Previously, under config.only_allow_pt2_compliant_ops, Dynamo graph
breaks when it see an OpOverloadPacket where any overloads are not
PT2 compliant. This is potentially brittle: if someone (unlikely) adds
a new overload for a custom operator, then this would cause a
previously non-graph-breaking call to the OpOverloadPacket to graph break.
In this PR:
- When Dynamo is about to write a call to an operator to the FX graph,
we check if it is PT2 compliant.
- For OpOverload, we check to see if the tag is on it
- For OpOverloadPacket, we do overload resolution and check to see if
the tag is on the OpOverload that it resolves to.
Test Plan:
- new tests, existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112200
Approved by: https://github.com/bdhirsh
Major change in this PR is to make torch context manager class a separate ```TorchCtxManagerClassVariable```, since we have dynamo implementation for these ctx managers.
I was thinking to wrap them as ```UserDefinedClassVariable``` and do dispatch at ```USCVariable.call_function```, but it seems almost the same amount of work and this way is more clear.
This is on the way of moving ```TorchVariable``` to ```TorchFunctionVariable``` which will only handle the functions who would be allowed in graph (e.g, ```torch.sin```) and constant folded (e.g, ```torch.is_floating_point```). All other torch functions would be go through skip/inline rules, and would be wrapped as ```UserFunctionVariable``` (for inlined) and ```SkipFilesVariable``` (for skipped).
The next steps:
* Wrap torch modules, classes, objects as regular ```PythonModuleVariable```, ```UserDefinedClassVariable``` and ```UserDefinedObjectVariable```.
* Generate the allow in graph torch functions list and wrap them as ```TorchFunctionVariable```.
* Finally merge ```skipfiles.check``` and ```is_allowed``` into one function ```allow_skip.check(fn)``` which would return a Enum of allow, skip and inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111622
Approved by: https://github.com/jansel
This PR:
- Moves TrueDiv, LShift, RShift, IsNonOverlappingAndDenseIndicator to `_sympy.functions.py`
- Moves SymNode to `fx.experimental.sym_node`.
- This file does not have any SymPy dependencies at import time
- It installs the magic methods in Sym{Bool,Int,Float}.
- N.b. With this split, we may be able to move Sym{Bool,Int,Float} to this file, and remove quite a few of the hacks around these classes
- Imports `sym_node` in `torch/__init__.py` rather than the whole `symbolic_shapes.py`.
This breaks the import-time dependency between torch and SymPy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112037
Approved by: https://github.com/peterbell10
ghstack dependencies: #112035, #112036
This PR:
- adds the pt2 compliant tag. This tag specifies that the operator works
with the PT2 compilation APIs. A custom op author should test their
ops with opcheck if they choose to add this tag.
- adds a config for Dynamo to allow only pt2 compliant ops into the
graph and graph break on all other OpOverload/OpOverloadPacket.
Bikeshedding help wanted on the name of the tag. It should be easily
grep-able so we can set up rules for it.
Test Plan:
- new tests
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111933
Approved by: https://github.com/ezyang
ghstack dependencies: #111912, #111915, #111948
This PR implements 2 things:
1. support the device agnostic stream and runtime APIs captured by the dynamo.
2. support the stream methods(include the event) captured by the dynamo.
Here are details for 1st.
Previously the stream captured in dynamo was tightly bind to CUDA. Here we implement a global singleton container named `StreamMethodContainer` for different backends to register their associated stream methods to dynamo. When import the backend’s product, the stream operations can be registered directly by calling
```
device_stream_method = {'current_stream': method_1,
'create_stream_context': method_2,
'set_stream': method_3,
'set_stream_by_id': method_4}
torch._dynamo.stream.register_stream_method(device_name, device_stream_method)
```
Stream methods need to be passed in this API according to the precise semantics represented by the dict key in `device_stream_method`. After register, these methods can be used by dynamo to capture the stream operations in users’ script, for example, get the current stream or set the specific stream. Additionally, the wrapped stream variable and the stream context variable are changed to be the device-agnostic, the proxy functions of these variables are assigned by the associated methods in the container. All of this are illustrated in the below. Below is a illustration.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108312
Approved by: https://github.com/jansel, https://github.com/jgong5
Triplet Margin Loss takes in a Callable `distance_function` parameter which is not supported as an argument on the fx graph. See previous error:
> File "/scratch/eellison/work/pytorch/torch/_dynamo/symbolic_convert.py", line 562, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/scratch/eellison/work/pytorch/torch/_dynamo/variables/torch.py", line 723, in call_function
*proxy_args_kwargs(args, kwargs),
File "/scratch/eellison/work/pytorch/torch/_dynamo/utils.py", line 504, in proxy_args_kwargs
f"call_function args: {typestr(*args)} {typestr(*list(kwargs.values()))}"
File "/scratch/eellison/work/pytorch/torch/_dynamo/exc.py", line 143, in unimplemented
raise Unsupported(msg)
torch._dynamo.exc.Unsupported: call_function args: TensorVariable() TensorVariable() TensorVariable() ConstantVariable(float) NNModuleVariable()
This is fixable by just inlining into `triplet_margin_loss` and continuing to compile it. This required support for `has_torch_function_variadic`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110302
Approved by: https://github.com/mlazos
Fix: #107315
This PR enables dynamo to trace through the `pytree` API by inlining its functions. In
order to do so, a few details of `pytree` had to be changed.
