| 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) |
| 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, |
| allow_none_pass_through: bool = False) -> 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-value] |
| if allow_none_pass_through: |
| return tuple((torch._remove_batch_dim(out, vmap_level, batch_size, out_dim) if out is not None else None) |
| for out, out_dim in zip(batched_outputs, out_dims_as_tuple)) |
| else: |
| return tuple(torch._remove_batch_dim(out, vmap_level, batch_size, out_dim) |
| 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. |
| 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. |
| |
| .. note:: |
| We have moved development of vmap to |
| `functorch. <https://github.com/pytorch/functorch>`_ functorch's |
| vmap is able to arbitrarily compose with gradient computation |
| and contains significant performance improvements. |
| Please give that a try if that is what you're looking for. |
| |
| Furthermore, 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. |
| |
| .. warning:: |
| torch.vmap is an experimental prototype that is subject to |
| change and/or deletion. Please use at your own risk. |
| |
| 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( |
| 'Please use functorch.vmap instead of torch.vmap ' |
| '(https://github.com/pytorch/functorch). ' |
| 'We\'ve moved development on torch.vmap over to functorch; ' |
| 'functorch\'s vmap has a multitude of significant performance and ' |
| 'functionality improvements.', |
| stacklevel=2) |
| return _vmap(func, in_dims, out_dims) |
| |
| # A version of vmap but without the initial "experimental prototype" warning |
| def _vmap(func: Callable, in_dims: in_dims_t = 0, out_dims: out_dims_t = 0, allow_none_pass_through: bool = False) -> Callable: |
| # The `allow_none_pass_through` argument is a temporary workaround may be removed. |
| # Currently it enables us to wrap the call in `autograd.grad` to the autograd engine, |
| # which may return None if any of the inputs are unused. See the issue discussing this: |
| # https://github.com/facebookresearch/functorch/issues/159. |
| @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) |
| if not allow_none_pass_through: |
| _validate_outputs(batched_outputs, func) |
| return _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func, |
| allow_none_pass_through=allow_none_pass_through) |
| finally: |
| torch._C._vmapmode_decrement_nesting() |
| return wrapped |
