint that input 0, dim 1 <= 10
try:
print(aten_dialect.module()(torch.ones(3, 15), torch.ones(3, 15)))
except Exception:
tb.print_exc()
# Errors because it violates our constraint that input 0, dim 1 == input 1, dim 1
try:
print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 2)))
except Exception:
tb.print_exc()
.. rst-class:: sphx-glr-script-out
.. code-block:: none
tensor([[2., 2., 2.],
[2., 2., 2.],
[2., 2., 2.]])
tensor([[2., 2.],
[2., 2.],
[2., 2.]])
Traceback (most recent call last):
File "/pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py", line 154, in <module>
print(aten_dialect.module()(torch.ones(3, 15), torch.ones(3, 15)))
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 822, in call_wrapped
return self._wrapped_call(self, *args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 400, in __call__
raise e
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 387, in __call__
return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc]
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1845, in _call_impl
return inner()
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1772, in inner
args_kwargs_result = hook(self, args, kwargs) # type: ignore[misc]
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 745, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 49, in _check_input_constraints_pre_hook
_check_input_constraints_for_graph(
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 347, in _check_input_constraints_for_graph
raise RuntimeError(
RuntimeError: Expected input at *args[0].shape[1] to be <= 10, but got 15
Traceback (most recent call last):
File "/pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py", line 160, in <module>
print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 2)))
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 822, in call_wrapped
return self._wrapped_call(self, *args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 400, in __call__
raise e
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 387, in __call__
return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc]
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1845, in _call_impl
return inner()
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1772, in inner
args_kwargs_result = hook(self, args, kwargs) # type: ignore[misc]
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 745, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 49, in _check_input_constraints_pre_hook
_check_input_constraints_for_graph(
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 302, in _check_input_constraints_for_graph
raise RuntimeError(
RuntimeError: Expected input at *args[1].shape[1] to be equal to 3, but got 2
.. GENERATED FROM PYTHON SOURCE LINES 166-176
Addressing Untraceable Code
^^^^^^^^^^^^^^^^^^^^^^^^^^^
As our goal is to capture the entire computational graph from a PyTorch
program, we might ultimately run into untraceable parts of programs. To
address these issues, the
`torch.export documentation <https://pytorch.org/docs/main/export.html#limitations-of-torch-export>`__,
or the
`torch.export tutorial <https://pytorch.org/tutorials/intermediate/torch_export_tutorial.html>`__
would be the best place to look.
.. GENERATED FROM PYTHON SOURCE LINES 178-195
Performing Quantization
-----------------------
To quantize a model, we first need to capture the graph with
``torch.export.export_for_training``, perform quantization, and then
call ``torch.export``. ``torch.export.export_for_training`` returns a
graph which contains ATen operators which are Autograd safe, meaning they are
safe for eager-mode training, which is needed for quantization. We will call
the graph at this level, the ``Pre-Autograd ATen Dialect`` graph.
Compared to
`FX Graph Mode Quantization <https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html>`__,
we will need to call two new APIs: ``prepare_pt2e`` and ``convert_pt2e``
instead of ``prepare_fx`` and ``convert_fx``. It differs in that
``prepare_pt2e`` takes a backend-specific ``Quantizer`` as an argument, which
will annotate the nodes in the graph with information needed to quantize the
model properly for a specific backend.
.. GENERATED FROM PYTHON SOURCE LINES 195-220
.. code-block:: default
from torch.export import export_for_training
example_args = (torch.randn(1, 3, 256, 256),)
pre_autograd_aten_dialect = export_for_training(SimpleConv(), example_args).module()
print("Pre-Autograd ATen Dialect Graph")
print(pre_autograd_aten_dialect)
from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
get_symmetric_quantization_config,
XNNPACKQuantizer,
)
quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
prepared_graph = prepare_pt2e(pre_autograd_aten_dialect, quantizer)
# calibrate with a sample dataset
converted_graph = convert_pt2e(prepared_graph)
print("Quantized Graph")
print(converted_graph)
aten_dialect: ExportedProgram = export(converted_graph, example_args)
print("ATen Dialect Graph")
print(aten_dialect)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Pre-Autograd ATen Dialect Graph
GraphModule(
(conv): Module()
)
def forward(self, x):
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
conv_weight = self.conv.weight
conv_bias = self.conv.bias
conv2d = torch.ops.aten.conv2d.default(x, conv_weight, conv_bias, [1, 1], [1, 1]); x = conv_weight = conv_bias = None
relu = torch.ops.aten.relu.default(conv2d); conv2d = None
return pytree.tree_unflatten((relu,), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/ao/quantization/utils.py:408: UserWarning: must run observer before calling calculate_qparams. Returning default values.
