Summary:
This PR adds a new GraphManipulation library for operating on the GraphModule nodes.
It also adds an implementation of replace_target_nodes_with, which replaces all nodes in the GraphModule or a specific op/target with a new specified op/target. An example use of this function would be replacing a generic operator with an optimized operator for specific sizes and shapes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44775
Reviewed By: jamesr66a
Differential Revision: D23874561
Pulled By: gcatron
fbshipit-source-id: e1497cd11e0bbbf1fabdf137d65c746248998e0b
Summary:
This PR adds get_all_users_of function. The function returns all the users of a specific node. A test unit is also added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45216
Reviewed By: ezyang
Differential Revision: D23883572
Pulled By: scottxu0730
fbshipit-source-id: 3eb68a411c3c6db39ed2506c9cb7bb7337520ee4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45188
This is a symbolically traceable alternative to Python's `assert`.
It should be useful to allow people who want to use FX to also
be able to assert things.
A bunch of TODO(before) land are inline - would love thoughts
on where is the best place for this code to live, and what this
function should be called (since `assert` is reserved).
Test Plan:
```
python test/test_fx.py TestFX.test_symbolic_trace_assert
```
Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23861567
fbshipit-source-id: d9d6b9556140faccc0290eba1fabea401d7850de
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44795
Today, we build our cpp tests twice, once as a standalone gtest binary,
and once linked in `libtorch_python` so we can call them from
`test_jit.py`.
This is convenient (it means that `test_jit.py` is a single entry point
for all our tests), but has a few drawbacks:
1. We can't actually use the gtest APIs, since we don't link gtest into
`libtorch_python`. We're stuck with the subset that we want to write
polyfills for, and an awkward registration scheme where you have to
write a test then include it in `tests.h`).
2. More seriously, we register custom operators and classes in these
tests. In a world where we may be linking many `libtorch_python`s, this
has a tendency to cause errors with `libtorch`.
So now, only tests that explicitly require cooperation with Python are
built into `libtorch_python`. The rest are built into
`build/bin/test_jit`.
There are tests which require that we define custom classes and
operators. In these cases, I've built thm into separate `.so`s that we
call `torch.ops.load_library()` on.
Test Plan: Imported from OSS
Reviewed By: SplitInfinity, ZolotukhinM
Differential Revision: D23735520
Pulled By: suo
fbshipit-source-id: d146bf4e7eb908afa6f96b394e4d395d63ad72ff
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44566
The Delegate objects were confusing. They were suppose to be a way to
configure how tracing works, but in some cases they appeared necessary
for consturcting graphs, which was not true. This makes the organization
clearer by removing Delgate and moving its functionality into a Tracer class,
similar to how pickle has a Pickler class.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23683177
Pulled By: zdevito
fbshipit-source-id: 7605a34e65dfac9a487c0bada39a23ca1327ab00
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44092
instead submodules and weights are installed directly on the
graph_module by transferring the original modules. This makes it more
likely that scripting will succeed (since we no longer have submodules
that are not used in the trace). It also prevents layered transforms
from having to special case handling of the `root` module. GraphModules
can now be re-traced as part of the input to other transforms.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23504210
Pulled By: zdevito
fbshipit-source-id: f79e5c4cbfc52eb0ffb5d6ed89b37ce35a7dc467
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43248
We add the support of __torch_function__ override for C++ custom op. The logic is the same as the other components, like torch.nn.Module.
Refactored some code a little bit to make it reusable.
Test Plan: buck test //caffe2/test:fx -- test_torch_custom_ops
Reviewed By: bradleyhd
Differential Revision: D23203204
fbshipit-source-id: c462a86e407e46c777171da32d7a40860acf061e
Summary:
It's useful if we add additional attributed to nodes in the graph - it's easier to set the attribute on all nodes, even if the value would happen to be None.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43432
Reviewed By: jamesr66a
Differential Revision: D23276433
Pulled By: dzhulgakov
fbshipit-source-id: c69e7cb723bbbb4dba3b508a3d6c0e456fe610df
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43653
When nodes are created without an explicit name, a name is generated for
it based on the target. In these cases, we need to avoid shadowing
builtin names. Otherwise, code like:
```
a.foo.bar
```
results in pretty-printed code like:
```
getattr = a.foo
getattr_1 = getattr.bar
```
While this is technically allowed in Python, it's probably a bad idea,
and more importantly is not supported by TorchScript (where `getattr` is
hardcoded).
This PR changes the name generation logic to avoid shadowing all
builtins and langauge keywords. We already do this for PyTorch
built-ins, so just extend that logic. So now the generated code will
look like:
```
getattr_1 = a.foo
getattr_2 = getattr_1.bar
```
Fixes#43522
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23357420
Pulled By: suo
fbshipit-source-id: 91e9974adc22987eca6007a2af4fb4fe67f192a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43640
+ added a `self.checkGraphModule` utility function to wrap the common
test assert pattern.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D23356262
Pulled By: suo
fbshipit-source-id: a50626dcb01246d0dbd442204a8db5958cae23ab
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43082
Fixes all present errors in mypy. Does not try to add annotations everywhere.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23145854
Pulled By: zdevito
fbshipit-source-id: 18e483ed605e89ed8125971e84da1a83128765b7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42991
Have Node both be a record of the operator in the graph, and the
way we _build_ the graph made it difficult to keep the IR datastructure
separate from the proxying logic in the build.
Among other issues this means that typos when using nodes would add
things to the graph:
```
for node in graph.nodes:
node.grph # does not error, returns an node.Attribute object!
```
This separates the builder into a Proxy object. Graph/Node no longer
need to understand `delegate` objects since they are now just pure IR.
This separates the `symbolic_trace` (proxy.py/symbolic_trace.py) from
the IR (node.py, graph.py).
This also allows us to add `create_arg` to the delegate object,
allowing the customization of how aggregate arguments are handled
when converting to a graph.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D23099786
Pulled By: zdevito
fbshipit-source-id: 6f207a8c237e5eb2f326b63b0d702c3ebcb254e4
