torch.library

torch.library is a collection of APIs for extending PyTorch’s core library of operators. It contains utilities for testing custom operators, creating new custom operators, and extending operators defined with PyTorch’s C++ operator registration APIs (e.g. aten operators).

For a detailed guide on effectively using these APIs, please see this gdoc

Testing custom ops

Use torch.library.opcheck() to test custom ops for incorrect usage of the Python torch.library and/or C++ TORCH_LIBRARY APIs. Also, if your operator supports training, use torch.autograd.gradcheck() to test that the gradients are mathematically correct.

torch.library.opcheck(op, args, kwargs=None, *, test_utils=('test_schema', 'test_autograd_registration', 'test_faketensor', 'test_aot_dispatch_dynamic'), raise_exception=True)

Given an operator and some sample arguments, tests if the operator is registered correctly.

That is, when you use the torch.library/TORCH_LIBRARY APIs to create a custom op, you specified metadata (e.g. mutability info) about the custom op and these APIs require that the functions you pass them satisfy certain properties (e.g. no data pointer access in the fake/meta/abstract kernel) opcheck tests these metadata and properties.

Concretely, we test the following: - test_schema: if the operator’s schema is correct. - test_autograd_registration: if autograd was registered correctly. - test_faketensor: If the operator has a FakeTensor kernel (and if it is correct). The FakeTensor kernel is necessary ( but not sufficient) for the operator to work with PyTorch compilation APIs (torch.compile/export/FX). - test_aot_dispatch_dynamic: If the operator has correct behavior with PyTorch compilation APIs (torch.compile/export/FX). This checks that the outputs (and gradients, if applicable) are the same under eager-mode PyTorch and torch.compile. This test is a superset of test_faketensor.

For best results, please call opcheck multiple times with a representative set of inputs. If your operator supports autograd, please use opcheck with inputs with requires_grad = True; if your operator supports multiple devices (e.g. CPU and CUDA), please use opcheck with inputs on all supported devices.

Parameters

• op (Union[OpOverload, OpOverloadPacket, CustomOpDef]) – The operator. Must either be a function decorated with torch.library.custom_op() or an OpOverload/OpOverloadPacket found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)

• args (Tuple[Any, ...]) – The args to the operator

• kwargs (Optional[Dict[str, Any]]) – The kwargs to the operator

• test_utils (Union[str, Sequence[str]]) – Tests that we should run. Default: all of them. Example: (“test_schema”, “test_faketensor”)

• raise_exception (bool) – If we should raise an exception on the first error. If False, we will return a dict with information on if each test passed or not.

Return type

Dict[str, str]

Warning

opcheck and torch.autograd.gradcheck() test different things; opcheck tests if your usage of torch.library APIs is correct while torch.autograd.gradcheck() tests if your autograd formula is mathematically correct. Use both to test custom ops that support gradient computation.

Example

>>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
>>> def numpy_add(x: Tensor, y: float) -> Tensor:
>>>     x_np = x.numpy(force=True)
>>>     z_np = x_np + y
>>>     return torch.from_numpy(z_np).to(x.device)
>>>
>>> @numpy_sin.register_fake
>>> def _(x, y):
>>>     return torch.empty_like(x)
>>>
>>> def setup_context(ctx, inputs, output):
>>>     y, = inputs
>>>     ctx.y = y
>>>
>>> def backward(ctx, grad):
>>>     return grad * ctx.y, None
>>>
>>> numpy_sin.register_autograd(backward, setup_context=setup_context)
>>>
>>> sample_inputs = [
>>>     (torch.randn(3), 3.14),
>>>     (torch.randn(2, 3, device='cuda'), 2.718),
>>>     (torch.randn(1, 10, requires_grad=True), 1.234),
>>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
>>> ]
>>>
>>> for args in sample_inputs:
>>>     torch.library.opcheck(foo, args)

Creating new custom ops in Python

Use torch.library.custom_op() to create new custom ops.

torch.library.custom_op(name, /, *, mutates_args, device_types=None, schema=None)

Wraps a function into custom operator.

Reasons why you may want to create a custom op include: - Wrapping a third-party library or custom kernel to work with PyTorch subsystems like Autograd. - Preventing torch.compile/export/FX tracing from peeking inside your function.

