torch.func.vmap¶
- torch.func.vmap(func, in_dims=0, out_dims=0, randomness='error', *, chunk_size=None)¶
vmap is the vectorizing map;
vmap(func)
returns a new function that mapsfunc
over some dimension of the inputs. Semantically, vmap pushes the map into PyTorch operations called byfunc
, 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 withvmap(func)
. vmap can also be used to compute batched gradients when composed with autograd.Note
torch.vmap()
is aliased totorch.func.vmap()
for convenience. Use whichever one you’d like.- Parameters:
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 thein_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
chunk_size
samples at a time. Note thatchunk_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 byin_dims
. It takes returns the same outputs asfunc
, except each output has an extra dimension at the index specified byout_dims
.- Return type:
One example of using
vmap()
is to compute batched dot products. PyTorch doesn’t provide a batchedtorch.dot
API; instead of unsuccessfully rummaging through docs, usevmap()
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)
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 toautograd.grad
, one per Jacobian row. Usingvmap()
, we can vectorize the whole computation, computing the Jacobian in a single call toautograd.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)
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.