Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52658
DCE will reverse iterate over the graph looking for nodes without users and delete them. It will skip over unused placeholders (since this affects the signature of the method) and outputs (which never have users but we want to keep them :) )
Test Plan: Added unit tests
Reviewed By: jamesr66a, khabinov, chenccfb
Differential Revision: D26602212
fbshipit-source-id: f4f196973e40546076636090bb0008c24f33795e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51974
Right now, when an FX `Graph` references an external object, we will emit
code like:
import foo
def forward(input: foo.bar.baz):
...
This is problematic in a world with `torch.package`, since then name
`foo.bar.baz` may reference a name from any number of packages.
This PR lays the groundwork for FX-package integration by separating the
resolution of external references from the genration of the function
code.
When generating a Graph's Python source, we keep track of all external
references and assign them unique names. At the end, we have a
dictionary mapping names -> actual objects. This becomes the `globals`
namespace we pass to `exec` when installing the forward function in a
`GraphModule`. This is nice because we can always be sure that `exec` is
seeing the same objects that were referenced from the `Graph`, no import
statements needed.
At serialization time, we use a `ModuleEnv` to resolve the globals dict
to a set of import statements that can be run to reprodce the `global`
namespace. This is only used on serialiation/deserialization, and those
functions are expected to check that the import statements are producing
the correct results.
Concretely, the code above will now look like:
from foo.bar import baz as foo_bar_baz
def forward(input: foo_bar_baz):
...
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D26340593
Pulled By: suo
fbshipit-source-id: fe247f75205d0a03fd067bdd0f95491e8edf1436
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46325
Otherwise, mutating them would make the uses/users lists inaccurate.
You can still mutate the node by assigning a new value to .args or .kwargs
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D24308672
Pulled By: zdevito
fbshipit-source-id: a5305e1d82668b36e46876c3bc517f6f1d03dd78
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46121
Otherwise, mutating them would make the uses/users lists inaccurate.
You can still mutate the node by assigning a new value to .args or .kwargs
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D24232288
Pulled By: zdevito
fbshipit-source-id: c95b1a73ae55ad9bdb922ca960c8f744ff732100
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45708
This makes it possible to define reasonable semantics for what happens
when a node in the list is deleted. In particular the iteration over nodes
will continue at the node that was after the deleted node _when it was deleted_.
If the new node is also deleted, we skip it and, continue to the node after it.
Eventually we either reach a node still in the list or we reach the end of the list.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D24089516
Pulled By: zdevito
fbshipit-source-id: d01312d11fe381c8d910a83a08582a2219f47dda
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43083
This adds type annotations to all classes, arguments, and returns
for fx. This should make it easier to understand the code, and
encourage users of the library to also write typed code.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D23145853
Pulled By: zdevito
fbshipit-source-id: 648d91df3f9620578c1c51408003cd5152e34514
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
