A big pain point ppl have with custom ops is that they do not accept arbitrary input/outputs. In this PR we create the concept of an "OpaqueObject" which allows users to pass arbitrary python objects into custom operators.
Some still slightly annoying parts with this implementation:
- The schema of the operator is `__torch__.torch.classes.aten.OpaqueObject` instead of whatever python type
- `@torch.library.custom_op` doesn't work.. yet?
UX:
```python
from torch._library.opaque_object import make_opaque, get_payload
# your custom python class
class OpaqueQueue:
def __init__(self, queue: list[torch.Tensor], init_tensor_: torch.Tensor) -> None:
super().__init__()
self.queue = queue
self.init_tensor_ = init_tensor_
def push(self, tensor: torch.Tensor) -> None:
self.queue.append(tensor)
def pop(self) -> torch.Tensor:
if len(self.queue) > 0:
return self.queue.pop(0)
return self.init_tensor_
def size(self) -> int:
return len(self.queue)
queue = OpaqueQueue([], torch.zeros(3))
obj: torch._C.ScriptObject = make_opaque(queue)
# obj.payload stores a direct reference to this python queue object
self.assertEqual(get_payload(obj), queue)
# This is able to be passed through the dispatcher
torch.ops._TestOpaqueObject.queue_push(obj, torch.ones(3))
self.assertTrue(queue.size(), 1)
```
Authoring a custom op:
```python
lib = torch.library.Library("_TestOpaqueObject", "FRAGMENT")
torch.library.define(
f"_TestOpaqueObject::queue_push",
"(__torch__.torch.classes.aten.OpaqueObject a, Tensor b) -> ()",
tags=torch.Tag.pt2_compliant_tag,
lib=lib,
)
@torch.library.impl(f"{libname}::queue_push", "CompositeExplicitAutograd", lib=lib)
def push_impl(q: torch._C.ScriptObject, b: torch.Tensor) -> None:
# We can get the payload directly by get_payload(q)
queue = get_payload(q)
assert isinstance(queue, OpaqueQueue)
queue.push(b)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162660
Approved by: https://github.com/zou3519
This PR fixes a bug where user defined triton kernels hidden behind `triton_op` do not register source code changes. If a user *only* changes a triton kernel source_code, because triton kernels are hidden under the custom op, dynamo hasn't traced into them yet.
This means at AOTAutograd time, we don't know the list of triton kernels that are defined by custom ops. This is an initial fix for the issue by parsing the AST of the custom op looking for triton kernels. This won't catch more degenerate cases if the custom op calls other custom ops/functions that then call triton kernels, and then the toplevel compiled graph doesn't know about it. To handle that, we'd have to trace through the custom op at dynamo time.
This should handle 99% of cases, though. I added an expectedFailure test to show the limitation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160120
Approved by: https://github.com/zou3519
Short-term fix for https://github.com/pytorch/pytorch/issues/160333
The problem is:
1) `triton_op` adds a decomposition for FunctionalTensorMode for this operation
2) Tensor Subclasses rely on FunctionalTensorMode's `__torch_dispatch__` returning NotImplemented.
3) `triton_op`'s FunctionalTensorMode decomposition takes precedence over FunctionalTensorMode's decomposition.
The easy fix is to copy-paste the FunctionalTensorMode's NotImplemented
return logic into the decomposition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160341
Approved by: https://github.com/drisspg
scaled_grouped_mm's kernel only supports column-major on the second operand. I -think- this is just for efficiency reasons. But inductor treats that buffer as flexible and may tweak the strides to be row-major instead, as seen in the issue.
~Tagging the op as "needs_fixed_stride_order"/"needs_exact_strides" does not work. Inductor only considers those tags for ops that don't have registered lowering (not sure if this is intended). scaled_grouped_mm does have a lowering, so we never check its tags.~ From discussion below, the op tags are expected to work.
