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torch.export

Warning

This feature is a prototype under active development and there WILL BE BREAKING CHANGES in the future.

Overview

:func:`torch.export.export` takes an arbitrary Python callable (a :class:`torch.nn.Module`, a function or a method) and produces a traced graph representing only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion, which can subsequently be executed with different outputs or serialized.

import torch
from torch.export import export

class Mod(torch.nn.Module):
    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        a = torch.sin(x)
        b = torch.cos(y)
        return a + b

example_args = (torch.randn(10, 10), torch.randn(10, 10))

exported_program: torch.export.ExportedProgram = export(
    Mod(), args=example_args
)
print(exported_program)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[10, 10], arg1_1: f32[10, 10]):
            # code: a = torch.sin(x)
            sin: f32[10, 10] = torch.ops.aten.sin.default(arg0_1);

            # code: b = torch.cos(y)
            cos: f32[10, 10] = torch.ops.aten.cos.default(arg1_1);

            # code: return a + b
            add: f32[10, 10] = torch.ops.aten.add.Tensor(sin, cos);
            return (add,)

    Graph signature: ExportGraphSignature(
        parameters=[],
        buffers=[],
        user_inputs=['arg0_1', 'arg1_1'],
        user_outputs=['add'],
        inputs_to_parameters={},
        inputs_to_buffers={},
        buffers_to_mutate={},
        backward_signature=None,
        assertion_dep_token=None,
    )
    Range constraints: {}

torch.export produces a clean intermediate representation (IR) with the following invariants. More specifications about the IR can be found :ref:`here <export.ir_spec>`.

  • Soundness: It is guaranteed to be a sound representation of the original program, and maintains the same calling conventions of the original program.
  • Normalized: There are no Python semantics within the graph. Submodules from the original programs are inlined to form one fully flattened computational graph.
  • Graph properties: The graph is purely functional, meaning it does not contain operations with side effects such as mutations or aliasing. It does not mutate any intermediate values, parameters, or buffers.
  • Metadata: The graph contains metadata captured during tracing, such as a stacktrace from user's code.

Under the hood, torch.export leverages the following latest technologies:

  • TorchDynamo (torch._dynamo) is an internal API that uses a CPython feature called the Frame Evaluation API to safely trace PyTorch graphs. This provides a massively improved graph capturing experience, with much fewer rewrites needed in order to fully trace the PyTorch code.
  • AOT Autograd provides a functionalized PyTorch graph and ensures the graph is decomposed/lowered to the ATen operator set.
  • Torch FX (torch.fx) is the underlying representation of the graph, allowing flexible Python-based transformations.

Existing frameworks

:func:`torch.compile` also utilizes the same PT2 stack as torch.export, but is slightly different:

  • JIT vs. AOT: :func:`torch.compile` is a JIT compiler whereas which is not intended to be used to produce compiled artifacts outside of deployment.
  • Partial vs. Full Graph Capture: When :func:`torch.compile` runs into an untraceable part of a model, it will "graph break" and fall back to running the program in the eager Python runtime. In comparison, torch.export aims to get a full graph representation of a PyTorch model, so it will error out when something untraceable is reached. Since torch.export produces a full graph disjoint from any Python features or runtime, this graph can then be saved, loaded, and run in different environments and languages.
  • Usability tradeoff: Since :func:`torch.compile` is able to fallback to the Python runtime whenever it reaches something untraceable, it is a lot more flexible. torch.export will instead require users to provide more information or rewrite their code to make it traceable.

Compared to :func:`torch.fx.symbolic_trace`, torch.export traces using TorchDynamo which operates at the Python bytecode level, giving it the ability to trace arbitrary Python constructs not limited by what Python operator overloading supports. Additionally, torch.export keeps fine-grained track of tensor metadata, so that conditionals on things like tensor shapes do not fail tracing. In general, torch.export is expected to work on more user programs, and produce lower-level graphs (at the torch.ops.aten operator level). Note that users can still use :func:`torch.fx.symbolic_trace` as a preprocessing step before torch.export.

