Summary:
Move check_no_missing_dump_files to after the "just print" location.
This allows us to print dump_files when there are actual missing files.
Test Plan:
```
torchfrtrace -j ~/pyper-training-online-924394600 --selected-ranks 1 2
Inferred common prefix nccl_trace_rank_
loaded 95 files in 0.040270328521728516s
built groups, memberships
Rank 1 Rank 2
------------------------------------------------------------------ ------------------------------------------------------------------
broadcast(input_sizes=[[2]], state=completed) broadcast(input_sizes=[[2]], state=completed)
```
Without this change, the command was erroring out.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139417
Approved by: https://github.com/Skylion007, https://github.com/fduwjj
I'm sick of reductions not working properly - spotty dim coverage, missing backwards, etc. This PR fixes quite a bit.
It applies to the following ops:
* `sum` / `mean` / `prod`
* `all` / `any`
* `amin` / `amax`
* `min` / `max`
* `argmin` / `argmax`
The general reduction logic has been factored out into a helper `_apply_reduction(func, func_name, identity_element, *args, **kwargs)`. The idea is that by providing a valid identity element, we can utilize conversions to padded dense when needed for reducing over the ragged dim.
Extensive test coverage includes:
* reductions across ragged dim
* reductions across non-batch, non-ragged dims
* reductions across both batch and ragged dims
* multiple dim reductions (for ops that support this)
* full reduction -> scalar
Bonus: the PR includes backwards fixes for `sum` and `mean`, which have never worked.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139317
Approved by: https://github.com/cpuhrsch
MInor, adds a linter that ensures that all jobs run on pull_request, schedule, push etc have a `if: github.repository_owner == 'pytorch'` or are dependent on a job that has that check
There is also a setting in Github repos that can disable all workflows for that repo
A lot of these are unnecessary because many jobs use reusable workflows that have that check. However, this is a one time change so I'm not that bothered
Unfortunately I can't put this at the workflow level, which would make this better
Lots of weird string parsing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138849
Approved by: https://github.com/malfet
To skip over the command that do not have output file specified
Recently I've noticed that `generate_torch_version.py` started to run on every rebuild, and this results in a failed plan for deb info rebuilds
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138290
Approved by: https://github.com/Skylion007
Summary:
X-link: https://github.com/pytorch/executorch/pull/5720
For smaller models the overhead of profiling ops might be prohibitively large (distorting the inference execution time significantly) so we provide users an option to disable op profiling and essentially only profile the important events such as inference execution time.
To disable operator profiling users need to do:
```
etdump_gen.set_event_tracer_profiling_level(executorch::runtime::EventTracerProfilingLevel::kNoOperatorProfiling);
```
Test Plan: Added test case.
Differential Revision: D61883224
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136838
Approved by: https://github.com/dbort
As title, when testing on an internal case, we found that we have very similar output for the error when certain ranks does not join one collective. This is because we didn't put all ranks into `candidate_ranks` so that they didn't get wiped out from entries and gets checked again.
Ideally for the given case, we should report this is an out of order case, because rank 0, 1 calls all-to-all while all the rest ranks call all-gather-base. But when we select entries to compare, we don't have global view of the entries.
In the specific case, on rank 0 and 1, it has collective of PG 7 on entry 1130 with seq ID = 1130. However, on other ranks, they have collective of PG 0 on entry 1130 with seq ID = 2. It's hard to use entry idx to do the match because if we later consider p2p, this assumption will collapse, so we now still defer it for users or further down debugging stream to figure it out. To make the message clearer, I also include both seqID and record_id (aka, entry index) in the message. (That does not mean this is not possible to implement in the code, for example, we can let all record_id to minus the maximum p2p seq id before it; but users will easily see the wrong order, so we don't think it's necessary to have that logic now)
P1626755348
Differential Revision: [D63815335](https://our.internmc.facebook.com/intern/diff/D63815335/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137256
Approved by: https://github.com/c-p-i-o
* Upload_metrics function to upload to ossci-raw-job-status bucket instead of dynamo
* Moves all added metrics to a field called "info" so ingesting into database table with a strict schema is easier
* Removes the dynamo_key field since it is no longer needed
* Removes the concept of reserved metrics, since they cannot be overwritten by user added metrics anymore
* Moves s3 resource initialization behind a function so import is faster
---
Tested by emitting a metric during run_test and seeing that documents got added to s3
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136799
Approved by: https://github.com/ZainRizvi
`json.dumps(float("inf"))` returns `Infinity`, which is technically invalid json
This is fine if you json.load, but ClickHouse cannot handle it
Solution here: cast inf and nan to string (which ClickHouse is able to cast back to float)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136877
Approved by: https://github.com/huydhn
I think this could help many teams, especially compile/export teams (/cc @ezyang), to let end user/bug reporters to quickly test WIP PR when reporting a related bug.
This could quickly run in an official nightly Docker container or in a nightly venv/coda env.
Let me know what do you think.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136535
Approved by: https://github.com/ezyang
This PR:
* Implements the pre-existing `nt.to_padded_tensor(padding_val)` ATen op via the FBGEMM kernel + appropriate view gymnastics (since that kernel only handles 2D values)
* Introduces a new `_nested_from_padded_tensor` op for the reverse conversion, implemented via the reverse FBGEMM kernel + view gymnastics
* Note: there is currently no public API for this; design booted to a future PR
TODO:
* ~~Propagate min / max sequence length via the new factory function `_nested_from_padded_tensor`~~
* ~~Verify that Inductor does computation fusion via test logic~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125947
Approved by: https://github.com/soulitzer
