[torch.func] alias torch.func.vmap as torch.vmap (#91026)

This PR also redirects torch.vmap to torch.func.vmap instead of the old
vmap prototype.

Test Plan:
- tests
- view docs preview
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91026
Approved by: https://github.com/albanD, https://github.com/samdow

aten/src/ATen/LegacyBatchedFallback.cpp

@@ -72,10 +72,11 @@ static void warnFallback(const c10::FunctionSchema& schema) {
   }
   TORCH_WARN("There is a performance drop because we have not yet implemented ",
              "the batching rule for ", schema.operator_name(), ". ",
-             "We've moved development of vmap to to functorch "
+             "You are using the legacy vmap prototype (torch._vmap_internals.vmap). ",
-             "(https://github.com/pytorch/functorch), please try functorch.vmap "
+             "If you are using torch.autograd.functional.{jacobian, hessian} ",
-             "instead and/or file ",
+             "or torch._vmap_internals.vmap: please switch to using ",
-             " an issue on GitHub so that we can prioritize its implementation.");
+             "torch.func.{jacrev, jacfwd, hessian} and/or torch.vmap instead ",
+             "for better operator coverage and performance improvements .");
 }
 
 // The general flow of the algorithm is as follows.

test/test_legacy_vmap.py

@@ -3,7 +3,8 @@
 from torch.testing._internal.common_utils import TestCase, run_tests
 import torch
 import torch.nn.functional as F
-from torch import Tensor, vmap
+from torch import Tensor
+from torch._vmap_internals import vmap
 import functools
 import itertools
 import warnings

torch/__init__.py

@@ -48,7 +48,7 @@ __all__ = [
     'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
     'set_float32_matmul_precision', 'get_float32_matmul_precision',
     'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
-    'compile',
+    'compile', 'vmap',
 ]
 
 ################################################################################
@@ -1116,8 +1116,6 @@ del register_after_fork
 # torch.jit.script as a decorator, for instance):
 from ._lobpcg import lobpcg as lobpcg
 
-from ._vmap_internals import vmap as vmap
-
 # These were previously defined in native_functions.yaml and appeared on the
 # `torch` namespace, but we moved them to c10 dispatch to facilitate custom
 # class usage. We add these lines here to preserve backward compatibility.
@@ -1245,3 +1243,4 @@ if 'TORCH_CUDA_SANITIZER' in os.environ:
 import torch.fx.experimental.symbolic_shapes
 
 from torch import func as func
+from torch.func import vmap

torch/_functorch/eager_transforms.py

@@ -1259,8 +1259,7 @@ def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Calla
 
     When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients:
 
-        >>> from torch.func import grad
+        >>> from torch.func import grad, vmap
-        >>> from torch.func import vmap
         >>> batch_size, feature_size = 3, 5
         >>>
         >>> def model(weights, feature_vec):

torch/_functorch/vmap.py

@@ -255,6 +255,10 @@ def vmap(
     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.
@@ -308,7 +312,7 @@ def vmap(
         >>>     return feature_vec.dot(weights).relu()
         >>>
         >>> examples = torch.randn(batch_size, feature_size)
-        >>> result = torch.func.vmap(model)(examples)
+        >>> 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.
@@ -333,12 +337,12 @@ def vmap(
         >>> # vectorized gradient computation
         >>> def get_vjp(v):
         >>>     return torch.autograd.grad(y, x, v)
-        >>> jacobian = torch.func.vmap(get_vjp)(I_N)
+        >>> 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.func.vmap(torch.func.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
+        >>> 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]
 
@@ -346,7 +350,7 @@ def vmap(
     the dimension that each inputs are batched along as
 
         >>> torch.dot                            # [N], [N] -> []
-        >>> batched_dot = torch.func.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
+        >>> 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
 
@@ -354,7 +358,7 @@ def vmap(
     ``in_dims`` must be a tuple with the batch dimension for each input as
 
         >>> torch.dot                            # [D], [D] -> []
-        >>> batched_dot = torch.func.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
+        >>> 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
 
@@ -364,7 +368,7 @@ def vmap(
         >>> 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.func.vmap(f, in_dims=({'x': 0, 'y': None},))
+        >>> 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
@@ -372,17 +376,17 @@ def vmap(
 
         >>> f = lambda x: x ** 2
         >>> x = torch.randn(2, 5)
-        >>> batched_pow = torch.func.vmap(f, out_dims=1)
+        >>> 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 f(x, scale=4.):
+        >>> def fn(x, scale=4.):
         >>>   return x * scale
         >>>
-        >>> batched_pow = torch.func.vmap(f)
+        >>> 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]
 

torch/_vmap_internals.py

@@ -191,111 +191,10 @@ def _get_name(func: Callable):
 # 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
+    Please use torch.vmap instead of this API.
-    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 "
+        "Please use torch.vmap instead of torch._vmap_internals.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)

torch/autograd/gradcheck.py

@@ -870,7 +870,7 @@ def _test_batched_grad(input, output, output_idx) -> bool:
     # NB: this doesn't work for CUDA tests: https://github.com/pytorch/pytorch/issues/50209
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", message="There is a performance drop")
-        warnings.filterwarnings("ignore", message="Please use functorch.vmap")
+        warnings.filterwarnings("ignore", message="Please use torch.vmap")
         try:
             result = vmap(vjp)(torch.stack(grad_outputs))
         except RuntimeError as ex:

torch/overrides.py

@@ -238,6 +238,7 @@ def get_ignored_functions() -> Set[Callable]:
         torch.vitals_enabled,
         torch.set_vital,
         torch.read_vitals,
+        torch.vmap,
         torch.frombuffer,
         torch.asarray,
         Tensor.__delitem__,