Compared to :func:`torch.jit.script`, torch.export does not capture Python control flow or data structures, but it supports more Python language features than TorchScript (as it is easier to have comprehensive coverage over Python bytecodes). The resulting graphs are simpler and only have straight line control flow (except for explicit control flow operators).

Compared to :func:`torch.jit.trace`, torch.export is sound: it is able to trace code that performs integer computation on sizes and records all of the side-conditions necessary to show that a particular trace is valid for other inputs.

Exporting a PyTorch Model

An Example

The main entrypoint is through :func:`torch.export.export`, which takes a callable (:class:`torch.nn.Module`, function, or method) and sample inputs, and captures the computation graph into an :class:`torch.export.ExportedProgram`. An example:

import torch
from torch.export import export

# Simple module for demonstration
class M(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()
        self.maxpool = torch.nn.MaxPool2d(kernel_size=3)

    def forward(self, x: torch.Tensor, *, constant=None) -> torch.Tensor:
        a = self.conv(x)
        a.add_(constant)
        return self.maxpool(self.relu(a))

example_args = (torch.randn(1, 3, 256, 256),)
example_kwargs = {"constant": torch.ones(1, 16, 256, 256)}

exported_program: torch.export.ExportedProgram = export(
    M(), args=example_args, kwargs=example_kwargs
)
print(exported_program)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[16, 3, 3, 3], arg1_1: f32[16], arg2_1: f32[1, 3, 256, 256], arg3_1: f32[1, 16, 256, 256]):

            # code: a = self.conv(x)
            convolution: f32[1, 16, 256, 256] = torch.ops.aten.convolution.default(
                arg2_1, arg0_1, arg1_1, [1, 1], [1, 1], [1, 1], False, [0, 0], 1
            );

            # code: a.add_(constant)
            add: f32[1, 16, 256, 256] = torch.ops.aten.add.Tensor(convolution, arg3_1);

            # code: return self.maxpool(self.relu(a))
            relu: f32[1, 16, 256, 256] = torch.ops.aten.relu.default(add);
            max_pool2d_with_indices = torch.ops.aten.max_pool2d_with_indices.default(
                relu, [3, 3], [3, 3]
            );
            getitem: f32[1, 16, 85, 85] = max_pool2d_with_indices[0];
            return (getitem,)

    Graph signature: ExportGraphSignature(
        parameters=['L__self___conv.weight', 'L__self___conv.bias'],
        buffers=[],
        user_inputs=['arg2_1', 'arg3_1'],
        user_outputs=['getitem'],
        inputs_to_parameters={
            'arg0_1': 'L__self___conv.weight',
            'arg1_1': 'L__self___conv.bias',
        },
        inputs_to_buffers={},
        buffers_to_mutate={},
        backward_signature=None,
        assertion_dep_token=None,
    )
    Range constraints: {}

Inspecting the ExportedProgram, we can note the following:

  • The :class:`torch.fx.Graph` contains the computation graph of the original program, along with records of the original code for easy debugging.
  • The graph contains only torch.ops.aten operators found here and custom operators, and is fully functional, without any inplace operators such as torch.add_.
  • The parameters (weight and bias to conv) are lifted as inputs to the graph, resulting in no get_attr nodes in the graph, which previously existed in the result of :func:`torch.fx.symbolic_trace`.
  • The :class:`torch.export.ExportGraphSignature` models the input and output signature, along with specifying which inputs are parameters.
  • The resulting shape and dtype of tensors produced by each node in the graph is noted. For example, the convolution node will result in a tensor of dtype torch.float32 and shape (1, 16, 256, 256).

Non-Strict Export

In PyTorch 2.3, we introduced a new mode of tracing called non-strict mode. It's still going through hardening, so if you run into any issues, please file them to Github with the "oncall: export" tag.