warnings.warn(
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/ao/quantization/observer.py:1318: UserWarning: must run observer before calling calculate_qparams. Returning default scale and zero point
warnings.warn(
Quantized Graph
GraphModule(
(conv): Module()
)
def forward(self, x):
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
_frozen_param0 = self._frozen_param0
dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(_frozen_param0, 1.0, 0, -127, 127, torch.int8); _frozen_param0 = None
conv_bias = self.conv.bias
quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 1.0, 0, -128, 127, torch.int8); x = None
dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 1.0, 0, -128, 127, torch.int8); quantize_per_tensor_default_1 = None
conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_1, dequantize_per_tensor_default, conv_bias, [1, 1], [1, 1]); dequantize_per_tensor_default_1 = dequantize_per_tensor_default = conv_bias = None
relu = torch.ops.aten.relu.default(conv2d); conv2d = None
quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 1.0, 0, -128, 127, torch.int8); relu = None
dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 1.0, 0, -128, 127, torch.int8); quantize_per_tensor_default_2 = None
return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec)
# To see more debug info, please use `graph_module.print_readable()`
ATen Dialect Graph
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_conv_bias: "f32[16]", b__frozen_param0: "i8[16, 3, 3, 3]", x: "f32[1, 3, 256, 256]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
dequantize_per_tensor: "f32[16, 3, 3, 3]" = torch.ops.quantized_decomposed.dequantize_per_tensor.default(b__frozen_param0, 1.0, 0, -127, 127, torch.int8); b__frozen_param0 = None
# File: <eval_with_key>.203:9 in forward, code: quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 1.0, 0, -128, 127, torch.int8); x = None
quantize_per_tensor: "i8[1, 3, 256, 256]" = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 1.0, 0, -128, 127, torch.int8); x = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
dequantize_per_tensor_1: "f32[1, 3, 256, 256]" = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 1.0, 0, -128, 127, torch.int8); quantize_per_tensor = None
conv2d: "f32[1, 16, 256, 256]" = torch.ops.aten.conv2d.default(dequantize_per_tensor_1, dequantize_per_tensor, p_conv_bias, [1, 1], [1, 1]); dequantize_per_tensor_1 = dequantize_per_tensor = p_conv_bias = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
relu: "f32[1, 16, 256, 256]" = torch.ops.aten.relu.default(conv2d); conv2d = None
quantize_per_tensor_1: "i8[1, 16, 256, 256]" = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 1.0, 0, -128, 127, torch.int8); relu = None
# File: <eval_with_key>.203:14 in forward, code: dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 1.0, 0, -128, 127, torch.int8); quantize_per_tensor_default_2 = None
dequantize_per_tensor_2: "f32[1, 16, 256, 256]" = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_1, 1.0, 0, -128, 127, torch.int8); quantize_per_tensor_1 = None
return (dequantize_per_tensor_2,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b__frozen_param0'), target='_frozen_param0', persistent=True), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='dequantize_per_tensor_2'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 221-224
More information on how to quantize a model, and how a backend can implement a
``Quantizer`` can be found
`here <https://pytorch.org/docs/main/quantization.html#prototype-pytorch-2-export-quantization>`__.
.. GENERATED FROM PYTHON SOURCE LINES 226-242
Lowering to Edge Dialect
------------------------
After exporting and lowering the graph to the ``ATen Dialect``, the next step
is to lower to the ``Edge Dialect``, in which specializations that are useful
for edge devices but not necessary for general (server) environments will be
applied.
Some of these specializations include:
- DType specialization
- Scalar to tensor conversion
- Converting all ops to the ``executorch.exir.dialects.edge`` namespace.
Note that this dialect is still backend (or target) agnostic.
The lowering is done through the ``to_edge`` API.
