This adds a new env var and flag,
autograd_cache_allow_custom_autograd_functions, (env var: `TORCHINDUCTOR_AUTOGRAD_CACHE_ALLOW_CUSTOM_AUTOGRAD`) which allows custom autograd functions into AOTAutogradCache.
@hirsheybar and I worked together to verify that the higher order op AutogradFunctionApply is pure with respect to the dynamo input being passed in, so this *should* be safe. I'm still putting it behind a flag and turning it on slowly, first on an internal model, though. Once we verify that it is correct on the internal model we can work to enable the flag by default.
Differential Revision: [D71633184](https://our.internmc.facebook.com/intern/diff/D71633184/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149751
Approved by: https://github.com/bdhirsh, https://github.com/zou3519
We didn't support multiple levels of vmap. The main problem is, during
the batching rule, we need to exclude the vmap dispatch key
(FuncTorchBatched) like how our C++ batching rules do it.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137306
Approved by: https://github.com/Chillee
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
For training graphs (when inputs require grad), previously, we would speculate the forward and backward graph to determine if there are any graph breaks, side effect and etc but would not actually use these speculated graphs. We would just insert a call function node on the graph and later rely on autograd's tracing.
This approach does not work for more generalized graphs like graphs that include user defined triton kernels because autograd is not able to do the higher order function conversation.
This PR speculates the forward and backward functions and emits them in a HOF that later gets used via templating mechanism.
While working on this PR, I have exposed some bugs in the current tracing due to trampoline functions losing the source information resulting in incorrect graphs being produced. I have fixed these source information bugs and killed the trampolines.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116897
Approved by: https://github.com/Skylion007, https://github.com/jansel, https://github.com/voznesenskym
For training graphs (when inputs require grad), previously, we would speculate the forward and backward graph to determine if there are any graph breaks, side effect and etc but would not actually use these speculated graphs. We would just insert a call function node on the graph and later rely on autograd's tracing.
This approach does not work for more generalized graphs like graphs that include user defined triton kernels because autograd is not able to do the higher order function conversation.
This PR speculates the forward and backward functions and emits them in a HOF that later gets used via templating mechanism.
While working on this PR, I have exposed some bugs in the current tracing due to trampoline functions losing the source information resulting in incorrect graphs being produced. I have fixed these source information bugs and killed the trampolines.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116358
Approved by: https://github.com/jansel
Twice this week I have had people confuse "operator defined with Python
operator registration aka torch.library" and "PyOperator which is used
to define control flow operators and other operators that cannot be
represented in JIT schema." Renaming PyOperator for clarity.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97493
Approved by: https://github.com/SherlockNoMad
functorch used to have a switch that enables/disables autograd.Function.
That switch now enables/disables torch.autograd.function._SingleLevelFunction, so
I've renamed it accordingly.
We could just delete the switch because users should not be directly
working with torch.autograd.function._SingleLevelFunction. However,
it was useful for debugging when something went wrong when I was
implementing the autograd.Function <> functorch interaction, so I want
to keep it around as a debugging tool for a while since the code is
already there.
Test Plan:
- updated tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92025
Approved by: https://github.com/soulitzer
We don't actually need `output_shapes` to implement
`generate_vmap_rule=True` support for autograd.Function.
- We need this in the vjp (backward) case because autograd automatically
reduces grad_inputs to inputs and we need to replicate that behavior.
In order to replicate that behavior, we recorded the original input
shapes so we know how to reduce the grad_input.
- There is no such behavior for forward-mode AD, so we don't need to
pass an `output_shapes` to reductify.
This PR simplifies the API of `reductify` and `reductify_leaf`. Instead
of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we
now combine these into a single argument,
`reduce_to_input_shape_without_bdim`.
- if it is None, then we don't do anything
- if it is not-None and a shape, then we will reduce the grad to the
provided shape.