In non-strict mode, we trace through the program using the Python interpreter. Your code will execute exactly as it would in eager mode; the only difference is that all Tensor objects will be replaced by ProxyTensors, which will record all their operations into a graph.

In strict mode, which is currently the default, we first trace through the program using TorchDynamo, a bytecode analysis engine. TorchDynamo does not actually execute your Python code. Instead, it symbolically analyzes it and builds a graph based on the results. This analysis allows torch.export to provide stronger guarantees about safety, but not all Python code is supported.

An example of a case where one might want to use non-strict mode is if you run into a unsupported TorchDynamo feature that might not be easily solved, and you know the python code is not exactly needed for computation. For example:

import contextlib
import torch

class ContextManager():
    def __init__(self):
        self.count = 0
    def __enter__(self):
        self.count += 1
    def __exit__(self, exc_type, exc_value, traceback):
        self.count -= 1

class M(torch.nn.Module):
    def forward(self, x):
        with ContextManager():
            return x.sin() + x.cos()

export(M(), (torch.ones(3, 3),), strict=False)  # Non-strict traces successfully
export(M(), (torch.ones(3, 3),))  # Strict mode fails with torch._dynamo.exc.Unsupported: ContextManager

In this example, the first call using non-strict mode (through the strict=False flag) traces successfully whereas the second call using strict mode (default) results with a failure, where TorchDynamo is unable to support context managers. One option is to rewrite the code (see :ref:`Limitations of torch.export <Limitations of torch.export>`), but seeing as the context manager does not affect the tensor computations in the model, we can go with the non-strict mode's result.

Export for Training and Inference

In PyTorch 2.5, we introduced a new API called :func:`export_for_training`. It's still going through hardening, so if you run into any issues, please file them to Github with the "oncall: export" tag.

In this API, we produce the most generic IR that contains all ATen operators (including both functional and non-functional) which can be used to train in eager PyTorch Autograd. This API is intended for eager training use cases such as PT2 Quantization and will soon be the default IR of torch.export.export. To read further about the motivation behind this change, please refer to https://dev-discuss.pytorch.org/t/why-pytorch-does-not-need-a-new-standardized-operator-set/2206

When this API is combined with :func:`run_decompositions()`, you should be able to get inference IR with any desired decomposition behavior.

To show some examples:

class ConvBatchnorm(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv = torch.nn.Conv2d(1, 3, 1, 1)
        self.bn = torch.nn.BatchNorm2d(3)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return (x,)

mod = ConvBatchnorm()
inp = torch.randn(1, 1, 3, 3)

ep_for_training = torch.export.export_for_training(mod, (inp,))
print(ep_for_training)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[3, 1, 1, 1]", p_conv_bias: "f32[3]", p_bn_weight: "f32[3]", p_bn_bias: "f32[3]", b_bn_running_mean: "f32[3]", b_bn_running_var: "f32[3]", b_bn_num_batches_tracked: "i64[]", x: "f32[1, 1, 3, 3]"):
            conv2d: "f32[1, 3, 3, 3]" = torch.ops.aten.conv2d.default(x, p_conv_weight, p_conv_bias);  x = p_conv_weight = p_conv_bias = None
            add_: "i64[]" = torch.ops.aten.add_.Tensor(b_bn_num_batches_tracked, 1);  b_bn_num_batches_tracked = add_ = None
            batch_norm: "f32[1, 3, 3, 3]" = torch.ops.aten.batch_norm.default(conv2d, p_bn_weight, p_bn_bias, b_bn_running_mean, b_bn_running_var, True, 0.1, 1e-05, True);  conv2d = p_bn_weight = p_bn_bias = b_bn_running_mean = b_bn_running_var = None
            return (batch_norm,)

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.PARAMETER: 2>, arg=TensorArgument(name='p_bn_weight'), target='bn.weight', persistent=None),
            InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_bn_bias'), target='bn.bias', persistent=None),
            InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_running_mean'), target='bn.running_mean', persistent=True),
            InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_running_var'), target='bn.running_var', persistent=True),
            InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_num_batches_tracked'), target='bn.num_batches_tracked', 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='batch_norm'), target=None)
        ]
    )
Range constraints: {}

From the above output, you can see that :func:`export_for_training` produces pretty much the same ExportedProgram as :func:`export` except for the operators in the graph. You can see that we captured batch_norm in the most general form. This op is non-functional and will be lowered to different ops when running inference.