.. GENERATED FROM PYTHON SOURCE LINES 242-252
.. code-block:: default
from executorch.exir import EdgeProgramManager, to_edge
example_args = (torch.randn(1, 3, 256, 256),)
aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
print("Edge Dialect Graph")
print(edge_program.exported_program())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Edge Dialect Graph
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
aten_convolution_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_convolution_default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1); x = p_conv_weight = p_conv_bias = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
aten_relu_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_relu_default(aten_convolution_default); aten_convolution_default = None
return (aten_relu_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_relu_default'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 253-257
``to_edge()`` returns an ``EdgeProgramManager`` object, which contains the
exported programs which will be placed on this device. This data structure
allows users to export multiple programs and combine them into one binary. If
there is only one program, it will by default be saved to the name "forward".
.. GENERATED FROM PYTHON SOURCE LINES 257-282
.. code-block:: default
class Encode(torch.nn.Module):
def forward(self, x):
return torch.nn.functional.linear(x, torch.randn(5, 10))
class Decode(torch.nn.Module):
def forward(self, x):
return torch.nn.functional.linear(x, torch.randn(10, 5))
encode_args = (torch.randn(1, 10),)
aten_encode: ExportedProgram = export(Encode(), encode_args)
decode_args = (torch.randn(1, 5),)
aten_decode: ExportedProgram = export(Decode(), decode_args)
edge_program: EdgeProgramManager = to_edge(
{"encode": aten_encode, "decode": aten_decode}
)
for method in edge_program.methods:
print(f"Edge Dialect graph of {method}")
print(edge_program.exported_program(method))
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Edge Dialect graph of encode
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[1, 10]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:261 in forward, code: return torch.nn.functional.linear(x, torch.randn(5, 10))
aten_randn_default: "f32[5, 10]" = executorch_exir_dialects_edge__ops_aten_randn_default([5, 10], device = device(type='cpu'), pin_memory = False)
aten_permute_copy_default: "f32[10, 5]" = executorch_exir_dialects_edge__ops_aten_permute_copy_default(aten_randn_default, [1, 0]); aten_randn_default = None
aten_mm_default: "f32[1, 5]" = executorch_exir_dialects_edge__ops_aten_mm_default(x, aten_permute_copy_default); x = aten_permute_copy_default = None
return (aten_mm_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_mm_default'), target=None)])
Range constraints: {}
Edge Dialect graph of decode
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[1, 5]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:266 in forward, code: return torch.nn.functional.linear(x, torch.randn(10, 5))
aten_randn_default: "f32[10, 5]" = executorch_exir_dialects_edge__ops_aten_randn_default([10, 5], device = device(type='cpu'), pin_memory = False)
aten_permute_copy_default: "f32[5, 10]" = executorch_exir_dialects_edge__ops_aten_permute_copy_default(aten_randn_default, [1, 0]); aten_randn_default = None
aten_mm_default: "f32[1, 10]" = executorch_exir_dialects_edge__ops_aten_mm_default(x, aten_permute_copy_default); x = aten_permute_copy_default = None
return (aten_mm_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_mm_default'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 283-292
We can also run additional passes on the exported program through
the ``transform`` API. An in-depth documentation on how to write
transformations can be found
`here <../compiler-custom-compiler-passes.html>`__.
Note that since the graph is now in the Edge Dialect, all passes must also
result in a valid Edge Dialect graph (specifically one thing to point out is
that the operators are now in the ``executorch.exir.dialects.edge`` namespace,
rather than the ``torch.ops.aten`` namespace.
.. GENERATED FROM PYTHON SOURCE LINES 292-317
.. code-block:: default
example_args = (torch.randn(1, 3, 256, 256),)
aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
print("Edge Dialect Graph")
print(edge_program.exported_program())
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass
class ConvertReluToSigmoid(ExportPass):
def call_operator(self, op, args, kwargs, meta):
if op == exir_ops.edge.aten.relu.default:
return super().call_operator(
exir_ops.edge.aten.sigmoid.default, args, kwargs, meta
)
else:
return super().call_operator(op, args, kwargs, meta)
transformed_edge_program = edge_program.transform((ConvertReluToSigmoid(),))
print("Transformed Edge Dialect Graph")
print(transformed_edge_program.exported_program())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Edge Dialect Graph
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
aten_convolution_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_convolution_default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1); x = p_conv_weight = p_conv_bias = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
aten_relu_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_relu_default(aten_convolution_default); aten_convolution_default = None
return (aten_relu_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_relu_default'), target=None)])
Range constraints: {}
Transformed Edge Dialect Graph
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
aten_convolution_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_convolution_default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1); x = p_conv_weight = p_conv_bias = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
aten_sigmoid_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_sigmoid_default(aten_convolution_default); aten_convolution_default = None
return (aten_sigmoid_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_sigmoid_default'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 318-322
Note: if you see error like ``torch._export.verifier.SpecViolationError:
Operator torch._ops.aten._native_batch_norm_legit_functional.default is not
Aten Canonical``,
please file an issue in https://github.com/pytorch/executorch/issues and we're happy to help!