Test Plan:
- updated original unittests
- wait for test suite
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92024
Approved by: https://github.com/soulitzer
This PR:
- adds a nice error message if the user doesn't follow the API of the
vmap staticmethod correctly. That is, the user must return two
arguments from the vmap staticmethod API: (outputs, out_dims), and
out_dims must be a PyTree with either the same structure as `outputs`
our be broadcastable to the same structure as `outputs`.
- Fixes an edge case for out_dims=None. out_dims is allowed to be None,
but wrap_outputs_maintaining_identity was treating "None" as "This is
not the vmap case"
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92023
Approved by: https://github.com/soulitzer
This PR:
- changes generate_vmap_rule to either be True or False. Previously it
could be True, False, or not set. This simplifies the implementation a
bit.
- changes the vmap staticmethod to always be on the autograd.Function
rather than sometimes defined.
This is how the other staticmethod (forward, backward, jvp) are
implemented and allows us to document it.
There are 4 possible states for the autograd.Function w.r.t. to the
above:
- generate_vmap_rule is True, vmap staticmethod overriden. This raises
an error when used with vmap.
- generate_vmap_rule is False, vmap staticmethod overriden. This is
valid.
- generate_vmap_rule is True, vmap staticmethod not overriden. This is
valid.
- generate_vmap_rule is False, vmap staticmethod not overriden. This
raises an error when used with vmap.
Future:
- setup_context needs the same treatment, but that's a bit tricker to
implement.
Test Plan:
- new unittest
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91787
Approved by: https://github.com/soulitzer
Support for jvp is very similar to support for backward():
- We need to vmap over a version of the original autograd.Function's jvp
method that does not take ctx as input.
- On the output, we need to reductify to ensure the output tangent has
the same shape as the output. This reductify does not have the
extra reduction semantics, because PyTorch forward-mode AD requires the
output tangent to have the same exact shape as the output.
- setup_context needs to tell us the bdims of the saved_tensors
(necessary for vmap over jvp_no_context), as well
as the output shapes (necessary for reductify).
Test Plan:
- Added jvp support to the *GenVmapAutogradFunction
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91211
Approved by: https://github.com/soulitzer
Design document:
https://docs.google.com/document/d/1bIQkWXy3J35_20c_a5kchikabBW5M8_uRAhl0BIMwU4/edit
This PR adds a `generate_vmap_rule` option (default False) to autograd.Function.
By setting it to True, a user promises to us that their autograd.Function's
{forward, backward, jvp}, if defined, only uses PyTorch operations, in addition to the other
limitations of autograd.Function+functorch (such as the user not
capturing any Tensors being transformed over from outside of the
autograd.Function).
Concretely, the approach is:
- we update `custom_function_call` to accept an additional
`generate_vmap_rule` argument.
- The vmap rule for `custom_function_call` and `generate_vmap_rule=True`
is: we construct a vmapped version of the autograd.Function and dispatch
on it.
- The vmapped version of the autograd.Function can be thought of like
the following: if we have an autograd.Function Foo, then
VmappedFoo.apply(in_dims, ...) has the same semantics as
vmap(Foo.apply, in_dims...)
- VmappedFoo's forward, setup_context, and backward staticmethod are
vmapped versions of Foo's staticmethods.
- See the design doc for more motivation and explanation
Test Plan:
- This PR introduces additional autograd.Function with the suffix "GenVmap" to
autograd_function_db.
- There are also some minor UX tests
Future:
- jvp support
- likely more testing to come, but please let me know if you have
cases that you want me to test here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90966
Approved by: https://github.com/soulitzer
As seen in
https://docs.google.com/document/d/1bIQkWXy3J35_20c_a5kchikabBW5M8_uRAhl0BIMwU4/edit
`reductify_leaf(grad_input, ...)` is a helper function that processes a
single grad_input Tensor. The reason why we need it is:
- the grad_input has some optional bdim
- the input has some optional bdim
- if these are different, we need to coerce the grad_input into having
the same shape as the input, either by reducing or expanding the
grad_input.