You can also go from this IR to an inference IR via :func:`run_decompositions` with arbitrary customizations.

# Lower to core aten inference IR, but keep conv2d
decomp_table = torch.export.default_decompositions()
del decomp_table[torch.ops.aten.conv2d.default]
ep_for_inference = ep_for_training.run_decompositions(decomp_table)

print(ep_for_inference)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[3, 1, 1, 1]", p_conv_bias: "f32[3]", p_bn_weight: "f32[3]", p_bn_bias: "f32[3]", b_bn_running_mean: "f32[3]", b_bn_running_var: "f32[3]", b_bn_num_batches_tracked: "i64[]", x: "f32[1, 1, 3, 3]"):
            conv2d: "f32[1, 3, 3, 3]" = torch.ops.aten.conv2d.default(x, p_conv_weight, p_conv_bias);  x = p_conv_weight = p_conv_bias = None
            add: "i64[]" = torch.ops.aten.add.Tensor(b_bn_num_batches_tracked, 1);  b_bn_num_batches_tracked = None
            _native_batch_norm_legit_functional = torch.ops.aten._native_batch_norm_legit_functional.default(conv2d, p_bn_weight, p_bn_bias, b_bn_running_mean, b_bn_running_var, True, 0.1, 1e-05);  conv2d = p_bn_weight = p_bn_bias = b_bn_running_mean = b_bn_running_var = None
            getitem: "f32[1, 3, 3, 3]" = _native_batch_norm_legit_functional[0]
            getitem_3: "f32[3]" = _native_batch_norm_legit_functional[3]
            getitem_4: "f32[3]" = _native_batch_norm_legit_functional[4];  _native_batch_norm_legit_functional = None
            return (getitem_3, getitem_4, add, getitem)

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.PARAMETER: 2>, arg=TensorArgument(name='p_bn_weight'), target='bn.weight', persistent=None),
        InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_bn_bias'), target='bn.bias', persistent=None),
        InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_running_mean'), target='bn.running_mean', persistent=True),
        InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_running_var'), target='bn.running_var', persistent=True),
        InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_num_batches_tracked'), target='bn.num_batches_tracked', persistent=True),
        InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)
    ],
    output_specs=[
        OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='getitem_3'), target='bn.running_mean'),
        OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='getitem_4'), target='bn.running_var'),
        OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='add'), target='bn.num_batches_tracked'),
        OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)
    ]
)
Range constraints: {}

Here you can see that we kept conv2d op in the IR while decomposing the rest. Now the IR is a functional IR containing core aten operators except for conv2d.

You can do even more customization by directly registering your chosen decomposition behaviors.

You can do even more customizations by directly registering custom decomp behaviour

# Lower to core aten inference IR, but customize conv2d
decomp_table = torch.export.default_decompositions()

def my_awesome_custom_conv2d_function(x, weight, bias, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1):
    return 2 * torch.ops.aten.convolution(x, weight, bias, stride, padding, dilation, False, [0, 0], groups)

decomp_table[torch.ops.aten.conv2d.default] = my_awesome_conv2d_function
ep_for_inference = ep_for_training.run_decompositions(decomp_table)

