PassManager is a class used to run multiple passes on a given graph module.
Class Attributes
* `passes: List[Callable]`: A list of callable passes
* `constraints: List[Callable]`: A list of constraints
* `run_checks_after_each_pass`: Flag for running checks each pass
Class Methods:
* `__call__(graph_module: DispatchGraphModule)`:
* Runs the passes based on the list of passes until the graph stops changes, or until `steps` number of times.
* Each time a pass is run, it will check that the graph module still maintains the required invariants by calling `check()` and will lint the graph to check that it’s well formed if the flag `run_checks_after_each_pass` is set.
* `check(graph_module: DispatchGraphModule)`: Runs various checks on the given graph module to make sure that it contains the needed data for passes
* `add_check(check: Callable)`: Adds the `check` function to the given pass manager instance
* `add_constraint(constraint: Callable)`: Adds a constraint to the current list of constraints
We can create a PassManager and run it by doing:
```
PassManager(passes=[pass1, pass2])(graph_module)
```
Differential Revision: [D37523159](https://our.internmc.facebook.com/intern/diff/D37523159)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80531
Approved by: https://github.com/SherlockNoMad
There are small typos in:
- caffe2/python/recurrent.py
- test/distributed/test_c10d_nccl.py
- test/test_fx.py
- torch/csrc/jit/runtime/autodiff.cpp
- torchgen/gen.py
Fixes:
- Should read `propagation` rather than `propogation`.
- Should read `multiplied` rather than `multuplied`.
- Should read `eliminate` rather than `elminate`.
- Should read `dispatcher` rather than `disaptcher`.
Semi-automated pull request generated by
https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81435
Approved by: https://github.com/ngimel
If installed with pep517 support, `torchvision` will be build againstreleased version of PyTorch rather than against the one currently installed on the system
Also update `torchvision` hash to 8a45147f9d and:
- Added `maskrcnn_resnet50_fpn_v2`, `maskrcnn_resnet50_fpn_v2`, `retinanet_resnet50_fpn_v2`, `ssd300_vgg16`, `fcos_resnet50_fpn` and `ssdlite320_mobilenet_v3_large` to the list of untraceable models
- Set default input size to (1, 3, 16, 224, 224) for `mvit_v1_b` model
- Skipped `test_roi_aligned`,`test_batched_nms`, `test_roi_pooled` and `test_roi_align_aligned` ONNX test (tracked in https://github.com/pytorch/pytorch/issues/81121 )
- Skipped TorchVision integration tests in `test_package` (tracked in https://github.com/pytorch/pytorch/issues/81115 )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81074
Approved by: https://github.com/kit1980
Summary: The root module may have different forward functions. The current implementation assumes only the func forward can be traced. In this PR, we add an attribute func name to Tracer class to enable users trace different functions
Test Plan:
python3 test/test_fx.py TestFX.test_trace_multiple_funcs
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77502
Approved by: https://github.com/jamesr66a
Summary: The root module may have different forward functions. The current implementation assumes only the func `forward` can be traced. In this diff, we add an argument of forward func name to enable users trace different forward functions
Test Plan: N1903198
Differential Revision: D36157032
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77109
Approved by: https://github.com/jamesr66a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76253
We're observing large QPS regression on the original PR https://github.com/pytorch/pytorch/pull/72302. For the training job we had, it regressed from 720k QPS to 450k QPS (see the test plan in FB internal). We suspect this is because the api was changed from `_record_function_enter` to `_record_function_enter_new`, and we're running experiments to confirm that. Will add more details when the runs in the test plan has finished. For now, it's better to revert the diff to unblock internal usecases and we can think about how to reland this diff later.
Original commit changeset: dc9939f1fa6d
Original Phabricator Diff: D35257354
Test Plan:
on trunk: f338665947
with this diff: f338502850
Reviewed By: malfet, robieta
Differential Revision: D35853300
fbshipit-source-id: dd38042aeacb848f66756491a4c849c7c652a0e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74662
Previously, we would not emit a check that `concrete_args` with value `None` matched that value during runtime. This fixes that and improves some of the warning messages
Test Plan: Imported from OSS
Reviewed By: Chillee
Differential Revision: D35137362
Pulled By: jamesr66a
fbshipit-source-id: 222a2c8a907748f90290f1c1b4ab8012b46099a0
(cherry picked from commit b960405ad87e57dcf62ca25dd4d4bdfc34c8744c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74637
Forgot to update the expect file in https://github.com/pytorch/pytorch/pull/74242. Reland to include changes in expect file.
Test Plan: unit test
Reviewed By: yinghai
Differential Revision: D35089989
fbshipit-source-id: 5e3ad9c696cf31cbc691d34fdb77eff26f92e38d
(cherry picked from commit 110ac12f5e2bcca7552d4b4691c7d98fafb21a57)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74242
The inputs and outputs of the graph module might be different from the graph inputs and outputs if users are using custom codegen. In interpreter, it runs the graph instead of the generated forward function so it might not work if user provides the inputs to the graph module. To fill the gap, we call `process_inputs` and `process_outputs` inside interpreter.
Test Plan: unit test: test_interpreter_with_codegen
Reviewed By: jamesr66a, Chillee
Differential Revision: D34898108
fbshipit-source-id: 250bd236f6c8c1268a363cf19a09521a4f64b3a9
(cherry picked from commit b33076fa3b10788d455cecc590bc01c4ad8ef94c)
Summary:
The fx test case wasn't disabled properly because it didn't call the parent class' setUp().
