Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72390
This class didn't add much value and only caused more boilerplate code.
This change removes the class and updates all the use cases with
uses of `ExprHandle`.
A side effect of this change is different names in loop variables, which
caused massive mechanical changes in our tests.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D34030296
Pulled By: ZolotukhinM
fbshipit-source-id: 2ba4e313506a43ab129a10d99e72b638b7d40108
(cherry picked from commit c2ec46a0587cafd4e915c5bf1e0dc0b5d244e8d5)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68858
when executing with ir_eval, check for index out of bounds.
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D32657881
Pulled By: davidberard98
fbshipit-source-id: 62dd0f85bb182b34e9c9f795ff761081290f6922
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66612
For op authoring project, we want to expose the python bindings
to create Expr. These are the missing bindings.
Test Plan: Imported from OSS
Reviewed By: soulitzer
Differential Revision: D31667852
fbshipit-source-id: 6d3ff83a7676cfea391ab3ea60dde6874a64047a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64828
Also, make `removeUnusedSelfArgument` more consistent with other passes
by mutating the graph in-place rather than returning a copy.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D30870776
Pulled By: ZolotukhinM
fbshipit-source-id: 4873f01b013921143a5aa43746d655a2d8d620c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63587
Now that there is no classes using KernelArena for memory management we
can remove it.
Differential Revision:
D30429115
D30429115
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 375f6f9294d27790645eeb7cb5a8e87047a57544
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63216
Currently there are three classes managed by KernelArena: Expr, Stmt,
and Tensor (and derived classes). KernelArena has been a long standing
painpoint for NNC devs and we're moving away from that memory management
model to ref-count based memory model (using shared_ptr). This commit
switches Expr and Stmt to shared_ptr and is the biggest change in this
transition. Later commits will detach Tensor from KernelArena and kill
the arena + scope altogether.
Differential Revision:
D30353195
D30353195
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 9575225ada3d0fb65087ae40435f3dfea4792cae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61725
Alloc/free inside a loop isn't really an optimization, and furthermore
it breaks some attempted optimization in the llvm backend: we use alloca for
small allocations, which is efficient since alloca is on the stack, but there's
no corresponding free, so we leak tons of stack. I hit this while building an
rfactor buffer inside a very deeply nested loop.
ghstack-source-id: 133627310
Test Plan:
Unit test which simulates use of a temp buffer in a deeply nested
loop.
Reviewed By: navahgar
Differential Revision: D29533364
fbshipit-source-id: c321f4cb05304cfb9146afe32edc4567b623412e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61385
The bindings coverage might be not full yet, but this already allows us
to register custom lowerings from python.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D29623487
Pulled By: ZolotukhinM
fbshipit-source-id: b97ee420a57fd887e204c021b9e098764b2ee232
Summary:
A few more quality of life improvements for NNC's python bindings:
- Use standard `torch.dtype`s (rather than `te.Dtype`)
- Make names optional (they don't seem to matter)
- Make shapes optional
- A few implicit conversions to make code cleaner
Followup to https://github.com/pytorch/pytorch/issues/59920
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60058
Reviewed By: bertmaher
Differential Revision: D29151953
Pulled By: jansel
fbshipit-source-id: c8286e329eb4ee3921ca0786e17248cf6a898bd8
Summary:
Some minor quality of life improvements for the NNC python bindings:
- expose `call_raw()`
- support passing integers to `call()` (for dynamic shapes)
- implicit conversions to cleanup `[BufferArg(x) for x in [A, B, C]]` into just `[A, B, C]`
- don't silently default to "ir_eval" for unknown mode (e.g. "LLVM")
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59920
Reviewed By: ZolotukhinM
Differential Revision: D29090904
Pulled By: jansel
fbshipit-source-id: 154ace82725ae2046cfe2e6eb324fd37f5d209a7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57169
The pass is planned to be used in AOT pipeline, where we expect input
graphs to be functional. As such, these graphs should not use 'self'
argument even if it is present, and thus it can be remove safely.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D28128328
Pulled By: ZolotukhinM
fbshipit-source-id: a7dfbf7776682826100c8eb0fef982a2e81c2554
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57076
This pass is intended to be used in conjunction with shape propagation
pass: first we use sample inputs to specify shape info for graph inputs
and then we run shape-prop to infer shapes of intermediate values in the
graph.
Differential Revision: D28048290
Test Plan: Imported from OSS
Reviewed By: astaff
Pulled By: ZolotukhinM
fbshipit-source-id: 778d772e873d59d77af9f669f45dc44b9ee5e443
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53063
The problem was that a derived class was marked with "py::nodelete",
while the base class wasn't. Now they both are marked correctly.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D26737877
Pulled By: ZolotukhinM
fbshipit-source-id: 17d9d430651c8f695fc7b6bf6784e7719e20a4d2
Summary:
As discussed with suo , having it in `torch._C.XX` means that it automatically gets added to `torch.XX` which is unfortunate. Making it `torch._C._XX` means that it won't be added to `torch.`.
Let me know if that approach to hide it is not good and we can update that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51690
Reviewed By: gchanan
Differential Revision: D26243207
Pulled By: albanD
fbshipit-source-id: 3eb91a96635e90a6b98df799e3a732833dd280d5
Summary:
Relate to https://github.com/pytorch/pytorch/issues/50483.
Everything except ONNX, detectron and release notes tests are moved to use common_utils.run_tests() to ensure CI reports XML correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50923
Reviewed By: samestep
Differential Revision: D26027621
Pulled By: walterddr
fbshipit-source-id: b04c03f10d1fe96181b720c4c3868e86e4c6281a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49698
Reincarnation of #47620 by jamesr66a.
It's just an initial bunch of things that we're exposing to python, more
is expected to come in future. Some things can probably be done better,
but I'm putting this out anyway, since some other people were interested
in using and/or developing this.
Differential Revision: D25668694
Test Plan: Imported from OSS
Reviewed By: bertmaher
Pulled By: ZolotukhinM
fbshipit-source-id: fb0fd1b31e851ef9ab724686b9ac2d172fa4905a