This API is used as a decorator around a function (please see examples). The provided function must have type hints; these are needed to interface with PyTorch’s various subsystems.

Parameters

• name (str) – A name for the custom op that looks like “{namespace}::{name}”, e.g. “mylib::my_linear”. The name is used as the op’s stable identifier in PyTorch subsystems (e.g. torch.export, FX graphs). To avoid name collisions, please use your project name as the namespace; e.g. all custom ops in pytorch/fbgemm use “fbgemm” as the namespace.

• mutates_args (Iterable[str]) – The names of args that the function mutates. This MUST be accurate, otherwise, the behavior is undefined.

• device_types (None | str | Sequence[str]) – The device type(s) the function is valid for. If no device type is provided, then the function is used as the default implementation for all device types. Examples: “cpu”, “cuda”.

• schema (None | str) – A schema string for the operator. If None (recommended) we’ll infer a schema for the operator from its type annotations. We recommend letting us infer a schema unless you have a specific reason not to. Example: “(Tensor x, int y) -> (Tensor, Tensor)”.

Return type

Callable

Note

We recommend not passing in a schema arg and instead letting us infer it from the type annotations. It is error-prone to write your own schema. You may wish to provide your own schema if our interpretation of the type annotation is not what you want. For more info on how to write a schema string, see here

Examples::

>>> import torch
>>> from torch import Tensor
>>> from torch.library import custom_op
>>> import numpy as np
>>>
>>> @custom_op("mylib::numpy_sin", mutates_args=())
>>> def numpy_sin(x: Tensor) -> Tensor:
>>>     x_np = x.cpu().numpy()
>>>     y_np = np.sin(x_np)
>>>     return torch.from_numpy(y_np).to(device=x.device)
>>>
>>> x = torch.randn(3)
>>> y = numpy_sin(x)
>>> assert torch.allclose(y, x.sin())
>>>
>>> # Example of a custom op that only works for one device type.
>>> @custom_op("mylib::numpy_sin_cpu", mutates_args=(), device_types="cpu")
>>> def numpy_sin_cpu(x: Tensor) -> Tensor:
>>>     x_np = x.numpy()
>>>     y_np = np.sin(x_np)
>>>     return torch.from_numpy(y_np)
>>>
>>> x = torch.randn(3)
>>> y = numpy_sin_cpu(x)
>>> assert torch.allclose(y, x.sin())
>>>
>>> # Example of a custom op that mutates an input
>>> @custom_op("mylib::numpy_sin_inplace", mutates_args={"x"}, device_types="cpu")
>>> def numpy_sin_inplace(x: Tensor) -> None:
>>>     x_np = x.numpy()
>>>     np.sin(x_np, out=x_np)
>>>
>>> x = torch.randn(3)
>>> expected = x.sin()
>>> numpy_sin_inplace(x)
>>> assert torch.allclose(x, expected)

Extending custom ops (created from Python or C++)

Use the register.* methods, such as torch.library.register_kernel() and func:torch.library.register_fake, to add implementations for any operators (they may have been created using torch.library.custom_op() or via PyTorch’s C++ operator registration APIs).

torch.library.register_kernel(op, device_types, func=None, /, *, lib=None)

Register an implementation for a device type for this operator.

Some valid device_types are: “cpu”, “cuda”, “xla”, “mps”, “ipu”, “xpu”. This API may be used as a decorator.

Parameters

• fn (Callable) – The function to register as the implementation for the given device types.

• device_types (None | str | Sequence[str]) – The device_types to register an impl to. If None, we will register to all device types – please only use this option if your implementation is truly device-type-agnostic.

Examples::

>>> import torch
>>> from torch import Tensor
>>> from torch.library import custom_op
>>> import numpy as np
>>>
>>> # Create a custom op that works on cpu
>>> @custom_op("mylib::numpy_sin", mutates_args=(), device_types="cpu")
>>> def numpy_sin(x: Tensor) -> Tensor:
>>>     x_np = x.numpy()
>>>     y_np = np.sin(x_np)
>>>     return torch.from_numpy(y_np)
>>>
>>> # Add implementations for the cuda device
>>> @torch.library.register_kernel("mylib::numpy_sin", "cuda")
>>> def _(x):
>>>     x_np = x.cpu().numpy()
>>>     y_np = np.sin(x_np)
>>>     return torch.from_numpy(y_np).to(device=x.device)
>>>
>>> x_cpu = torch.randn(3)
>>> x_cuda = x_cpu.cuda()
>>> assert torch.allclose(numpy_sin(x_cpu), x_cpu.sin())
>>> assert torch.allclose(numpy_sin(x_cuda), x_cuda.sin())

torch.library.register_autograd(op, backward, /, *, setup_context=None, lib=None)

Register a backward formula for this custom op.