In summary, this PR:
- Introduces `TreeSpecVariable` for representing `TreeSpec` instances
- Specializes `<type>.__bases__` call, returning a `TupleVariable`
- Enables the call to `id` builtin function for every variable that implements
`as_python_constant` method
- Specializes `ConstantVariable.call_method` for its (un)flatten functions
- Implements `UserDefinedObjectVariable.as_python_constant`
- Modifies `pytree` by:
- Make `SUPPORTED_NODES` a map of ids (instead of types) to `NodeDef`
- Removed `functools.wraps` function, since it can't be inlined
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108533
Approved by: https://github.com/ezyang, https://github.com/voznesenskym
ghstack dependencies: #109201
I just ported the C++ torch.tensor implementation to Python, swapping out the inner bits to successively stack tensors together, so that we can trace through `scalar_tensor`.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109515
Approved by: https://github.com/voznesenskym
ghstack dependencies: #109513
RFC: https://github.com/pytorch/rfcs/pull/54
First commit is the contents of https://github.com/Quansight-Labs/numpy_pytorch_interop/
We have already been using this in core for the last few months as a external dependency. This PR pulls all these into core.
In the next commits, I do a number of things in this order
- Fix a few small issues
- Make the tests that this PR adds pass
- Bend backwards until lintrunner passes
- Remove the optional dependency on `torch_np` and simply rely on the upstreamed code
- Fix a number dynamo tests that were passing before (they were not tasting anything I think) and are not passing now.
Missing from this PR (but not blocking):
- Have a flag that deactivates tracing NumPy functions and simply breaks. There used to be one but after the merge stopped working and I removed it. @lezcano to investigate.
- https://github.com/pytorch/pytorch/pull/106431#issuecomment-1667079543. @voznesenskym to submit a fix after we merge.
All the tests in `tests/torch_np` take about 75s to run.
This was a work by @ev-br, @rgommers @honno and I. I did not create this PR via ghstack (which would have been convenient) as this is a collaboration, and ghstack doesn't allow for shared contributions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106211
Approved by: https://github.com/ezyang
This PR adds initial dynamo support for DTensor, in particular, it:
- allows DTensor be passed into a compiled function, and allow fakify
DTensor during dynamo tracing by turning the inner local tensor to meta
tensor.
- We use `allow_in_graph` to include `DTensor` and `DTensor.from_local` to be represented as `TorchVariable`
- The dtensor created becomes a normal `TensorVariable` and it would insert any tensor operations to the output graph just like torch.Tensor
- note that dtensor have a new instance method `redistribute` compare to plain tensor, and we currently special handle it in `TensorVariable`
`from_local` and `redistribute` both accepts some non-trival metadata as arguments (i.e. DeviceMesh, Placement) which fx.Graph does not support. In order to let these two APIs appear in the dynamo captured graph, we encoded the metadata into a new_function (like `functools.partial`) and the new function only accepts prim args (i.e. tensor), then we put `call_function` with this new_function to the graph. This is suggested by @ezyang. The underlying rationale here is that the metadata will not change across the graph invocations so it's safe to encode them.
Captured graph:
```
def forward(self, L_x_ : torch.Tensor):
l_x_ = L_x_
# File: /scratch/wanchaol/work/pytorch/test/distributed/_tensor/test_dtensor.py:685, code: dt = DTensor.from_local(x, mesh, [Shard(0)], run_check=False)
prim_from_local = torch__dynamo_variables_torch_prim_from_local(l_x_, run_check = False); l_x_ = None
# File: /scratch/wanchaol/work/pytorch/test/distributed/_tensor/test_dtensor.py:686, code: return dt.redistribute(mesh, [Replicate()]).to_local() + 2
prim_redistribute = torch__dynamo_variables_tensor_prim_redistribute(prim_from_local); prim_from_local = None
to_local = prim_redistribute.to_local(); prim_redistribute = None
add = to_local + 2; to_local = None
return (add,)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103146
Approved by: https://github.com/voznesenskym
torch.profiler.record_function and torch.profiler.profile are ignored by dynamo. In the common case, users have `record_function` in the middle of their program in order to annotate a section of the profile.
The previous error message was `Profiler will be ignored`. Users would think that profiling would be completely ignored.
Now the message will look like `Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105362
Approved by: https://github.com/yanboliang, https://github.com/aaronenyeshi