print(ep_for_inference)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[3, 1, 1, 1]", p_conv_bias: "f32[3]", p_bn_weight: "f32[3]", p_bn_bias: "f32[3]", b_bn_running_mean: "f32[3]", b_bn_running_var: "f32[3]", b_bn_num_batches_tracked: "i64[]", x: "f32[1, 1, 3, 3]"):
            convolution: "f32[1, 3, 3, 3]" = torch.ops.aten.convolution.default(x, p_conv_weight, p_conv_bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1);  x = p_conv_weight = p_conv_bias = None
            mul: "f32[1, 3, 3, 3]" = torch.ops.aten.mul.Tensor(convolution, 2);  convolution = None
            add: "i64[]" = torch.ops.aten.add.Tensor(b_bn_num_batches_tracked, 1);  b_bn_num_batches_tracked = None
            _native_batch_norm_legit_functional = torch.ops.aten._native_batch_norm_legit_functional.default(mul, p_bn_weight, p_bn_bias, b_bn_running_mean, b_bn_running_var, True, 0.1, 1e-05);  mul = p_bn_weight = p_bn_bias = b_bn_running_mean = b_bn_running_var = None
            getitem: "f32[1, 3, 3, 3]" = _native_batch_norm_legit_functional[0]
            getitem_3: "f32[3]" = _native_batch_norm_legit_functional[3]
            getitem_4: "f32[3]" = _native_batch_norm_legit_functional[4];  _native_batch_norm_legit_functional = None
            return (getitem_3, getitem_4, add, getitem)

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.PARAMETER: 2>, arg=TensorArgument(name='p_bn_weight'), target='bn.weight', persistent=None),
        InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_bn_bias'), target='bn.bias', persistent=None),
        InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_running_mean'), target='bn.running_mean', persistent=True),
        InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_running_var'), target='bn.running_var', persistent=True),
        InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b_bn_num_batches_tracked'), target='bn.num_batches_tracked', persistent=True),
        InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)
    ],
    output_specs=[
        OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='getitem_3'), target='bn.running_mean'),
        OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='getitem_4'), target='bn.running_var'),
        OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='add'), target='bn.num_batches_tracked'),
        OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)
    ]
)
Range constraints: {}

Expressing Dynamism

By default torch.export will trace the program assuming all input shapes are static, and specializing the exported program to those dimensions. However, some dimensions, such as a batch dimension, can be dynamic and vary from run to run. Such dimensions must be specified by using the :func:`torch.export.Dim` API to create them and by passing them into :func:`torch.export.export` through the dynamic_shapes argument. An example:

import torch
from torch.export import Dim, export

class M(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.branch1 = torch.nn.Sequential(
            torch.nn.Linear(64, 32), torch.nn.ReLU()
        )
        self.branch2 = torch.nn.Sequential(
            torch.nn.Linear(128, 64), torch.nn.ReLU()
        )
        self.buffer = torch.ones(32)

    def forward(self, x1, x2):
        out1 = self.branch1(x1)
        out2 = self.branch2(x2)
        return (out1 + self.buffer, out2)

example_args = (torch.randn(32, 64), torch.randn(32, 128))

# Create a dynamic batch size
batch = Dim("batch")
# Specify that the first dimension of each input is that batch size
dynamic_shapes = {"x1": {0: batch}, "x2": {0: batch}}

exported_program: torch.export.ExportedProgram = export(
    M(), args=example_args, dynamic_shapes=dynamic_shapes
)
print(exported_program)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[32, 64], arg1_1: f32[32], arg2_1: f32[64, 128], arg3_1: f32[64], arg4_1: f32[32], arg5_1: f32[s0, 64], arg6_1: f32[s0, 128]):

            # code: out1 = self.branch1(x1)
            permute: f32[64, 32] = torch.ops.aten.permute.default(arg0_1, [1, 0]);
            addmm: f32[s0, 32] = torch.ops.aten.addmm.default(arg1_1, arg5_1, permute);
            relu: f32[s0, 32] = torch.ops.aten.relu.default(addmm);

            # code: out2 = self.branch2(x2)
            permute_1: f32[128, 64] = torch.ops.aten.permute.default(arg2_1, [1, 0]);
            addmm_1: f32[s0, 64] = torch.ops.aten.addmm.default(arg3_1, arg6_1, permute_1);
            relu_1: f32[s0, 64] = torch.ops.aten.relu.default(addmm_1);  addmm_1 = None