In order for an operator to work with autograd, you need to register a backward formula: 1. You must tell us how to compute gradients during the backward pass by providing us a “backward” function. 2. If you need any values from the forward to compute gradients, you can use setup_context to save values for backward.

backward runs during the backward pass. It accepts (ctx, *grads): - grads is one or more gradients. The number of gradients matches the number of outputs of the operator. The ctx object is the same ctx object used by torch.autograd.Function. The semantics of backward_fn are the same as torch.autograd.Function.backward().

setup_context(ctx, inputs, output) runs during the forward pass. Please save quantities needed for backward onto the ctx object via either torch.autograd.function.FunctionCtx.save_for_backward() or assigning them as attributes of ctx. If your custom op has kwarg-only arguments, we expect the signature of setup_context to be setup_context(ctx, inputs, keyword_only_inputs, output).

Both setup_context_fn and backward_fn must be traceable. That is, they may not directly access torch.Tensor.data_ptr() and they must not depend on or mutate global state. If you need a non-traceable backward, you can make it a separate custom_op that you call inside backward_fn.

Examples

>>> import torch
>>> import numpy as np
>>> from torch import Tensor
>>>
>>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
>>> def numpy_sin(x: Tensor) -> Tensor:
>>>     x_np = x.cpu().numpy()
>>>     y_np = np.sin(x_np)
>>>     return torch.from_numpy(y_np).to(device=x.device)
>>>
>>> def setup_context(ctx, inputs, output) -> Tensor:
>>>     x, = inputs
>>>     ctx.save_for_backward(x)
>>>
>>> def backward(ctx, grad):
>>>     x, = ctx.saved_tensors
>>>     return grad * x.cos()
>>>
>>> torch.library.register_autograd("mylib::numpy_sin", backward, setup_context=setup_context)
>>>
>>> x = torch.randn(3, requires_grad=True)
>>> y = numpy_sin(x)
>>> grad_x, = torch.autograd.grad(y, x, torch.ones_like(y))
>>> assert torch.allclose(grad_x, x.cos())
>>>
>>> # Example with a keyword-only arg
>>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
>>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
>>>     x_np = x.cpu().numpy()
>>>     y_np = x_np * val
>>>     return torch.from_numpy(y_np).to(device=x.device)
>>>
>>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
>>>     ctx.val = keyword_only_inputs["val"]
>>>
>>> def backward(ctx, grad):
>>>     return grad * ctx.val
>>>
>>> torch.library.register_autograd("mylib::numpy_mul", backward, setup_context=setup_context)
>>>
>>> x = torch.randn(3, requires_grad=True)
>>> y = numpy_mul(x, val=3.14)
>>> grad_x, = torch.autograd.grad(y, x, torch.ones_like(y))
>>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))

torch.library.register_fake(op, func=None, /, *, lib=None, _stacklevel=1)

Register a FakeTensor implementation (“fake impl”) for this operator.

Also sometimes known as a “meta kernel”, “abstract impl”.

An “FakeTensor implementation” specifies the behavior of this operator on Tensors that carry no data (“FakeTensor”). Given some input Tensors with certain properties (sizes/strides/storage_offset/device), it specifies what the properties of the output Tensors are.

The FakeTensor implementation has the same signature as the operator. It is run for both FakeTensors and meta tensors. To write a FakeTensor implementation, assume that all Tensor inputs to the operator are regular CPU/CUDA/Meta tensors, but they do not have storage, and you are trying to return regular CPU/CUDA/Meta tensor(s) as output. The FakeTensor implementation must consist of only PyTorch operations (and may not directly access the storage or data of any input or intermediate Tensors).

This API may be used as a decorator (see examples).

