MLPY/Lib/site-packages/torch/_functorch/apis.py

# NOTE: We allow Dynamo to see this file (via torch/_dynamo/trace_rules.py) so that it can
#       trace through functorch transforms.
#       Currently, we can't allow Dynamo to see `eager_transforms.py`/`vmap.py` as that break a lot of thing
#       and there isn't a mechanism to selectively expose only some functions (eg. grad) from a file
#       to Dynamo.
from torch._functorch.vmap import (vmap_impl, _check_randomness_arg,
                                   Callable, in_dims_t, out_dims_t, _check_out_dims_is_int_or_int_pytree,
                                   _process_batched_inputs, _chunked_vmap)
from torch._functorch.utils import exposed_in, argnums_t
import functools

# 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.
#
# vmap's randomness behavior differs from JAX's, which would require a PRNG key
# to be passed everywhere.


@exposed_in('torch.func')
def vmap(
        func: Callable,
        in_dims: in_dims_t = 0,
        out_dims: out_dims_t = 0,
        randomness: str = 'error',
        *,
        chunk_size=None) -> Callable:
    """
    vmap is the vectorizing map; ``vmap(func)`` 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::
        :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
        convenience. Use whichever one you'd like.

    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.
        randomness (str): Specifies whether the randomness in this
            vmap should be the same or different across batches. If 'different',
            the randomness for each batch will be different. If 'same', the
            randomness will be the same across batches. If 'error', any calls to
            random functions will error. Default: 'error'. WARNING: this flag
            only applies to random PyTorch operations and does not apply to
            Python's random module or numpy randomness.
        chunk_size (None or int): If None (default), apply a single vmap over inputs.
            If not None, then compute the vmap :attr:`chunk_size` samples at a time.
            Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
            If you run into memory issues computing the vmap, please try a non-None chunk_size.

    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:
        :func:`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 :func:`vmap` is to compute batched dot products. PyTorch
    doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
    rummaging through docs, use :func:`vmap` to construct a new function.

        >>> torch.dot                            # [D], [D] -> []
        >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
        >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
        >>> batched_dot(x, y)

    :func:`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)

    :func:`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 :func:`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)

    :func:`vmap` can also be nested, producing an output with multiple batched dimensions

        >>> torch.dot                            # [D], [D] -> []
        >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
        >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
        >>> batched_dot(x, y) # tensor of size [2, 3]

    If the inputs are not batched along the first dimension, ``in_dims`` specifies
    the dimension that each inputs are batched along as

        >>> torch.dot                            # [N], [N] -> []
        >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
        >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
        >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension

    If there are multiple inputs each of which is batched along different dimensions,
    ``in_dims`` must be a tuple with the batch dimension for each input as

        >>> torch.dot                            # [D], [D] -> []
        >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
        >>> x, y = torch.randn(2, 5), torch.randn(5)
        >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None

    If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
    matching the shape of the input:

        >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
        >>> x, y = torch.randn(2, 5), torch.randn(5)
        >>> input = {'x': x, 'y': y}
        >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
        >>> batched_dot(input)

    By default, the output is batched along the first dimension. However, it can be batched
    along any dimension by using ``out_dims``

        >>> f = lambda x: x ** 2
        >>> x = torch.randn(2, 5)
        >>> batched_pow = torch.vmap(f, out_dims=1)
        >>> batched_pow(x) # [5, 2]

    For any function that uses kwargs, the returned function will not batch the kwargs but will
    accept kwargs

        >>> x = torch.randn([2, 5])
        >>> def fn(x, scale=4.):
        >>>   return x * scale
        >>>
        >>> batched_pow = torch.vmap(fn)
        >>> assert torch.allclose(batched_pow(x), x * 4)
        >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]

    .. note::
        vmap does not provide general autobatching or handle variable-length
        sequences out of the box.
    """
    _check_randomness_arg(randomness)
    if not (chunk_size is None or chunk_size > 0):
        raise ValueError(f"vmap: chunk_size should be None or greater than 0. (got {chunk_size})")

    # @functools.wraps(func)
    def wrapped(*args, **kwargs):
        return vmap_impl(func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs)

    return wrapped


def chunk_vmap(
        func: Callable,
        in_dims: in_dims_t = 0,
        out_dims: out_dims_t = 0,
        randomness: str = 'error',
        chunks=2) -> Callable:
    """
    chunk_vmap is the vectorizing map (vmap) using chunks of input data. It is a mix of vmap (which vectorizes
    everything) and map (which executes things sequentially). ``chunk_vmap`` vectorizes the input with number of
    chunks at a time. For more details about vectorizing map, see :func:`vmap`.

    .. note::
        Please use :func:`vmap` with ``chunk_size`` argument instead of this API.

    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.
        randomness (str): Specifies whether the randomness in this
            vmap should be the same or different across batches. If 'different',
            the randomness for each batch will be different. If 'same', the
            randomness will be the same across batches. If 'error', any calls to
            random functions will error. Default: 'error'. WARNING: this flag
            only applies to random PyTorch operations and does not apply to
            Python's random module or numpy randomness.
        chunks (int): Number of chunks to use to split the input data. Default is 2.
            If equals to 1 then :func:`vmap` is called.