.. GENERATED FROM PYTHON SOURCE LINES 325-340
Delegating to a Backend
-----------------------
We can now delegate parts of the graph or the whole graph to a third-party
backend through the ``to_backend`` API. An in-depth documentation on the
specifics of backend delegation, including how to delegate to a backend and
how to implement a backend, can be found
`here <../compiler-delegate-and-partitioner.html>`__.
There are three ways for using this API:
1. We can lower the whole module.
2. We can take the lowered module, and insert it in another larger module.
3. We can partition the module into subgraphs that are lowerable, and then
lower those subgraphs to a backend.
.. GENERATED FROM PYTHON SOURCE LINES 342-348
Lowering the Whole Module
^^^^^^^^^^^^^^^^^^^^^^^^^
To lower an entire module, we can pass ``to_backend`` the backend name, the
module to be lowered, and a list of compile specs to help the backend with the
lowering process.
.. GENERATED FROM PYTHON SOURCE LINES 348-385
.. code-block:: default
class LowerableModule(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
# Export and lower the module to Edge Dialect
example_args = (torch.ones(1),)
aten_dialect: ExportedProgram = export(LowerableModule(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
to_be_lowered_module = edge_program.exported_program()
from executorch.exir.backend.backend_api import LoweredBackendModule, to_backend
# Import the backend
from executorch.exir.backend.test.backend_with_compiler_demo import ( # noqa
BackendWithCompilerDemo,
)
# Lower the module
lowered_module: LoweredBackendModule = to_backend(
"BackendWithCompilerDemo", to_be_lowered_module, []
)
print(lowered_module)
print(lowered_module.backend_id)
print(lowered_module.processed_bytes)
print(lowered_module.original_module)
# Serialize and save it to a file
save_path = "delegate.pte"
with open(save_path, "wb") as f:
f.write(lowered_module.buffer())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
LoweredBackendModule()
BackendWithCompilerDemo
b'1version:0#op:demo::aten.sin.default, numel:1, dtype:torch.float32<debug_handle>2#'
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[1]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:355 in forward, code: return torch.sin(x)
aten_sin_default: "f32[1]" = executorch_exir_dialects_edge__ops_aten_sin_default(x); x = None
return (aten_sin_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_sin_default'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 386-394
In this call, ``to_backend`` will return a ``LoweredBackendModule``. Some
important attributes of the ``LoweredBackendModule`` are:
- ``backend_id``: The name of the backend this lowered module will run on in
the runtime
- ``processed_bytes``: a binary blob which will tell the backend how to run
this program in the runtime
- ``original_module``: the original exported module
.. GENERATED FROM PYTHON SOURCE LINES 396-401
Compose the Lowered Module into Another Module
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In cases where we want to reuse this lowered module in multiple programs, we
can compose this lowered module with another module.