Note that there is a special case in autograd that the user is allowed
to return a grad_input Tensor that is an expanded version of the
original input tensor. In this case, autograd automatically reduces
grad_input to the same shape as the input. Unfortunately this logic
doesn't work when bdims are involved, so we manually handle it in
`reductify_leaf`.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90965
Approved by: https://github.com/soulitzer
As seen in
https://docs.google.com/document/d/1bIQkWXy3J35_20c_a5kchikabBW5M8_uRAhl0BIMwU4/edit
`restore_vmap` is a private helper function. It is vmap but has the
following
differences:
- instead of returning outputs, it returns an (outputs, out_dims) tuple.
out_dims is a pytree of shape shape as outputs and contains Optional[int]
specifying where the vmapped dimension, if it exists, is in the
corresponding output.
- does no validation on in_dims or inputs (vmap expects at least one
Tensor to be vmapped).
restore_vmap allows for no inputs to have the vmap dimension
- does no validation on outputs (vmap expects only Tensor outputs)
restore_vmap allows for return of arbitrary outputs (not just
Tensors)
Test Plan:
- added some simple test to test restore_vmap
- I am OK with restore_vmap not being a part of vmap right now -- the
implementation of vmap rarely changes and it is a bit difficult to
refactor vmap in a way that restore_vmap is a subroutine.
Other questions:
- Bikeshedding the `restore_vmap` name
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90963
Approved by: https://github.com/samdow, https://github.com/soulitzer
This PR:
- adds VmapInterpreter.randomness. This returns the randomness option
the user provided in vmap(..., randomness=...)
- adds randomness in the info object passed to the vmap staticmethod of
autograd.Function. This is so that the user can handle random operations
on their own terms (if randomness="error", and if the autograd.Function
has random operations, then it is the user's responsiblity to raise an
error).
Test Plan:
- updated unittest
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90789
Approved by: https://github.com/samdow, https://github.com/soulitzer
It turns out it is possible to break cycles by not directly importing a
module:
- there's a problem that torch.jit imports torch._ops and torch._ops
import torch.jit
- there's another problem that torch.autograd.function imports
custom_function_call but torch._functorch.autograd_function imports
torch.autograd.function
The "better" way to handle all of this is to do some large refactoring so
that torch._functorch.autograd_function imports some file that has
_SingleLevelAutogradFunction and then have torch.autograd.function
depend on torch.functorch.autograd_function... (and ditto for torch.jit
vs torch._ops), but I'm scared to move code around too much for BC
reasons and the fix in this PR works well.
Test Plan:
- import torch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90415
Approved by: https://github.com/albanD, https://github.com/soulitzer
This PR adds functorch.jvp support for autograd.Function. It does so by
adding a jvp rule for custom_function_call.
For a regular PyTorch operation (like at::sin), the VariableType kernel:
- re-dispatches to at::sin
- calls the jvp rule for at::sin
The jvp rule for custom_function_call does just that. It constructs a
new autograd.Function (because the above logic already exists). Inside
the forward, it re-dispatches to custom_function_call. In the jvp rule,
it just calls whatever the jvp rule is supposed to be.
Since this logic is really close to the custom_function_call_grad, I
just put them together.
Test Plan:
- added jvp rules to the autograd.Function in autograd_function_db
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90077
Approved by: https://github.com/albanD, https://github.com/soulitzer
This PR adds a `vmap` staticmethod to autograd.Function and a
corresponding vmap kernel for custom_function_call. These two items mean
that autograd.Function with a vmap staticmethod can be used with vmap.