            # code: return (out1 + self.buffer, out2)
            add: f32[s0, 32] = torch.ops.aten.add.Tensor(relu, arg4_1);
            return (add, relu_1)

    Graph signature: ExportGraphSignature(
        parameters=[
            'branch1.0.weight',
            'branch1.0.bias',
            'branch2.0.weight',
            'branch2.0.bias',
        ],
        buffers=['L__self___buffer'],
        user_inputs=['arg5_1', 'arg6_1'],
        user_outputs=['add', 'relu_1'],
        inputs_to_parameters={
            'arg0_1': 'branch1.0.weight',
            'arg1_1': 'branch1.0.bias',
            'arg2_1': 'branch2.0.weight',
            'arg3_1': 'branch2.0.bias',
        },
        inputs_to_buffers={'arg4_1': 'L__self___buffer'},
        buffers_to_mutate={},
        backward_signature=None,
        assertion_dep_token=None,
    )
    Range constraints: {s0: RangeConstraint(min_val=2, max_val=9223372036854775806)}

Some additional things to note:

  • Through the :func:`torch.export.Dim` API and the dynamic_shapes argument, we specified the first dimension of each input to be dynamic. Looking at the inputs arg5_1 and arg6_1, they have a symbolic shape of (s0, 64) and (s0, 128), instead of the (32, 64) and (32, 128) shaped tensors that we passed in as example inputs. s0 is a symbol representing that this dimension can be a range of values.
  • exported_program.range_constraints describes the ranges of each symbol appearing in the graph. In this case, we see that s0 has the range [2, inf]. For technical reasons that are difficult to explain here, they are assumed to be not 0 or 1. This is not a bug, and does not necessarily mean that the exported program will not work for dimensions 0 or 1. See The 0/1 Specialization Problem for an in-depth discussion of this topic.

We can also specify more expressive relationships between input shapes, such as where a pair of shapes might differ by one, a shape might be double of another, or a shape is even. An example:

class M(torch.nn.Module):
    def forward(self, x, y):
        return x + y[1:]

x, y = torch.randn(5), torch.randn(6)
dimx = torch.export.Dim("dimx", min=3, max=6)
dimy = dimx + 1

exported_program = torch.export.export(
    M(), (x, y), dynamic_shapes=({0: dimx}, {0: dimy}),
)
print(exported_program)

ExportedProgram:
class GraphModule(torch.nn.Module):
    def forward(self, arg0_1: "f32[s0]", arg1_1: "f32[s0 + 1]"):
        # code: return x + y[1:]
        slice_1: "f32[s0]" = torch.ops.aten.slice.Tensor(arg1_1, 0, 1, 9223372036854775807);  arg1_1 = None
        add: "f32[s0]" = torch.ops.aten.add.Tensor(arg0_1, slice_1);  arg0_1 = slice_1 = None
        return (add,)

Graph signature: ExportGraphSignature(
    input_specs=[
        InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None),
        InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None)
    ],
    output_specs=[
        OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)]
)
Range constraints: {s0: ValueRanges(lower=3, upper=6, is_bool=False), s0 + 1: ValueRanges(lower=4, upper=7, is_bool=False)}

Some things to note:

  • By specifying {0: dimx} for the first input, we see that the resulting shape of the first input is now dynamic, being [s0]. And now by specifying {0: dimy} for the second input, we see that the resulting shape of the second input is also dynamic. However, because we expressed dimy = dimx + 1, instead of arg1_1's shape containing a new symbol, we see that it is now being represented with the same symbol used in arg0_1, s0. We can see that relationship of dimy = dimx + 1 is being shown through s0 + 1.
  • Looking at the range constraints, we see that s0 has the range [3, 6], which is specified initially, and we can see that s0 + 1 has the solved range of [4, 7].