.. GENERATED FROM PYTHON SOURCE LINES 401-436
.. code-block:: default
class NotLowerableModule(torch.nn.Module):
def __init__(self, bias):
super().__init__()
self.bias = bias
def forward(self, a, b):
return torch.add(torch.add(a, b), self.bias)
class ComposedModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.non_lowerable = NotLowerableModule(torch.ones(1) * 0.3)
self.lowerable = lowered_module
def forward(self, x):
a = self.lowerable(x)
b = self.lowerable(a)
ret = self.non_lowerable(a, b)
return a, b, ret
example_args = (torch.ones(1),)
aten_dialect: ExportedProgram = export(ComposedModule(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
exported_program = edge_program.exported_program()
print("Edge Dialect graph")
print(exported_program)
print("Lowered Module within the graph")
print(exported_program.graph_module.lowered_module_0.backend_id)
print(exported_program.graph_module.lowered_module_0.processed_bytes)
print(exported_program.graph_module.lowered_module_0.original_module)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py:75: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
getattr_node = gm.graph.get_attr(lifted_node)
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph.py:1801: UserWarning: Node non_lowerable_bias target non_lowerable.bias bias of non_lowerable does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
warnings.warn(
Edge Dialect graph
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, c_non_lowerable_bias: "f32[1]", x: "f32[1]"):
# File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/executorch/exir/lowered_backend_module.py:344 in forward, code: return executorch_call_delegate(self, *args)
lowered_module_0 = self.lowered_module_0
executorch_call_delegate: "f32[1]" = torch.ops.higher_order.executorch_call_delegate(lowered_module_0, x); lowered_module_0 = x = None
# File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/executorch/exir/lowered_backend_module.py:344 in forward, code: return executorch_call_delegate(self, *args)
lowered_module_1 = self.lowered_module_0
executorch_call_delegate_1: "f32[1]" = torch.ops.higher_order.executorch_call_delegate(lowered_module_1, executorch_call_delegate); lowered_module_1 = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:409 in forward, code: return torch.add(torch.add(a, b), self.bias)
aten_add_tensor: "f32[1]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(executorch_call_delegate, executorch_call_delegate_1)
aten_add_tensor_1: "f32[1]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_add_tensor, c_non_lowerable_bias); aten_add_tensor = c_non_lowerable_bias = None
return (executorch_call_delegate, executorch_call_delegate_1, aten_add_tensor_1)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.CONSTANT_TENSOR: 4>, arg=TensorArgument(name='c_non_lowerable_bias'), target='non_lowerable.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='executorch_call_delegate'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='executorch_call_delegate_1'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}
Lowered Module within the graph
BackendWithCompilerDemo
b'1version:0#op:demo::aten.sin.default, numel:1, dtype:torch.float32<debug_handle>2#'
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[1]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:355 in forward, code: return torch.sin(x)
aten_sin_default: "f32[1]" = executorch_exir_dialects_edge__ops_aten_sin_default(x); x = None
return (aten_sin_default,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_sin_default'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 437-441
Notice that there is now a ``torch.ops.higher_order.executorch_call_delegate`` node in the
graph, which is calling ``lowered_module_0``. Additionally, the contents of
``lowered_module_0`` are the same as the ``lowered_module`` we created
previously.
.. GENERATED FROM PYTHON SOURCE LINES 443-451
Partition and Lower Parts of a Module
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A separate lowering flow is to pass ``to_backend`` the module that we want to
lower, and a backend-specific partitioner. ``to_backend`` will use the
backend-specific partitioner to tag nodes in the module which are lowerable,
partition those nodes into subgraphs, and then create a
``LoweredBackendModule`` for each of those subgraphs.
.. GENERATED FROM PYTHON SOURCE LINES 451-478
.. code-block:: default
class Foo(torch.nn.Module):
def forward(self, a, x, b):
y = torch.mm(a, x)
z = y + b
a = z - a
y = torch.mm(a, x)
z = y + b
return z
example_args = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
aten_dialect: ExportedProgram = export(Foo(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
exported_program = edge_program.exported_program()
print("Edge Dialect graph")
print(exported_program)
from executorch.exir.backend.test.op_partitioner_demo import AddMulPartitionerDemo
delegated_program = to_backend(exported_program, AddMulPartitionerDemo())
print("Delegated program")
print(delegated_program)
print(delegated_program.graph_module.lowered_module_0.original_module)
print(delegated_program.graph_module.lowered_module_1.original_module)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Edge Dialect graph