    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``.
    """
    _check_randomness_arg(randomness)

    if chunks == 1:
        return vmap(func, in_dims=in_dims, out_dims=out_dims, randomness=randomness)

    def _get_chunk_flat_args(flat_args_, flat_in_dims_, chunks_):
        flat_args_chunks = tuple(
            t.chunk(chunks_, dim=in_dim) if in_dim is not None else [t, ] * chunks_
            for t, in_dim in zip(flat_args_, flat_in_dims_)
        )
        # transpose chunk dim and flatten structure
        # chunks_flat_args is a list of flatten args
        chunks_flat_args = zip(*flat_args_chunks)
        return chunks_flat_args

    @functools.wraps(func)
    def wrapped_with_chunks(*args, **kwargs):
        _check_out_dims_is_int_or_int_pytree(out_dims, func)
        _, flat_in_dims, flat_args, args_spec = _process_batched_inputs(in_dims, args, func)
        # Chunk flat arguments
        chunks_flat_args = _get_chunk_flat_args(flat_args, flat_in_dims, chunks)

        # Apply vmap on chunks
        return _chunked_vmap(func, flat_in_dims, chunks_flat_args, args_spec, out_dims, randomness, **kwargs)

    return wrapped_with_chunks


@exposed_in("torch.func")
def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable:
    """``grad`` operator helps computing gradients of ``func`` with respect to the
    input(s) specified by ``argnums``. This operator can be nested to
    compute higher-order gradients.

    Args:
        func (Callable): A Python function that takes one or more arguments.
            Must return a single-element Tensor. If specified ``has_aux`` equals ``True``,
            function can return a tuple of single-element Tensor and other auxiliary objects:
            ``(output, aux)``.
        argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to.
            ``argnums`` can be single integer or tuple of integers. Default: 0.
        has_aux (bool): Flag indicating that ``func`` returns a tensor and other
            auxiliary objects: ``(output, aux)``. Default: False.

    Returns:
        Function to compute gradients with respect to its inputs. By default, the output of
        the function is the gradient tensor(s) with respect to the first argument.
        If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects
        is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with
        respect to each ``argnums`` value is returned.

    Example of using ``grad``:

        >>> # xdoctest: +SKIP
        >>> from torch.func import grad
        >>> x = torch.randn([])
        >>> cos_x = grad(lambda x: torch.sin(x))(x)
        >>> assert torch.allclose(cos_x, x.cos())
        >>>
        >>> # Second-order gradients
        >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x)
        >>> assert torch.allclose(neg_sin_x, -x.sin())

    When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients:

        >>> # xdoctest: +SKIP
        >>> from torch.func import grad, vmap
        >>> batch_size, feature_size = 3, 5
        >>>
        >>> def model(weights, feature_vec):
        >>>     # Very simple linear model with activation
        >>>     assert feature_vec.dim() == 1
        >>>     return feature_vec.dot(weights).relu()
        >>>
        >>> def compute_loss(weights, example, target):
        >>>     y = model(weights, example)
        >>>     return ((y - target) ** 2).mean()  # MSELoss
        >>>
        >>> weights = torch.randn(feature_size, requires_grad=True)
        >>> examples = torch.randn(batch_size, feature_size)
        >>> targets = torch.randn(batch_size)
        >>> inputs = (weights, examples, targets)
        >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(*inputs)

    Example of using ``grad`` with ``has_aux`` and ``argnums``:

        >>> # xdoctest: +SKIP
        >>> from torch.func import grad
        >>> def my_loss_func(y, y_pred):
        >>>    loss_per_sample = (0.5 * y_pred - y) ** 2
        >>>    loss = loss_per_sample.mean()
        >>>    return loss, (y_pred, loss_per_sample)
        >>>
        >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True)
        >>> y_true = torch.rand(4)
        >>> y_preds = torch.rand(4, requires_grad=True)
        >>> out = fn(y_true, y_preds)
        >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample))

    .. note::
        Using PyTorch ``torch.no_grad`` together with ``grad``.

        Case 1: Using ``torch.no_grad`` inside a function:

            >>> # xdoctest: +SKIP
            >>> def f(x):
            >>>     with torch.no_grad():
            >>>         c = x ** 2
            >>>     return x - c

        In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``.

        Case 2: Using ``grad`` inside ``torch.no_grad`` context manager:

            >>> # xdoctest: +SKIP
            >>> with torch.no_grad():
            >>>     grad(f)(x)

        In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the
        outer one. This is because ``grad`` is a "function transform": its result
        should not depend on the result of a context manager outside of ``f``.

    """
    # To avoid cyclical dependency.
    import torch._functorch.eager_transforms as eager_transforms

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs)
    return wrapper


@exposed_in("torch.func")
def grad_and_value(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable:
    """
    Returns a function to compute a tuple of the gradient and primal, or
    forward, computation.

    Args:
        func (Callable): A Python function that takes one or more arguments.
            Must return a single-element Tensor. If specified ``has_aux``
            equals ``True``, function can return a tuple of single-element
            Tensor and other auxiliary objects: ``(output, aux)``.
        argnums (int or Tuple[int]): Specifies arguments to compute gradients
            with respect to. ``argnums`` can be single integer or tuple of
            integers. Default: 0.
        has_aux (bool): Flag indicating that ``func`` returns a tensor and
            other auxiliary objects: ``(output, aux)``. Default: False.

    Returns:
        Function to compute a tuple of gradients with respect to its inputs
        and the forward computation. By default, the output of the function is
        a tuple of the gradient tensor(s) with respect to the first argument
        and the primal computation. If specified ``has_aux`` equals
        ``True``, tuple of gradients and tuple of the forward computation with
        output auxiliary objects is returned. If ``argnums`` is a tuple of
        integers, a tuple of a tuple of the output gradients with respect to
        each ``argnums`` value and the forward computation is returned.

    See :func:`grad` for examples
    """
    from torch._functorch import eager_transforms

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        return eager_transforms.grad_and_value_impl(func, argnums, has_aux, args, kwargs)
    return wrapper