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74216
Reviewed By: zou3519
Differential Revision: D34898707
Pulled By: janeyx99
fbshipit-source-id: 83e56f5a1efc50d24646c182160f7cfcb5bc9935
(cherry picked from commit bb8dd72d1640c1ef0201d615c5d405479afdf078)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74189
Use the codegen on the original graph module for the new graph module produced by transformer.
Test Plan: Added a unit test: test_custom_codegen_with_transformer
Reviewed By: yinghai
Differential Revision: D34867938
fbshipit-source-id: fcda6600faeccfa7a650ba7226ca125e8440b19c
(cherry picked from commit d098c12081f61ddcf69052db5b8a1f31b0a0b67b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73763
The test that is enabled generates a graph as such:
```
linear_25 --> sigmoid_14 --> output_1
\--> output_2
```
Before this diff, (unpadding) layout_transform nodes would be added as follows:
```
linear_25 --> layout_xform1 --> sigmoid_14 --> layout_xform2--> output_1
\--> output_2
```
This causes an assertion to fail for the sigmoid node where the input and output types
don't match due to padding differences.
This diff modifies the replacement algorithm to not affect users of an output's parent node
when the user requires padded inputs. This yields the following graph instead:
```
linear_25 --> sigmoid_14 --> layout_xform2--> output_1
\--> layout_xform1 --> output_2
```
Test Plan: Manually and CI
Reviewed By: jfix71, dborkovic
Differential Revision: D34623590
fbshipit-source-id: 3834b06c95fc5626eccc282216cbe039ac5a3242
(cherry picked from commit af012372ae1a6bb654b0ed9b765993960d5251e4)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73198
Previously, if an arg to an FX node is a subclass of tuple then it gets sanitized essentially back to that base class. An example here is when setting an arg to be a TensorMetadata object, which is a NamedTuple, it will be set as a tuple instead.
- Change `map_aggregate` to repack the tuple to `type(a)` when it's not directly a tuple (try/except for best attempt)
- During codegen, call `add_global` for `type(a)` if it's not directly a tuple.
- Add an option for an arg to provide a `_custom_fx_repr_fn` for use inside stringifying via `_format_arg`
Test Plan: Added unit test coverage, where we inline the named tuple into arg/kwarg.
Reviewed By: jamesr66a
Differential Revision: D34381888
fbshipit-source-id: bd672a8542e2bba5aa604b448bec920efc256440
(cherry picked from commit 68f99c12dd)
Summary:
The goal of this is to make FX's codegen extensible. I've refactored it into a class with 5 extensibility points on it.
```
class Codegen(object):
def generate_prologue(self, free_vars: List[str], maybe_return_annotation: str) -> str:
"""
Given the free variables and a return annotation, generates the beginning of the FX function.
By default, `generate_prologue(['a', 'b'], '') == 'def forward(a, b):'`
"""
def generate_output(self, output_args: Argument) -> str:
"""
Given the output arguments, generates the return statement of the FX function.
"""
def process_inputs(self, args: Any) -> Any:
"""
Transforms the inputs so that the graph can take them as arguments, as
non-default codegen may result in the inputs to the function being
different from the inputs to the graph.
If the graph was directly runnable, this invariant should hold true
`f.process_outputs(f.graph(*f.process_inputs(*inputs))) == f(*inputs)`
"""
def process_outputs(self, outputs: Any) -> Any:
"""
Transforms the outputs of the graph to be identical to the codegen.
See ``process_inputs`` for more details.
"""
def additional_globals(self) -> List[Tuple[str, Any]]:
"""
If your codegen uses extra global values, add them here.
For example, return ['List', typing.List] if you need ``List`` in the global context.
"""
```
So, for example, the `ListCodeGen` we want for AOTAutograd looks like this
```
class ListCodeGen(CodeGen):
def generate_prologue(self, free_vars, maybe_return_annotation):
lst_unpack = f"""
def forward(self, args_list: List[torch.Tensor]){maybe_return_annotation}:
{', '.join(free_vars)} = args_list"""
return lst_unpack
def additional_globals(self):
return [('List', typing.List)]
def process_inputs(self, *inputs):
assert(len(inputs) == 1)
return inputs[0]
```
and
```
def f(a, b):
return a + b
nf = fx.symbolic_trace(f)
nf.graph.set_codegen(ListCodeGen())
nf.recompile()
print(nf.code)
```
would result in
```
def forward(self, args_list: List[torch.Tensor]):
a, b = args_list
add = a + b; a = b = None
return add
```
Backwards compatibility changes - I added `process_outputs` and `process_inputs` to `fx.Graph`, while removing `flatten_inputs` and `flatten_outputs` - those didn't have `backwards_compatibility` on them, so I *think* it's probably fine?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72566
Reviewed By: desertfire
Differential Revision: D34160424
Pulled By: Chillee
fbshipit-source-id: ebf6411312b373e3fbcb13288a34befa449a2375
(cherry picked from commit 13cd12eaa1)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61608
See #61544 for an example of issues created by functional wrappers. In this
case, these are directly wrapping the native function with no added
functionality. One exception was `bilinear` which was just missing the default
argument in C++, but was otherwise the same.
I've kept the symbol `torch.functional.istft` because it looks like public API,
but it could just as easily be moved to `_torch_docs.py`.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D31401361
Pulled By: albanD
fbshipit-source-id: 162b74d0b2d4f2e5c4834687a94541960cefdd52
(cherry picked from commit 700cd73ca1)
