402 lines
18 KiB
Python
402 lines
18 KiB
Python
# NOTE: We allow Dynamo to see this file (via torch/_dynamo/trace_rules.py) so that it can
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# trace through functorch transforms.
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# Currently, we can't allow Dynamo to see `eager_transforms.py`/`vmap.py` as that break a lot of thing
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# and there isn't a mechanism to selectively expose only some functions (eg. grad) from a file
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# to Dynamo.
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from torch._functorch.vmap import (vmap_impl, _check_randomness_arg,
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Callable, in_dims_t, out_dims_t, _check_out_dims_is_int_or_int_pytree,
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_process_batched_inputs, _chunked_vmap)
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from torch._functorch.utils import exposed_in, argnums_t
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import functools
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# vmap(func)(inputs) wraps all Tensor inputs to be batched in BatchedTensors,
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# sends those into func, and then unwraps the output BatchedTensors. Operations
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# on BatchedTensors perform the batched operations that the user is asking for.
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#
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# vmap's randomness behavior differs from JAX's, which would require a PRNG key
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# to be passed everywhere.
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@exposed_in('torch.func')
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def vmap(
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func: Callable,
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in_dims: in_dims_t = 0,
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out_dims: out_dims_t = 0,
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randomness: str = 'error',
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*,
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chunk_size=None) -> Callable:
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"""
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vmap is the vectorizing map; ``vmap(func)`` returns a new function that
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maps ``func`` over some dimension of the inputs. Semantically, vmap
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pushes the map into PyTorch operations called by ``func``, effectively
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vectorizing those operations.
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vmap is useful for handling batch dimensions: one can write a function
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``func`` that runs on examples and then lift it to a function that can
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take batches of examples with ``vmap(func)``. vmap can also be used to
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compute batched gradients when composed with autograd.
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.. note::
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:func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
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convenience. Use whichever one you'd like.
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Args:
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func (function): A Python function that takes one or more arguments.
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Must return one or more Tensors.
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in_dims (int or nested structure): Specifies which dimension of the
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inputs should be mapped over. ``in_dims`` should have a
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structure like the inputs. If the ``in_dim`` for a particular
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input is None, then that indicates there is no map dimension.
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Default: 0.
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out_dims (int or Tuple[int]): Specifies where the mapped dimension
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should appear in the outputs. If ``out_dims`` is a Tuple, then
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it should have one element per output. Default: 0.
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randomness (str): Specifies whether the randomness in this
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vmap should be the same or different across batches. If 'different',
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the randomness for each batch will be different. If 'same', the
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randomness will be the same across batches. If 'error', any calls to
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random functions will error. Default: 'error'. WARNING: this flag
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only applies to random PyTorch operations and does not apply to
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Python's random module or numpy randomness.
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chunk_size (None or int): If None (default), apply a single vmap over inputs.
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If not None, then compute the vmap :attr:`chunk_size` samples at a time.
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Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
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If you run into memory issues computing the vmap, please try a non-None chunk_size.
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Returns:
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Returns a new "batched" function. It takes the same inputs as
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``func``, except each input has an extra dimension at the index
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specified by ``in_dims``. It takes returns the same outputs as
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``func``, except each output has an extra dimension at the index
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specified by ``out_dims``.
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.. warning:
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:func:`vmap` works best with functional-style code. Please do not
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perform any side-effects in ``func``, with the exception of
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in-place PyTorch operations. Examples of side-effects include mutating
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Python data structures and assigning values to variables not captured
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in ``func``.
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One example of using :func:`vmap` is to compute batched dot products. PyTorch
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doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
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rummaging through docs, use :func:`vmap` to construct a new function.
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>>> torch.dot # [D], [D] -> []
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>>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N]
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>>> x, y = torch.randn(2, 5), torch.randn(2, 5)
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>>> batched_dot(x, y)
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:func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
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model authoring experience.
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>>> batch_size, feature_size = 3, 5
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>>> weights = torch.randn(feature_size, requires_grad=True)
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>>>
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>>> def model(feature_vec):
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>>> # Very simple linear model with activation
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>>> return feature_vec.dot(weights).relu()
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>>>
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>>> examples = torch.randn(batch_size, feature_size)
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>>> result = torch.vmap(model)(examples)
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:func:`vmap` can also help vectorize computations that were previously difficult
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or impossible to batch. One example is higher-order gradient computation.
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The PyTorch autograd engine computes vjps (vector-Jacobian products).
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Computing a full Jacobian matrix for some function f: R^N -> R^N usually
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requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
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we can vectorize the whole computation, computing the Jacobian in a single
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call to ``autograd.grad``.
