This allows to know at any point during the backward pass what is running and where the Node currently running was created at:
```python
import torch
from torch.utils._python_dispatch import TorchDispatchMode
from torch.autograd import detect_anomaly
class MyMode(TorchDispatchMode):
def __torch_dispatch__(self, func, types, args, kwargs=None):
node = torch._C._current_autograd_node()
print(f"Running {func} from within {node}")
if node is not None:
print("The Node was created at:")
print("\n ".join(node.metadata["traceback_"]))
return func(*args, **kwargs or {})
with MyMode(), detect_anomaly():
print("FW")
a = torch.rand(10, requires_grad=True)
b = a.mul(2)
b = b.div(3)
b = b.sum()
print("BW")
b.backward()
```
Gives
```
$ python foo.py
foo.py:15: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
with MyMode(), detect_anomaly():
FW
Running aten.rand.default from within None
Running aten.mul.Tensor from within None
Running aten.div.Tensor from within None
Running aten.sum.default from within None
BW
Running aten.ones_like.default from within None
Running aten.expand.default from within <SumBackward0 object at 0x7fa40c0c6dc0>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten.isnan.default from within <SumBackward0 object at 0x7fa40c0c6500>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten.any.default from within <SumBackward0 object at 0x7fa32b23a780>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten._local_scalar_dense.default from within <SumBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten.div.Tensor from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten.isnan.default from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten.any.default from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten._local_scalar_dense.default from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten.mul.Tensor from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.isnan.default from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.any.default from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten._local_scalar_dense.default from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.detach.default from within <AccumulateGrad object at 0x7fa40c0c9730>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.detach.default from within <AccumulateGrad object at 0x7fa40c0c94b0>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90867
Approved by: https://github.com/soulitzer
We have an older torch.vmap implementation. It is no longer supported.
It still needs to exist somewhere for the sake of BC with
torch.autograd.functional.
This PR makes it clear what files are meant for implementing the old
vmap implementation. I've seen a couple of PRs recently adding support
for the old vmap implementation, so this will lessen the confusion.
Test Plan:
- CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90324
Approved by: https://github.com/samdow
We will need this to implement a convolution meta function that
is SymInt aware. I use templates so that regular convolution code
is not affected by the change. No tests for symbolic ints directly; that will
come in a subsequent PR which also needs to refactor fake tensors.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89069
Approved by: https://github.com/SherlockNoMad
Fixes#81690
TODO:
* [x] C++ Unpickler Fix (locally tested pickled in Python and unpickled in C++)
* [x] C++ Pickler Fix (locally tested pickled in C++ and unpickled in Python)
* [x] Do quant_tensor, sparse_tensor, etc require similar changes? (Sparse and Quant don't need this)
* [x] Add Comments
* [x] How to make sure C++ and Python are in sync? (Functions in `pickler.h` help in getting and setting Tensor Metadata (math-bits for now) on a tensor. They are the only place which should handle this.)
Notes:
Quant Tensor don't support complex dtypes and for float they segfault with `_neg_view` : https://github.com/pytorch/pytorch/issues/88484
Sparse Tensor:
```python
>>> a = torch.tensor([[0, 2.], [3j, 0]]).to_sparse()
>>> a.conj().is_conj()
False
>>> a._neg_view()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NotImplementedError: Cannot access storage of SparseTensorImpl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88182
Approved by: https://github.com/ezyang, https://github.com/anjali411
The logic for determine conv backend and therefore output striding is very complex. It depends on build settings, input striding/contiguity, sizes, etc. Eventually we should port that logic to the meta impl for dynamic shapes but that will require a lot more work and keeping the implementations in sync. See https://github.com/pytorch/torchdynamo/issues/1701
This is a prerequisite to removing the inductor conv stride propagation and more general fake tensor for inductor propagation. In that PR, the meta impls for cpu conv give incorrect striding which led to test failures (https://github.com/pytorch/pytorch/pull/87083).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87305
Approved by: https://github.com/ezyang
# Summary
Add in a torch.backends.cuda flag and update context manager to pic between the three implementations of the scaled_dot_product_attention.
cc @cpuhrsch @jbschlosser @bhosmer @mikaylagawarecki
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87946
Approved by: https://github.com/cpuhrsch
In this PR:
- graph_task stores graph roots on construction so that we can later traverse through the graph
- before the nodes are returned, they needed to be converted from raw_ptr to shared_ptr, and this should be OK because the graph is guaranteed to be alive
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87507
Approved by: https://github.com/albanD
This API adds some improvements to external backends who are building C++ backends out of tree using the `PrivateUse1` dispatch key.
