We allow registering decomps for HigherOrderOp via the existing decomp
mechanisms:
- I refactored those APIs to accept torch._ops.OperatorBase, which is the base
class for torch.ops.HigherOrderOperator and torch.ops.OpOverload
- HigherOrderOps must directly call maybe_handle_decomp in their
ProxyTorchDispatchMode handling in order to resolve decompositions. We
can change this in the future so that they do not need to do this.
Next, we add an inductor decomp for out_dtype. This decomp shouldn't be
generally available because we want to preserve out_dtype to the backend
for other use cases (i.e. executorch).
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108080
Approved by: https://github.com/HDCharles
This adds an expect-test that finds the set of core ATen operators by
subtracting the operators with decomposition in core_aten_decompositions from the
set of all operators that have decompositions and could be decomposed.
This is useful because if you add a new decomposition but forget to add it to
the list of core decompositions, it will appear in the PR diff.
Also, by going through this list I have identified some operators where the
functional variant is decomposed, but not the inplace variant which must be an
oversight.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104262
Approved by: https://github.com/lezcano
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Using the same repro from the issue (but with BatchNorm2D)
Rectifies native_batch_norm schema by splitting the schema into 2:
1. one will have NON-optional alias-able running_mean and running_var inputs
2. the other will just not have those parameters at all (no_stats variation)
**Calling for name suggestions!**
## test plan
I've added tests in test_functionalization.py as well as an entry in common_method_invocations.py for `native_batch_norm_legit`
CI should pass.
## next steps
Because of bc/fc reasons, we reroute native_batch_norm to call our new schemas ONLY through the python dispatcher, but in 2 weeks or so, we should make `native_batch_norm_legit` the official batch_norm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88697
Approved by: https://github.com/albanD
This is an interesting one
Since this is an operation that's intrinsically defined on the reals,
we should perform the ops on that dtype always, and just cast to
the desired dtype at the end. This simplifies the decomposition.
Now, I started looking at this one when I started seeing failures on a
test that's added in a later PR. What's going on here is that, by doing
an upcast to a higher dtype and then cast down to integers, sometimes
there's an off-by-one error. I think this is fine, as the decomposition
is more accurate than the original function, which goes in line with
the whole PrimTorch effort.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87203
Approved by: https://github.com/mruberry
Fixes: https://github.com/pytorch/pytorch/issues/88010
This PR does a couple things to stop slow gradcheck from timing out:
- Splits out test_ops_fwd_gradients from test_ops_gradients, and factors out TestFwdGradients and TestBwdGradients which both inherit from TestGradients, now situated in common_utils (maybe there is a better place?)
- Skips CompositeCompliance (and several other test files) for slow gradcheck CI since they do not use gradcheck
- because test times for test_ops_fwd_gradients and test_ops_gradients are either unknown or wrong, we hardcode them for now to prevent them from being put together. We can undo the hack after we see actual test times are updated. ("def calculate_shards" randomly divides tests with unknown test times in a round-robin fashion.)
- Updates references to test_ops_gradients and TestGradients
- Test files that are skipped for slow gradcheck CI are now centrally located in in run_tests.py, this reduces how fine-grained we can be with the skips, so for some skips (one so far) we still use the old skipping mechanism, e.g. for test_mps
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88216
Approved by: https://github.com/albanD
`diag` was unnecessarily implemented as a kernel rather than as a composite
function, which made it unnecessarily difficult (explicit backward + all it entails).
We also change a few uses of `diag` on 2D tensors for `diagonal()`. The
latter returns a view rather than creating a new tensor.
We also upgrade its meta implementation to a fully-fledged
decomposition
I tried implementing the backwards of `diagonal()` via `diag_scatter` (or better `diag_scatter_` to keep the perf) but functionalisation was failing and I was not sure how to fix this, so I moved on. It may be possible to simplify that one as well if @soulitzer or someone knows how to do this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87180
Approved by: https://github.com/ngimel, https://github.com/albanD, https://github.com/mruberry