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>>> # Setup
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>>> N = 5
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>>> f = lambda x: x ** 2
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>>> x = torch.randn(N, requires_grad=True)
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>>> y = f(x)
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>>> I_N = torch.eye(N)
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>>>
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>>> # Sequential approach
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>>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
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>>> for v in I_N.unbind()]
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>>> jacobian = torch.stack(jacobian_rows)
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>>>
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>>> # vectorized gradient computation
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>>> def get_vjp(v):
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>>> return torch.autograd.grad(y, x, v)
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>>> jacobian = torch.vmap(get_vjp)(I_N)
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:func:`vmap` can also be nested, producing an output with multiple batched dimensions
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>>> torch.dot # [D], [D] -> []
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>>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0]
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>>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
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>>> batched_dot(x, y) # tensor of size [2, 3]
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If the inputs are not batched along the first dimension, ``in_dims`` specifies
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the dimension that each inputs are batched along as
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>>> torch.dot # [N], [N] -> []
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>>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D]
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>>> x, y = torch.randn(2, 5), torch.randn(2, 5)
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>>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension
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If there are multiple inputs each of which is batched along different dimensions,
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``in_dims`` must be a tuple with the batch dimension for each input as
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>>> torch.dot # [D], [D] -> []
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>>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N]
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>>> x, y = torch.randn(2, 5), torch.randn(5)
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>>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
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If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
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matching the shape of the input:
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>>> f = lambda dict: torch.dot(dict['x'], dict['y'])
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>>> x, y = torch.randn(2, 5), torch.randn(5)
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>>> input = {'x': x, 'y': y}
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>>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
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>>> batched_dot(input)
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By default, the output is batched along the first dimension. However, it can be batched
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along any dimension by using ``out_dims``
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>>> f = lambda x: x ** 2
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>>> x = torch.randn(2, 5)
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>>> batched_pow = torch.vmap(f, out_dims=1)
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>>> batched_pow(x) # [5, 2]
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For any function that uses kwargs, the returned function will not batch the kwargs but will
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accept kwargs
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>>> x = torch.randn([2, 5])
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>>> def fn(x, scale=4.):
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>>> return x * scale
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>>>
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>>> batched_pow = torch.vmap(fn)
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>>> assert torch.allclose(batched_pow(x), x * 4)
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>>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
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.. note::
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vmap does not provide general autobatching or handle variable-length
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sequences out of the box.
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"""
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_check_randomness_arg(randomness)
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if not (chunk_size is None or chunk_size > 0):
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raise ValueError(f"vmap: chunk_size should be None or greater than 0. (got {chunk_size})")
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# @functools.wraps(func)
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def wrapped(*args, **kwargs):
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return vmap_impl(func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs)
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return wrapped
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def chunk_vmap(
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func: Callable,
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in_dims: in_dims_t = 0,
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out_dims: out_dims_t = 0,
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randomness: str = 'error',
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chunks=2) -> Callable:
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"""
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chunk_vmap is the vectorizing map (vmap) using chunks of input data. It is a mix of vmap (which vectorizes
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everything) and map (which executes things sequentially). ``chunk_vmap`` vectorizes the input with number of
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chunks at a time. For more details about vectorizing map, see :func:`vmap`.
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.. note::
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Please use :func:`vmap` with ``chunk_size`` argument instead of this API.
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Args:
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func (function): A Python function that takes one or more arguments.
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Must return one or more Tensors.
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in_dims (int or nested structure): Specifies which dimension of the
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inputs should be mapped over. ``in_dims`` should have a
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structure like the inputs. If the ``in_dim`` for a particular
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input is None, then that indicates there is no map dimension.
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Default: 0.
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out_dims (int or Tuple[int]): Specifies where the mapped dimension
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should appear in the outputs. If ``out_dims`` is a Tuple, then
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it should have one element per output. Default: 0.
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randomness (str): Specifies whether the randomness in this
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vmap should be the same or different across batches. If 'different',
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the randomness for each batch will be different. If 'same', the
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randomness will be the same across batches. If 'error', any calls to
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random functions will error. Default: 'error'. WARNING: this flag
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only applies to random PyTorch operations and does not apply to
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Python's random module or numpy randomness.
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chunks (int): Number of chunks to use to split the input data. Default is 2.
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If equals to 1 then :func:`vmap` is called.
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Returns:
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Returns a new "batched" function. It takes the same inputs as
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``func``, except each input has an extra dimension at the index
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specified by ``in_dims``. It takes returns the same outputs as
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``func``, except each output has an extra dimension at the index
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specified by ``out_dims``.
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"""
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_check_randomness_arg(randomness)
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if chunks == 1:
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return vmap(func, in_dims=in_dims, out_dims=out_dims, randomness=randomness)
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def _get_chunk_flat_args(flat_args_, flat_in_dims_, chunks_):
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flat_args_chunks = tuple(
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t.chunk(chunks_, dim=in_dim) if in_dim is not None else [t, ] * chunks_
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for t, in_dim in zip(flat_args_, flat_in_dims_)
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)
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# transpose chunk dim and flatten structure
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# chunks_flat_args is a list of flatten args
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chunks_flat_args = zip(*flat_args_chunks)
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return chunks_flat_args
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@functools.wraps(func)
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def wrapped_with_chunks(*args, **kwargs):
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_check_out_dims_is_int_or_int_pytree(out_dims, func)
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_, flat_in_dims, flat_args, args_spec = _process_batched_inputs(in_dims, args, func)
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# Chunk flat arguments
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chunks_flat_args = _get_chunk_flat_args(flat_args, flat_in_dims, chunks)
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# Apply vmap on chunks
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return _chunked_vmap(func, flat_in_dims, chunks_flat_args, args_spec, out_dims, randomness, **kwargs)
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return wrapped_with_chunks
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@exposed_in("torch.func")
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def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable:
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"""``grad`` operator helps computing gradients of ``func`` with respect to the
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input(s) specified by ``argnums``. This operator can be nested to
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compute higher-order gradients.