The docs and linked examples go over the API in more detail, but you should be able to use it like:
```
# This should probably be in the __init__.py file of a external backend's python package
> torch.register_privateuse1_backend("foo")`
# And it will allow the user to do this:
> a = torch.ones(2, device="foo")
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86992
Approved by: https://github.com/albanD
# Summary
- This code creates the runtime dispatch system for choosing a performant fused SDP kernel. The only choice of fused kernel is flash_attention. It also creates python flags and a context manager that can be used to turn off and on behavior for dispatch.
- This also adds support for flash_attention with dense tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85984
Approved by: https://github.com/cpuhrsch
If you e.g. printed within a decomp which would call `in_kernel_invocation_manager`, on the exit from the manager it would unilaterally remove meta from the tls / set the tensor to return its real device. We should just restore what the existing state was.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85920
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
Addresses: https://github.com/pytorch/pytorch/issues/83617
This PR a way to query the TLS graph task's exec_info which is a map mapping the Node to a bool indicating whether it will be executed in the current backward pass (as determined by the inputs= argument for .grad of .backward).
- this works with both custom Function nodes and normal codegened nodes
- to be able to verify whether the pyobject passed is an actual node, we now store pointers to PyTypeObjects into a set on registration.
- error out when .backward without inputs= to avoid silently returning True
Alternatives:
- not sure if it is possible to bind to Python from a raw pointer to Node. At least we wouldn't be able to use existing logic, and the Python object should only hold a weak reference to the Node.
- other solutions to the motivating issue seem to require more extensive modification to the engine
See the issue linked for an example of usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84773
Approved by: https://github.com/albanD
This moves functorch's python bindings to torch/csrc/functorch/init.cpp.
Coming next is the torchdim move. I didn't do torchdim yet because
moving functorch's python bindings unblocks some other things that I
want to do first.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85426
Approved by: https://github.com/ezyang
From PR:
```
Note: [Fake Tensor Dispatch Keys]
In order to model the behavior of device-specific autocast
and autograd logic, we update the dispatch keys of FakeTensors
to reflect their fake device. This includes the BackendComponent
(DispatchKey::Meta -> DispatchKey::CUDA), and also the BackendComponent
related Autocast and Autograd keys. __torch__dispatch__ sits below
Autocast and Autograd, and is only invoked when we are at the
kernel for the BackendComponent. Then, we add Meta to the
thread-local dispatch include set to hit the meta kernel
instead of the kernel of the BackendComponent for the fake device.
```
Also adds the `conv1/2/3d.padding` operators to the Autocast rule set. Without that fix, the FakeTensor dtype would diverge.
See: https://github.com/pytorch/pytorch/issues/81608
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82449
Approved by: https://github.com/ezyang
We define specializations for pybind11 defined templates
(in particular, PYBIND11_DECLARE_HOLDER_TYPE) and consequently
it is important that these specializations *always* be #include'd
when making use of pybind11 templates whose behavior depends on
these specializations, otherwise we can cause an ODR violation.
The easiest way to ensure that all the specializations are always
loaded is to designate a header (in this case, torch/csrc/util/pybind.h)
that ensures the specializations are defined, and then add a lint
to ensure this header is included whenever pybind11 headers are
included.
The existing grep linter didn't have enough knobs to do this
conveniently, so I added some features. I'm open to suggestions
for how to structure the features better. The main changes:
- Added an --allowlist-pattern flag, which turns off the grep lint
if some other line exists. This is used to stop the grep
lint from complaining about pybind11 includes if the util
include already exists.
- Added --match-first-only flag, which lets grep only match against
the first matching line. This is because, even if there are multiple
includes that are problematic, I only need to fix one of them.
We don't /really/ need this, but when I was running lintrunner -a
to fixup the preexisting codebase it was annoying without this,
as the lintrunner overall driver fails if there are multiple edits
on the same file.
I excluded any files that didn't otherwise have a dependency on
torch/ATen, this was mostly caffe2 and the valgrind wrapper compat
bindings.
Note the grep replacement is kind of crappy, but clang-tidy lint
cleaned it up in most cases.
See also https://github.com/pybind/pybind11/issues/4099
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82552
Approved by: https://github.com/albanD