Serialization

To save the ExportedProgram, users can use the :func:`torch.export.save` and :func:`torch.export.load` APIs. A convention is to save the ExportedProgram using a .pt2 file extension.

An example:

import torch
import io

class MyModule(torch.nn.Module):
    def forward(self, x):
        return x + 10

exported_program = torch.export.export(MyModule(), torch.randn(5))

torch.export.save(exported_program, 'exported_program.pt2')
saved_exported_program = torch.export.load('exported_program.pt2')

Specializations

A key concept in understanding the behavior of torch.export is the difference between static and dynamic values.

A dynamic value is one that can change from run to run. These behave like normal arguments to a Python function—you can pass different values for an argument and expect your function to do the right thing. Tensor data is treated as dynamic.

A static value is a value that is fixed at export time and cannot change between executions of the exported program. When the value is encountered during tracing, the exporter will treat it as a constant and hard-code it into the graph.

When an operation is performed (e.g. x + y) and all inputs are static, then the output of the operation will be directly hard-coded into the graph, and the operation won’t show up (i.e. it will get constant-folded).

When a value has been hard-coded into the graph, we say that the graph has been specialized to that value.

The following values are static:

Input Tensor Shapes

By default, torch.export will trace the program specializing on the input tensors' shapes, unless a dimension is specified as dynamic via the dynamic_shapes argument to torch.export. This means that if there exists shape-dependent control flow, torch.export will specialize on the branch that is being taken with the given sample inputs. For example:

import torch
from torch.export import export

class Mod(torch.nn.Module):
    def forward(self, x):
        if x.shape[0] > 5:
            return x + 1
        else:
            return x - 1

example_inputs = (torch.rand(10, 2),)
exported_program = export(Mod(), example_inputs)
print(exported_program)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[10, 2]):
            add: f32[10, 2] = torch.ops.aten.add.Tensor(arg0_1, 1);
            return (add,)

The conditional of (x.shape[0] > 5) does not appear in the ExportedProgram because the example inputs have the static shape of (10, 2). Since torch.export specializes on the inputs' static shapes, the else branch (x - 1) will never be reached. To preserve the dynamic branching behavior based on the shape of a tensor in the traced graph, :func:`torch.export.Dim` will need to be used to specify the dimension of the input tensor (x.shape[0]) to be dynamic, and the source code will need to be :ref:`rewritten <Data/Shape-Dependent Control Flow>`.

Note that tensors that are part of the module state (e.g. parameters and buffers) always have static shapes.

Python Primitives

torch.export also specializes on Python primtivies, such as int, float, bool, and str. However they do have dynamic variants such as SymInt, SymFloat, and SymBool.

For example:

import torch
from torch.export import export

class Mod(torch.nn.Module):
    def forward(self, x: torch.Tensor, const: int, times: int):
        for i in range(times):
            x = x + const
        return x

example_inputs = (torch.rand(2, 2), 1, 3)
exported_program = export(Mod(), example_inputs)
print(exported_program)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[2, 2], arg1_1, arg2_1):
            add: f32[2, 2] = torch.ops.aten.add.Tensor(arg0_1, 1);
            add_1: f32[2, 2] = torch.ops.aten.add.Tensor(add, 1);
            add_2: f32[2, 2] = torch.ops.aten.add.Tensor(add_1, 1);
            return (add_2,)

Because integers are specialized, the torch.ops.aten.add.Tensor operations are all computed with the hard-coded constant 1, rather than arg1_1. If a user passes a different value for arg1_1 at runtime, like 2, than the one used during export time, 1, this will result in an error. Additionally, the times iterator used in the for loop is also "inlined" in the graph through the 3 repeated torch.ops.aten.add.Tensor calls, and the input arg2_1 is never used.

Python Containers

Python containers (List, Dict, NamedTuple, etc.) are considered to have static structure.