For a detailed guide on custom ops, please see https://docs.google.com/document/d/1W–T6wz8IY8fOI0Vm8BF44PdBgs283QvpelJZWieQWQ/edit

Examples

>>> import torch
>>> import numpy as np
>>> from torch import Tensor
>>>
>>> # Example 1: an operator without data-dependent output shape
>>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
>>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
>>>     raise NotImplementedError("Implementation goes here")
>>>
>>> @torch.library.register_fake("mylib::custom_linear")
>>> def _(x, weight, bias):
>>>     assert x.dim() == 2
>>>     assert weight.dim() == 2
>>>     assert bias.dim() == 1
>>>     assert x.shape[1] == weight.shape[1]
>>>     assert weight.shape[0] == bias.shape[0]
>>>     assert x.device == weight.device
>>>
>>>     return (x @ weight.t()) + bias
>>>
>>> with torch._subclasses.fake_tensor.FakeTensorMode():
>>>     x = torch.randn(2, 3)
>>>     w = torch.randn(3, 3)
>>>     b = torch.randn(3)
>>>     y = torch.ops.mylib.custom_linear(x, w, b)
>>>
>>> assert y.shape == (2, 3)
>>>
>>> # Example 2: an operator with data-dependent output shape
>>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
>>> def custom_nonzero(x: Tensor) -> Tensor:
>>>     x_np = x.numpy(force=True)
>>>     res = np.stack(np.nonzero(x_np), axis=1)
>>>     return torch.tensor(res, device=x.device)
>>>
>>> @torch.library.register_fake("mylib::custom_nonzero")
>>> def _(x):
>>>     # Number of nonzero-elements is data-dependent.
>>>     # Since we cannot peek at the data in an fake impl,
>>>     # we use the ctx object to construct a new symint that
>>>     # represents the data-dependent size.
>>>     ctx = torch.library.get_ctx()
>>>     nnz = ctx.new_dynamic_size()
>>>     shape = [nnz, x.dim()]
>>>     result = x.new_empty(shape, dtype=torch.int64)
>>>     return result
>>>
>>> from torch.fx.experimental.proxy_tensor import make_fx
>>>
>>> x = torch.tensor([0, 1, 2, 3, 4, 0])
>>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
>>> trace.print_readable()
>>>
>>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))

torch.library.impl_abstract(qualname, func=None, *, lib=None, _stacklevel=1)

This API was renamed to torch.library.register_fake() in PyTorch 2.4. Please use that instead.

torch.library.get_ctx()

get_ctx() returns the current AbstractImplCtx object.

Calling get_ctx() is only valid inside of an fake impl (see torch.library.register_fake() for more usage details.

Return type

AbstractImplCtx

Low-level APIs

The following APIs are direct bindings to PyTorch’s C++ low-level operator registration APIs.

Warning

The low-level operator registration APIs and the PyTorch Dispatcher are a complicated PyTorch concept. We recommend you use the higher level APIs above (that do not require a torch.library.Library object) when possible. This blog post <http://blog.ezyang.com/2020/09/lets-talk-about-the-pytorch-dispatcher/>`_ is a good starting point to learn about the PyTorch Dispatcher.

A tutorial that walks you through some examples on how to use this API is available on Google Colab.

class torch.library.Library(ns, kind, dispatch_key='')

A class to create libraries that can be used to register new operators or override operators in existing libraries from Python. A user can optionally pass in a dispatch keyname if they only want to register kernels corresponding to only one specific dispatch key.

To create a library to override operators in an existing library (with name ns), set the kind to “IMPL”. To create a new library (with name ns) to register new operators, set the kind to “DEF”. To create a fragment of a possibly existing library to register operators (and bypass the limitation that there is only one library for a given namespace), set the kind to “FRAGMENT”.

Parameters

• ns – library name

• kind – “DEF”, “IMPL” (default: “IMPL”), “FRAGMENT”

• dispatch_key – PyTorch dispatch key (default: “”)

define(schema, alias_analysis='', *, tags=())

Defines a new operator and its semantics in the ns namespace.

Parameters

• schema – function schema to define a new operator.

• alias_analysis (optional) – Indicates if the aliasing properties of the operator arguments can be inferred from the schema (default behavior) or not (“CONSERVATIVE”).