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:455 in forward, code: y = torch.mm(a, x)
aten_mm_default: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(a, x)
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:456 in forward, code: z = y + b
aten_add_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default, b); aten_mm_default = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:457 in forward, code: a = z - a
aten_sub_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_sub_Tensor(aten_add_tensor, a); aten_add_tensor = a = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:458 in forward, code: y = torch.mm(a, x)
aten_mm_default_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(aten_sub_tensor, x); aten_sub_tensor = x = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:459 in forward, code: z = y + b
aten_add_tensor_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default_1, b); aten_mm_default_1 = b = None
return (aten_add_tensor_1,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}
Delegated program
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
# No stacktrace found for following nodes
lowered_module_0 = self.lowered_module_0
lowered_module_1 = self.lowered_module_1
executorch_call_delegate_1 = torch.ops.higher_order.executorch_call_delegate(lowered_module_1, a, x, b); lowered_module_1 = None
getitem_1: "f32[2, 2]" = executorch_call_delegate_1[0]; executorch_call_delegate_1 = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:457 in forward, code: a = z - a
aten_sub_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_sub_Tensor(getitem_1, a); getitem_1 = a = None
# No stacktrace found for following nodes
executorch_call_delegate = torch.ops.higher_order.executorch_call_delegate(lowered_module_0, aten_sub_tensor, x, b); lowered_module_0 = aten_sub_tensor = x = b = None
getitem: "f32[2, 2]" = executorch_call_delegate[0]; executorch_call_delegate = None
return (getitem,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, aten_sub_tensor: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:458 in forward, code: y = torch.mm(a, x)
aten_mm_default_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(aten_sub_tensor, x); aten_sub_tensor = x = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:459 in forward, code: z = y + b
aten_add_tensor_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default_1, b); aten_mm_default_1 = b = None
return [aten_add_tensor_1]
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='aten_sub_tensor'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:455 in forward, code: y = torch.mm(a, x)
aten_mm_default: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(a, x); a = x = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:456 in forward, code: z = y + b
aten_add_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default, b); aten_mm_default = b = None
return [aten_add_tensor]
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 479-485
Notice that there are now 2 ``torch.ops.higher_order.executorch_call_delegate`` nodes in the
graph, one containing the operations `add, mul` and the other containing the
operations `mul, add`.
Alternatively, a more cohesive API to lower parts of a module is to directly
call ``to_backend`` on it:
.. GENERATED FROM PYTHON SOURCE LINES 485-506
.. code-block:: default
class Foo(torch.nn.Module):
def forward(self, a, x, b):
y = torch.mm(a, x)
z = y + b
a = z - a
y = torch.mm(a, x)
z = y + b
return z
example_args = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
aten_dialect: ExportedProgram = export(Foo(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
exported_program = edge_program.exported_program()
delegated_program = edge_program.to_backend(AddMulPartitionerDemo())
print("Delegated program")
print(delegated_program.exported_program())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Delegated program
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
# No stacktrace found for following nodes
lowered_module_0 = self.lowered_module_0
lowered_module_1 = self.lowered_module_1
executorch_call_delegate_1 = torch.ops.higher_order.executorch_call_delegate(lowered_module_1, a, x, b); lowered_module_1 = None
getitem_1: "f32[2, 2]" = executorch_call_delegate_1[0]; executorch_call_delegate_1 = None
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:491 in forward, code: a = z - a
aten_sub_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_sub_Tensor(getitem_1, a); getitem_1 = a = None
# No stacktrace found for following nodes
executorch_call_delegate = torch.ops.higher_order.executorch_call_delegate(lowered_module_0, aten_sub_tensor, x, b); lowered_module_0 = aten_sub_tensor = x = b = None
getitem: "f32[2, 2]" = executorch_call_delegate[0]; executorch_call_delegate = None
return (getitem,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 507-519
Running User-Defined Passes and Memory Planning
-----------------------------------------------
As a final step of lowering, we can use the ``to_executorch()`` API to pass in
backend-specific passes, such as replacing sets of operators with a custom
backend operator, and a memory planning pass, to tell the runtime how to
allocate memory ahead of time when running the program.
