Source code for torch._vmap_internals
import torch
import functools
from torch import Tensor
from typing import Any, Callable, Optional, Tuple, Union, List
from torch.utils._pytree import tree_flatten, tree_unflatten, _broadcast_to_and_flatten
import warnings
in_dims_t = Union[int, Tuple]
out_dims_t = Union[int, Tuple[int, ...]]
# Checks that all args-to-be-batched have the same batch dim size
def _validate_and_get_batch_size(
flat_in_dims: List[Optional[int]],
flat_args: List) -> int:
batch_sizes = [arg.size(in_dim) for in_dim, arg in zip(flat_in_dims, flat_args)
if in_dim is not None]
if batch_sizes and any([size != batch_sizes[0] for size in batch_sizes]):
raise ValueError(
f'vmap: Expected all tensors to have the same size in the mapped '
f'dimension, got sizes {batch_sizes} for the mapped dimension')
return batch_sizes[0]
def _num_outputs(batched_outputs: Union[Tensor, Tuple[Tensor, ...]]) -> int:
if isinstance(batched_outputs, tuple):
return len(batched_outputs)
return 1
# If value is a tuple, check it has length `num_elements`.
# If value is not a tuple, make a tuple with `value` repeated `num_elements` times
def _as_tuple(value: Any, num_elements: int, error_message_lambda: Callable[[], str]) -> Tuple:
if not isinstance(value, tuple):
return (value,) * num_elements
if len(value) != num_elements:
raise ValueError(error_message_lambda())
return value
# Creates BatchedTensors for every Tensor in arg that should be batched.
# Returns the (potentially) batched arguments and the batch_size.
def _create_batched_inputs(
in_dims: in_dims_t, args: Tuple, vmap_level: int, func: Callable) -> Tuple[Tuple, int]:
if not isinstance(in_dims, int) and not isinstance(in_dims, tuple):
raise ValueError(
f'vmap({_get_name(func)}, in_dims={in_dims}, ...)(<inputs>): '
f'expected `in_dims` to be int or a (potentially nested) tuple '
f'matching the structure of inputs, got: {type(in_dims)}.')
if len(args) == 0:
raise ValueError(
f'vmap({_get_name(func)})(<inputs>): got no inputs. Maybe you forgot to add '
f'inputs, or you are trying to vmap over a function with no inputs. '
f'The latter is unsupported.')
flat_args, args_spec = tree_flatten(args)
flat_in_dims = _broadcast_to_and_flatten(in_dims, args_spec)
if flat_in_dims is None:
raise ValueError(
f'vmap({_get_name(func)}, in_dims={in_dims}, ...)(<inputs>): '
f'in_dims is not compatible with the structure of `inputs`. '
f'in_dims has structure {tree_flatten(in_dims)[1]} but inputs '
f'has structure {args_spec}.')
for arg, in_dim in zip(flat_args, flat_in_dims):
if not isinstance(in_dim, int) and in_dim is not None:
raise ValueError(
f'vmap({_get_name(func)}, in_dims={in_dims}, ...)(<inputs>): '
f'Got in_dim={in_dim} for an input but in_dim must be either '
f'an integer dimension or None.')
if isinstance(in_dim, int) and not isinstance(arg, Tensor):
raise ValueError(
f'vmap({_get_name(func)}, in_dims={in_dims}, ...)(<inputs>): '
f'Got in_dim={in_dim} for an input but the input is of type '
f'{type(arg)}. We cannot vmap over non-Tensor arguments, '
f'please use None as the respective in_dim')
if in_dim is not None and (in_dim < 0 or in_dim >= arg.dim()):
raise ValueError(
f'vmap({_get_name(func)}, in_dims={in_dims}, ...)(<inputs>): '
f'Got in_dim={in_dim} for some input, but that input is a Tensor '
f'of dimensionality {arg.dim()} so expected in_dim to satisfy '
f'0 <= in_dim < {arg.dim()}.')