```py
class NumpyMul(torch.autograd.Function)
staticmethod
def forward(x, y):
return torch.tensor(to_numpy(x) * to_numpy(y), device=x.device)
staticmethod
def setup_context(ctx, outputs, x, y):
ctx.save_for_backward(x, y)
staticmethod
def backward(ctx, grad_output):
x, y = ctx.saved_tensors
gx = None
if isinstance(x, torch.Tensor) and x.requires_grad:
gx = NumpyMul.apply(grad_output, y)
gy = None
if isinstance(y, torch.Tensor) and y.requires_grad:
gy = NumpyMul.apply(grad_output, x)
return gx, gy
staticmethod
def vmap(info, in_dims, x, y):
x_bdim, y_bdim = in_dims
x = x.movedim(x_bdim, -1) if x_bdim else x.unsqueeze(-1)
y = y.movedim(y_bdim, -1) if y_bdim else y.unsqueeze(-1)
result = NumpyMul.apply(x, y)
result = result.movedim(-1, 0)
return result, 0
```
API Spec
- the staticmethod takes two arguments (info, in_dims) as well as the
unexpanded inputs (x, y).
- If we think about it as `vmap(info, in_dims, *args)`, `in_dims` is a
pytree with the same tree structure as args. It has None if the arg is
not being vmapped over and an integer vmapped dimension index if it is.
- `info` is an object with metadata about the vmap. It currently has one
field, `info.batch_size`. In the future we can extend this by adding
things like the randomness information.
- If there is a single vmap going on, (x, y) are NOT BatchedTensors,
they've already been unpacked.
- We expect the user to return a `(outputs, out_dims)` tuple. `out_dims`
must "broadcast" to the same pytree structure as `outputs`.
Semantics
- vmap(NumpyMul.apply)(x) will apply the vmap staticmethod if there is
one and will never actually run NumpyMul.forward.
- In order for the autograd.Function to support nested vmap (e.g.,
`vmap(vmap(NumpyMul.apply))(x)`, then the vmap staticmethod must call
into operations that vmap understands (i.e. PyTorch operators or more
autograd.Function).
At a high level, this PR:
- adds a vmap rule for custom_function_call
Testing
- Added some tests for in_dims and info
- Added vmap staticmethod to most of the autograd.Function in
autograd_function_db and sent them through functorch's vmap-related
OpInfo tests
Future
- Better error messages if the user gets the return contract wrong. I
didn't include them in this PR because it might involve a refactor of
some of the existing code in functorch/_src/vmap.py that will add
~200LOC to the PR, but LMK if you'd prefer it here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90037
Approved by: https://github.com/samdow, https://github.com/soulitzer
Happy to split this PR more if it helps.
This PR adds functorch.grad support for autograd.Function. There's a lot
going on; here is the high level picture and there are more details as
comments in the code.
Mechanism (PyOperator)
- Somehow, autograd.Function needs to dispatch with functorch. This is
necessary because every layer of functorch needs to see the
autograd.Function; grad layers need to preserve the backward pass.
- The mechanism for this is via PyOperator. If functorch transforms are
active, then we wrap the autograd.Function in a `custom_function_call`
PyOperator where we are able to define various rules for functorch
transforms.
- `custom_function_call` has a rule for the functorch grad transform.
autograd.Function changes
- I needed to make some changes to autograd.Function to make this work.
- First, this PR splits autograd.Function into a _SingleLevelFunction
(that works with a single level of functorch transform) and
autograd.Function (which works with multiple levels). This is necessary
because functorch's grad rule needs some way of specifying a backward
pass for that level only.
- This PR changes autograd.Function's apply to eitehr call
`custom_function_call` (if functorch is active) or super().apply (if
functorch isn't active).
Testing
- Most of this PR is just testing. It creates an autograd.Function
OpInfo database that then gets passed to the functorch grad-based tests
(grad, vjp, vjpvjp).
- Since functorch transform tests are autogenerated from OpInfo tests,
this is the easiest way to test various autograd.Function with
functorch.
Future
- jvp and vmap support coming next
- better error message (functorch only supports autograd.Function that
have the optional setup_context staticmethod)
- documentation to come when we remove the feature flag
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89860
Approved by: https://github.com/soulitzer