A default memory planning pass is provided, but we can also choose a
backend-specific memory planning pass if it exists. More information on
writing a custom memory planning pass can be found
`here <../compiler-memory-planning.html>`__
.. GENERATED FROM PYTHON SOURCE LINES 519-535
.. code-block:: default
from executorch.exir import ExecutorchBackendConfig, ExecutorchProgramManager
from executorch.exir.passes import MemoryPlanningPass
executorch_program: ExecutorchProgramManager = edge_program.to_executorch(
ExecutorchBackendConfig(
passes=[], # User-defined passes
memory_planning_pass=MemoryPlanningPass(), # Default memory planning pass
)
)
print("ExecuTorch Dialect")
print(executorch_program.exported_program())
import executorch.exir as exir
.. rst-class:: sphx-glr-script-out
.. code-block:: none
ExecuTorch Dialect
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
# No stacktrace found for following nodes
alloc: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:489 in forward, code: y = torch.mm(a, x)
aten_mm_default: "f32[2, 2]" = torch.ops.aten.mm.out(a, x, out = alloc); alloc = None
# No stacktrace found for following nodes
alloc_1: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:490 in forward, code: z = y + b
aten_add_tensor: "f32[2, 2]" = torch.ops.aten.add.out(aten_mm_default, b, out = alloc_1); aten_mm_default = alloc_1 = None
# No stacktrace found for following nodes
alloc_2: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:491 in forward, code: a = z - a
aten_sub_tensor: "f32[2, 2]" = torch.ops.aten.sub.out(aten_add_tensor, a, out = alloc_2); aten_add_tensor = a = alloc_2 = None
# No stacktrace found for following nodes
alloc_3: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:492 in forward, code: y = torch.mm(a, x)
aten_mm_default_1: "f32[2, 2]" = torch.ops.aten.mm.out(aten_sub_tensor, x, out = alloc_3); aten_sub_tensor = x = alloc_3 = None
# No stacktrace found for following nodes
alloc_4: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))
# File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:493 in forward, code: z = y + b
aten_add_tensor_1: "f32[2, 2]" = torch.ops.aten.add.out(aten_mm_default_1, b, out = alloc_4); aten_mm_default_1 = b = alloc_4 = None
return (aten_add_tensor_1,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}
.. GENERATED FROM PYTHON SOURCE LINES 536-552
Notice that in the graph we now see operators like ``torch.ops.aten.sub.out``
and ``torch.ops.aten.div.out`` rather than ``torch.ops.aten.sub.Tensor`` and
``torch.ops.aten.div.Tensor``.
This is because between running the backend passes and memory planning passes,
to prepare the graph for memory planning, an out-variant pass is run on
the graph to convert all of the operators to their out variants. Instead of
allocating returned tensors in the kernel implementations, an operator's
``out`` variant will take in a prealloacated tensor to its out kwarg, and
store the result there, making it easier for memory planners to do tensor
lifetime analysis.
We also insert ``alloc`` nodes into the graph containing calls to a special
``executorch.exir.memory.alloc`` operator. This tells us how much memory is
needed to allocate each tensor output by the out-variant operator.
.. GENERATED FROM PYTHON SOURCE LINES 554-561
Saving to a File
----------------
Finally, we can save the ExecuTorch Program to a file and load it to a device
to be run.
Here is an example for an entire end-to-end workflow:
.. GENERATED FROM PYTHON SOURCE LINES 561-593
.. code-block:: default
import torch
from torch.export import export, export_for_training, ExportedProgram
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return self.linear(x + self.param).clamp(min=0.0, max=1.0)
example_args = (torch.randn(3, 4),)
pre_autograd_aten_dialect = export_for_training(M(), example_args).module()
# Optionally do quantization:
# pre_autograd_aten_dialect = convert_pt2e(prepare_pt2e(pre_autograd_aten_dialect, CustomBackendQuantizer))
aten_dialect: ExportedProgram = export(pre_autograd_aten_dialect, example_args)
edge_program: exir.EdgeProgramManager = exir.to_edge(aten_dialect)
# Optionally do delegation:
# edge_program = edge_program.to_backend(CustomBackendPartitioner)
executorch_program: exir.ExecutorchProgramManager = edge_program.to_executorch(
ExecutorchBackendConfig(
passes=[], # User-defined passes
)
)
with open("model.pte", "wb") as file:
file.write(executorch_program.buffer)
.. GENERATED FROM PYTHON SOURCE LINES 594-609
Conclusion
----------
In this tutorial, we went over the APIs and steps required to lower a PyTorch
program to a file that can be run on the ExecuTorch runtime.
Links Mentioned
^^^^^^^^^^^^^^^
- `torch.export Documentation <https://pytorch.org/docs/2.1/export.html>`__
- `Quantization Documentation <https://pytorch.org/docs/main/quantization.html#prototype-pytorch-2-export-quantization>`__
- `IR Spec <../ir-exir.html>`__
- `Writing Compiler Passes + Partitioner Documentation <../compiler-custom-compiler-passes.html>`__
- `Backend Delegation Documentation <../compiler-delegate-and-partitioner.html>`__
- `Memory Planning Documentation <../compiler-memory-planning.html>`__
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 3.236 seconds)
.. _sphx_glr_download_tutorials_export-to-executorch-tutorial.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: export-to-executorch-tutorial.py <export-to-executorch-tutorial.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: export-to-executorch-tutorial.ipynb <export-to-executorch-tutorial.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_