Limitations of torch.export

Graph Breaks

As torch.export is a one-shot process for capturing a computation graph from a PyTorch program, it might ultimately run into untraceable parts of programs as it is nearly impossible to support tracing all PyTorch and Python features. In the case of torch.compile, an unsupported operation will cause a "graph break" and the unsupported operation will be run with default Python evaluation. In contrast, torch.export will require users to provide additional information or rewrite parts of their code to make it traceable. As the tracing is based on TorchDynamo, which evaluates at the Python bytecode level, there will be significantly fewer rewrites required compared to previous tracing frameworks.

When a graph break is encountered, :ref:`ExportDB <torch.export_db>` is a great resource for learning about the kinds of programs that are supported and unsupported, along with ways to rewrite programs to make them traceable.

An option to get past dealing with this graph breaks is by using :ref:`non-strict export <Non-Strict Export>`

Data/Shape-Dependent Control Flow

Graph breaks can also be encountered on data-dependent control flow (if x.shape[0] > 2) when shapes are not being specialized, as a tracing compiler cannot possibly deal with without generating code for a combinatorially exploding number of paths. In such cases, users will need to rewrite their code using special control flow operators. Currently, we support :ref:`torch.cond <cond>` to express if-else like control flow (more coming soon!).

Missing Fake/Meta/Abstract Kernels for Operators

When tracing, a FakeTensor kernel (aka meta kernel, abstract impl) is required for all operators. This is used to reason about the input/output shapes for this operator.

Please see :func:`torch.library.register_fake` for more details.

In the unfortunate case where your model uses an ATen operator that is does not have a FakeTensor kernel implementation yet, please file an issue.

Read More

.. toctree::
   :caption: Additional Links for Export Users
   :maxdepth: 1

   export.ir_spec
   torch.compiler_transformations
   torch.compiler_ir
   generated/exportdb/index
   cond


.. toctree::
   :caption: Deep Dive for PyTorch Developers
   :maxdepth: 1

   torch.compiler_dynamo_overview
   torch.compiler_dynamo_deepdive
   torch.compiler_dynamic_shapes
   torch.compiler_fake_tensor

API Reference

.. automodule:: torch.export

.. autofunction:: export

.. autofunction:: save

.. autofunction:: load

.. autofunction:: register_dataclass

.. autofunction:: torch.export.dynamic_shapes.Dim

.. autofunction:: torch.export.exported_program.default_decompositions

.. autofunction:: dims

.. autoclass:: torch.export.dynamic_shapes.ShapesCollection

    .. automethod:: dynamic_shapes


.. autofunction:: torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes

.. autoclass:: Constraint

.. autoclass:: ExportedProgram

    .. automethod:: module
    .. automethod:: buffers
    .. automethod:: named_buffers
    .. automethod:: parameters
    .. automethod:: named_parameters
    .. automethod:: run_decompositions


.. autoclass:: ExportBackwardSignature

.. autoclass:: ExportGraphSignature

.. autoclass:: ModuleCallSignature

.. autoclass:: ModuleCallEntry



.. automodule:: torch.export.decomp_utils

.. autoclass:: CustomDecompTable

    .. automethod:: copy
    .. automethod:: items
    .. automethod:: keys
    .. automethod:: materialize
    .. automethod:: pop
    .. automethod:: update


.. automodule:: torch.export.exported_program

.. automodule:: torch.export.graph_signature

.. autoclass:: InputKind

.. autoclass:: InputSpec

.. autoclass:: OutputKind

.. autoclass:: OutputSpec

.. autoclass:: SymIntArgument

.. autoclass:: SymBoolArgument

.. autoclass:: ExportGraphSignature

    .. automethod:: replace_all_uses
    .. automethod:: get_replace_hook


.. autoclass:: torch.export.graph_signature.CustomObjArgument


.. py:module:: torch.export.dynamic_shapes


.. automodule:: torch.export.unflatten
    :members:


.. automodule:: torch.export.custom_obj


.. automodule:: torch.export.experimental

.. automodule:: torch.export.passes

.. autofunction:: torch.export.passes.move_to_device_pass