FIXES https://github.com/pytorch/pytorch/issues/159097
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159134
Approved by: https://github.com/eellison
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
This PR:
- cleans up some existing comments that don't make sense anymore
- hooks up the "custom_op_default_layout_constraint" back (that seems to
have broken)
- cleans up the "lazy registration path" which seems to never get hit
anymore
- adds dislike_padding to nodes that require exact strides
Test Plan:
- tests + CI
disable padding
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148104
Approved by: https://github.com/shunting314, https://github.com/eellison
Summary: Previously, when attr is defined, "if attr" will try to evaluate the data of attr, which is not intendended and we get a ugly error stack if the attr is not evaluable (like a fake tensor) before the callable(attr) check.
Test Plan: Existing tests.
Reviewed By: yushangdi, henryoier
Differential Revision: D73460905
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151944
Approved by: https://github.com/yushangdi
Added a context manager, `torch._library.fake_profile.register_fake_profile(op_profiles)`, where given an operator profile, it will generate and register a fake impl for the operator based on the operator profile.
The input to `register_fake_profile` is a dictionary mapping operator name to a set of profiles which describe the input and outputs of the operator. Here's an example of a profile for `mylib.foo.default`:
```
"mylib.foo.default": {
OpProfile(
args_profile=(
TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
),
out_profile=TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
)
}
```
`foo`'s profile contains only one profile, which says that for 2 input tensors of rank 2, dtype float32, device cpu, we will return one tensor of rank 2, dtype float32, and device cpu.
This will then generate a fake kernel where given 2 input tensors of rank 2 (and the other tensor metadata), we will output one tensor of rank 2 (and the other tensor metadata). If the operator also supports other input ranks, then we can add to the profile for the fake impl to support more input types.
This profile can either be manually written or created by draft-export, and then checked into the codebase.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150807
Approved by: https://github.com/zou3519
ghstack dependencies: #150806
Added a flag, `allow_override`, to allow overriding existing kernel implementations in `torch.library.register_fake` `library.impl`. The default is false, where if a user tries to register a kernel to a dispatch key that already contains a kernel, it will error. This flag doesn't apply to CustomOpDefs, where overriding a fake kernel is already allowed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150806
Approved by: https://github.com/zou3519
# Fix typo errors across PyTorch codebase
This PR fixes various spelling errors throughout the PyTorch codebase to improve documentation quality and code readability.
## Changes Made
### Documentation Fixes
- Changed "seperate" to "separate" in multiple files:
- `setup.py`: Build system documentation
- `torch/_library/triton.py`: AOT compilation comments
- `torch/csrc/dynamo/compiled_autograd.h`: Node compilation documentation
- `torch/export/_unlift.py`: Pass population comments
- `torch/export/exported_program.py`: Decomposition table notes
### Code Comments and Error Messages
- Changed "occured" to "occurred" in:
- `test/mobile/test_lite_script_module.py`: Exception handling comments
- `torch/export/_draft_export.py`: Error message text
- `aten/src/ATen/native/cuda/linalg/BatchLinearAlgebra.cpp`: MAGMA bug comment
- `torch/csrc/utils/python_numbers.h`: Overflow handling comment
- `torch/csrc/jit/OVERVIEW.md`: Graph compilation documentation
- `torch/_dynamo/symbolic_convert.py`: Error explanation
### API Documentation
- Changed "fullfill" to "fulfill" in `torch/distributed/checkpoint/state_dict_loader.py`
- Changed "accross" to "across" in:
- `torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp`
- `torch/distributed/distributed_c10d.py`
## Motivation
These changes improve code readability and maintain consistent spelling throughout the codebase. No functional changes were made; this is purely a documentation and comment improvement PR.
## Test Plan
No testing required as these changes only affect comments and documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148262
Approved by: https://github.com/janeyx99
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
Summary:
This pr add a _is_script_object method to differentiate scriptModule and scriptObject, where the formal inherits from ScriptObject in C++ so they both passes the isinstance(obj, torch.ScriptObject) check.
The qualified name of ScriptObject (i.e. custom class) would starts with "__torch__.torch.classes", this has been a widely used assumption for dealing with custom class across our code base.
Test Plan: Add new test.
Differential Revision: D69685316
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147399
Approved by: https://github.com/yushangdi