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Args:
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func (Callable): A Python function that takes one or more arguments.
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Must return a single-element Tensor. If specified ``has_aux`` equals ``True``,
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function can return a tuple of single-element Tensor and other auxiliary objects:
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``(output, aux)``.
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argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to.
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``argnums`` can be single integer or tuple of integers. Default: 0.
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has_aux (bool): Flag indicating that ``func`` returns a tensor and other
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auxiliary objects: ``(output, aux)``. Default: False.
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Returns:
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Function to compute gradients with respect to its inputs. By default, the output of
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the function is the gradient tensor(s) with respect to the first argument.
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If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects
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is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with
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respect to each ``argnums`` value is returned.
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Example of using ``grad``:
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>>> # xdoctest: +SKIP
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>>> from torch.func import grad
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>>> x = torch.randn([])
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>>> cos_x = grad(lambda x: torch.sin(x))(x)
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>>> assert torch.allclose(cos_x, x.cos())
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>>>
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>>> # Second-order gradients
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>>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x)
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>>> assert torch.allclose(neg_sin_x, -x.sin())
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When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients:
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>>> # xdoctest: +SKIP
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>>> from torch.func import grad, vmap
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>>> batch_size, feature_size = 3, 5
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>>>
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>>> def model(weights, feature_vec):
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>>> # Very simple linear model with activation
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>>> assert feature_vec.dim() == 1
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>>> return feature_vec.dot(weights).relu()
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>>>
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>>> def compute_loss(weights, example, target):
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>>> y = model(weights, example)
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>>> return ((y - target) ** 2).mean() # MSELoss
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>>>
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>>> weights = torch.randn(feature_size, requires_grad=True)
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>>> examples = torch.randn(batch_size, feature_size)
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>>> targets = torch.randn(batch_size)
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>>> inputs = (weights, examples, targets)
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>>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(*inputs)
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Example of using ``grad`` with ``has_aux`` and ``argnums``:
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>>> # xdoctest: +SKIP
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>>> from torch.func import grad
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>>> def my_loss_func(y, y_pred):
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>>> loss_per_sample = (0.5 * y_pred - y) ** 2
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>>> loss = loss_per_sample.mean()
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>>> return loss, (y_pred, loss_per_sample)
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>>>
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>>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True)
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>>> y_true = torch.rand(4)
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>>> y_preds = torch.rand(4, requires_grad=True)
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>>> out = fn(y_true, y_preds)
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>>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample))
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.. note::
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Using PyTorch ``torch.no_grad`` together with ``grad``.
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Case 1: Using ``torch.no_grad`` inside a function:
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>>> # xdoctest: +SKIP
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>>> def f(x):
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>>> with torch.no_grad():
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>>> c = x ** 2
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>>> return x - c
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In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``.
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Case 2: Using ``grad`` inside ``torch.no_grad`` context manager:
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>>> # xdoctest: +SKIP
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>>> with torch.no_grad():
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>>> grad(f)(x)
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In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the
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outer one. This is because ``grad`` is a "function transform": its result
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should not depend on the result of a context manager outside of ``f``.
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"""
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# To avoid cyclical dependency.
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import torch._functorch.eager_transforms as eager_transforms
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs)
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return wrapper
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@exposed_in("torch.func")
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def grad_and_value(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable:
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"""
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Returns a function to compute a tuple of the gradient and primal, or
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forward, computation.
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Args:
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func (Callable): A Python function that takes one or more arguments.
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Must return a single-element Tensor. If specified ``has_aux``
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equals ``True``, function can return a tuple of single-element
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Tensor and other auxiliary objects: ``(output, aux)``.
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argnums (int or Tuple[int]): Specifies arguments to compute gradients
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with respect to. ``argnums`` can be single integer or tuple of
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integers. Default: 0.
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has_aux (bool): Flag indicating that ``func`` returns a tensor and
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other auxiliary objects: ``(output, aux)``. Default: False.
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Returns:
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Function to compute a tuple of gradients with respect to its inputs
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and the forward computation. By default, the output of the function is
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a tuple of the gradient tensor(s) with respect to the first argument
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and the primal computation. If specified ``has_aux`` equals
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``True``, tuple of gradients and tuple of the forward computation with
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output auxiliary objects is returned. If ``argnums`` is a tuple of
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integers, a tuple of a tuple of the output gradients with respect to
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each ``argnums`` value and the forward computation is returned.
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See :func:`grad` for examples
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"""
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from torch._functorch import eager_transforms
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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return eager_transforms.grad_and_value_impl(func, argnums, has_aux, args, kwargs)
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return wrapper
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