batch_size = _validate_and_get_batch_size(flat_in_dims, flat_args)
# See NOTE [Ignored _remove_batch_dim, _add_batch_dim]
batched_inputs = [arg if in_dim is None else
torch._add_batch_dim(arg, in_dim, vmap_level) # type: ignore
for in_dim, arg in zip(flat_in_dims, flat_args)]
return tree_unflatten(batched_inputs, args_spec), batch_size
# Undos the batching (and any batch dimensions) associated with the `vmap_level`.
def _unwrap_batched(
batched_outputs: Union[Tensor, Tuple[Tensor, ...]],
out_dims: out_dims_t,
vmap_level: int, batch_size: int, func: Callable) -> Tuple:
num_outputs = _num_outputs(batched_outputs)
out_dims_as_tuple = _as_tuple(
out_dims, num_outputs,
lambda: f'vmap({_get_name(func)}, ..., out_dims={out_dims}): `out_dims` must '
f'have one dim per output (got {num_outputs} outputs) of {_get_name(func)}.')
# NOTE [Ignored _remove_batch_dim, _add_batch_dim]
# There is something wrong with our type bindings for functions that begin
# with '_', see #40397.
if isinstance(batched_outputs, Tensor):
out_dim = out_dims_as_tuple[0]
return torch._remove_batch_dim(batched_outputs, vmap_level, batch_size, out_dim) # type: ignore
return tuple(torch._remove_batch_dim(out, vmap_level, batch_size, out_dim) # type: ignore
for out, out_dim in zip(batched_outputs, out_dims_as_tuple))
# Checks that `fn` returned one or more Tensors and nothing else.
# NB: A python function that return multiple arguments returns a single tuple,
# so we are effectively checking that `outputs` is a single Tensor or a tuple of
# Tensors.
def _validate_outputs(outputs: Any, func: Callable) -> None:
if isinstance(outputs, Tensor):
return
if not isinstance(outputs, tuple):
raise ValueError(f'vmap({_get_name(func)}, ...): `{_get_name(func)}` must only return '
f'Tensors, got type {type(outputs)} as the return.')
for idx, output in enumerate(outputs):
if isinstance(output, Tensor):
continue
raise ValueError(f'vmap({_get_name(func)}, ...): `{_get_name(func)}` must only return '
f'Tensors, got type {type(output)} for return {idx}.')
def _check_out_dims_is_int_or_int_tuple(out_dims: out_dims_t, func: Callable) -> None:
if isinstance(out_dims, int):
return
if not isinstance(out_dims, tuple) or \
not all([isinstance(out_dim, int) for out_dim in out_dims]):
raise ValueError(
f'vmap({_get_name(func)}, ..., out_dims={out_dims}): `out_dims` must be '
f'an int or a tuple of int representing where in the outputs the '
f'vmapped dimension should appear.')
def _get_name(func: Callable):
if hasattr(func, '__name__'):
return func.__name__
# Not all callables have __name__, in fact, only static functions/methods do.
# A callable created via functools.partial or an nn.Module, to name some
# examples, don't have a __name__.
return repr(func)
# vmap(func)(inputs) wraps all Tensor inputs to be batched in BatchedTensors,
# sends those into func, and then unwraps the output BatchedTensors. Operations
# on BatchedTensors perform the batched operations that the user is asking for.
[docs]def vmap(func: Callable, in_dims: in_dims_t = 0, out_dims: out_dims_t = 0) -> Callable:
"""
vmap is the vectorizing map. Returns a new function that maps `func` over some
dimension of the inputs. Semantically, vmap pushes the map into PyTorch
operations called by `func`, effectively vectorizing those operations.
vmap is useful for handling batch dimensions: one can write a function `func`
that runs on examples and then lift it to a function that can take batches of
examples with `vmap(func)`. vmap can also be used to compute batched
gradients when composed with autograd.
.. warning::
torch.vmap is an experimental prototype that is subject to
change and/or deletion. Please use at your own risk.