• tags (Tag | Sequence[Tag]) – one or more torch.Tag to apply to this operator. Tagging an operator changes the operator’s behavior under various PyTorch subsystems; please read the docs for the torch.Tag carefully before applying it.

Returns

name of the operator as inferred from the schema.

Example::

>>> my_lib = Library("mylib", "DEF")
>>> my_lib.define("sum(Tensor self) -> Tensor")

impl(op_name, fn, dispatch_key='', *, with_keyset=False)

Registers the function implementation for an operator defined in the library.

Parameters

• op_name – operator name (along with the overload) or OpOverload object.

• fn – function that’s the operator implementation for the input dispatch key or fallthrough_kernel() to register a fallthrough.

• dispatch_key – dispatch key that the input function should be registered for. By default, it uses the dispatch key that the library was created with.

Example::

>>> my_lib = Library("aten", "IMPL")
>>> def div_cpu(self, other):
>>>     return self * (1 / other)
>>> my_lib.impl("div.Tensor", div_cpu, "CPU")

torch.library.fallthrough_kernel()

A dummy function to pass to Library.impl in order to register a fallthrough.

torch.library.define(qualname, schema, *, lib=None, tags=())

torch.library.define(lib, schema, alias_analysis='')

Defines a new operator.

In PyTorch, defining an op (short for “operator”) is a two step-process: - we need to define the op (by providing an operator name and schema) - we need to implement behavior for how the operator interacts with various PyTorch subsystems, like CPU/CUDA Tensors, Autograd, etc.

This entrypoint defines the custom operator (the first step) you must then perform the second step by calling various impl_* APIs, like torch.library.impl() or torch.library.register_fake().

Parameters

• qualname (str) – The qualified name for the operator. Should be a string that looks like “namespace::name”, e.g. “aten::sin”. Operators in PyTorch need a namespace to avoid name collisions; a given operator may only be created once. If you are writing a Python library, we recommend the namespace to be the name of your top-level module.

• schema (str) – The schema of the operator. E.g. “(Tensor x) -> Tensor” for an op that accepts one Tensor and returns one Tensor. It does not contain the operator name (that is passed in qualname).

• lib (Optional[Library]) – If provided, the lifetime of this operator will be tied to the lifetime of the Library object.

• tags (Tag | Sequence[Tag]) – one or more torch.Tag to apply to this operator. Tagging an operator changes the operator’s behavior under various PyTorch subsystems; please read the docs for the torch.Tag carefully before applying it.

Example::

>>> import torch
>>> import numpy as np
>>>
>>> # Define the operator
>>> torch.library.define("mylib::sin", "(Tensor x) -> Tensor")
>>>
>>> # Add implementations for the operator
>>> @torch.library.impl("mylib::sin", "cpu")
>>> def f(x):
>>>     return torch.from_numpy(np.sin(x.numpy()))
>>>
>>> # Call the new operator from torch.ops.
>>> x = torch.randn(3)
>>> y = torch.ops.mylib.sin(x)
>>> assert torch.allclose(y, x.sin())

torch.library.impl(qualname, types, func=None, *, lib=None)

torch.library.impl(lib, name, dispatch_key='')

Register an implementation for a device type for this operator.

You may pass “default” for types to register this implementation as the default implementation for ALL device types. Please only use this if the implementation truly supports all device types; for example, this is true if it is a composition of built-in PyTorch operators.

Some valid types are: “cpu”, “cuda”, “xla”, “mps”, “ipu”, “xpu”.

Parameters

• qualname (str) – Should be a string that looks like “namespace::operator_name”.

• types (str | Sequence[str]) – The device types to register an impl to.

• lib (Optional[Library]) – If provided, the lifetime of this registration will be tied to the lifetime of the Library object.

Examples

>>> import torch
>>> import numpy as np
>>>
>>> # Define the operator
>>> torch.library.define("mylib::mysin", "(Tensor x) -> Tensor")
>>>
>>> # Add implementations for the cpu device
>>> @torch.library.impl("mylib::mysin", "cpu")
>>> def f(x):
>>>     return torch.from_numpy(np.sin(x.numpy()))
>>>
>>> x = torch.randn(3)
>>> y = torch.ops.mylib.mysin(x)
>>> assert torch.allclose(y, x.sin())