.. note::
If you're interested in using vmap for your use case, please
`contact us! <https://github.com/pytorch/pytorch/issues/42368>`_
We're interested in gathering feedback from early adopters to inform
the design.
Args:
func (function): A Python function that takes one or more arguments.
Must return one or more Tensors.
in_dims (int or nested structure): Specifies which dimension of the
inputs should be mapped over. `in_dims` should have a structure
like the inputs. If the `in_dim` for a particular input is None,
then that indicates there is no map dimension. Default: 0.
out_dims (int or Tuple[int]): Specifies where the mapped dimension
should appear in the outputs. If `out_dims` is a Tuple, then it should
have one element per output. Default: 0.
Returns:
Returns a new "batched" function. It takes the same inputs as `func`,
except each input has an extra dimension at the index specified by `in_dims`.
It takes returns the same outputs as `func`, except each output has
an extra dimension at the index specified by `out_dims`.
.. warning:
vmap works best with functional-style code. Please do not perform any
side-effects in `func`, with the exception of in-place PyTorch operations.
Examples of side-effects include mutating Python data structures and
assigning values to variables not captured in `func`.
One example of using `vmap` is to compute batched dot products. PyTorch
doesn't provide a batched `torch.dot` API; instead of unsuccessfully
rummaging through docs, use `vmap` to construct a new function.
>>> torch.dot # [D], [D] -> []
>>> batched_dot = torch.vmap(torch.dot) # [N, D], [N, D] -> [N]
>>> x, y = torch.randn(2, 5), torch.randn(2, 5)
>>> batched_dot(x, y)
`vmap` can be helpful in hiding batch dimensions, leading to a simpler
model authoring experience.
>>> batch_size, feature_size = 3, 5
>>> weights = torch.randn(feature_size, requires_grad=True)
>>>
>>> def model(feature_vec):
>>> # Very simple linear model with activation
>>> return feature_vec.dot(weights).relu()
>>>
>>> examples = torch.randn(batch_size, feature_size)
>>> result = torch.vmap(model)(examples)
`vmap` can also help vectorize computations that were previously difficult
or impossible to batch. One example is higher-order gradient computation.
The PyTorch autograd engine computes vjps (vector-Jacobian products).
Computing a full Jacobian matrix for some function f: R^N -> R^N usually
requires N calls to `autograd.grad`, one per Jacobian row. Using `vmap`,
we can vectorize the whole computation, computing the Jacobian in a single
call to `autograd.grad`.
>>> # Setup
>>> N = 5
>>> f = lambda x: x ** 2
>>> x = torch.randn(N, requires_grad=True)
>>> y = f(x)
>>> I_N = torch.eye(N)
>>>
>>> # Sequential approach
>>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
>>> for v in I_N.unbind()]
>>> jacobian = torch.stack(jacobian_rows)
>>>
>>> # vectorized gradient computation
>>> def get_vjp(v):
>>> return torch.autograd.grad(y, x, v)
>>> jacobian = torch.vmap(get_vjp)(I_N)
.. note::
vmap does not provide general autobatching or handle variable-length
sequences out of the box.
"""
warnings.warn(
'torch.vmap is an experimental prototype that is subject to '
'change and/or deletion. Please use at your own risk. There may be '
'unexpected performance cliffs due to certain operators not being '
'implemented. To see detailed performance warnings please use '
'`torch._C._debug_only_display_vmap_fallback_warnings(True) '
'before the call to `vmap`.',
stacklevel=2)
@functools.wraps(func)
def wrapped(*args):
_check_out_dims_is_int_or_int_tuple(out_dims, func)
vmap_level = torch._C._vmapmode_increment_nesting()
try:
batched_inputs, batch_size = _create_batched_inputs(in_dims, args, vmap_level, func)
batched_outputs = func(*batched_inputs)
_validate_outputs(batched_outputs, func)
return _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func)
finally:
torch._C._vmapmode_decrement_nesting()
return wrapped
