Summary: This allows us to start alerting on cache failures, based on scuba data
Test Plan:
Added new tests explicitly for the Remote Cache API.
Note that we have existing tests for memcache, but not for manifold AFAICT.
There are two potential wrinkles. One we're adding a new field (and everything uses ScubaData AFAICT, so this should just work).
The other one is the implicit api contract that if the sample is None, then it will be ignored (and not crash). I believe the second one is implemented correctly (and tested). The first one is a little more nebulous, but I think won't cause any breakages.
Also manually ran a compile and made sure it didn't break - P1851504490 as well as forcing it to break and checking we didn't screw up the exception handling - P1851504243
Rollback Plan:
Differential Revision: D77054339
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156874
Approved by: https://github.com/oulgen, https://github.com/masnesral
This logs a waitcounter of the name pytorch.dynamo_timed.{key}.
Primarily sending this now to make sure everyone likes the API, then
I'll add tests, and migrate one dynamo_timed to use it. (likely starting
with
https://github.com/pytorch/pytorch/pull/141379).
Testing is a bit harder, since we don't normally have any way to read
_WaitCounter state AFAICT. I want to poke around and see if I can figure
out a way to read the state, otherwise I'll just mock it to at least
make sure it's mostly working.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141402
Approved by: https://github.com/jamesjwu, https://github.com/masnesral
Here's the overview:
There's a new contextmanager singleton called MetricsContext. Entering the MetricsContext is how we demarcate the boundary on which we'll create a single CompilationMetrics object, and therefore, a single dynamo_compile log entry. While we're inside the MetricsContext, we can update/set many different metrics. Most importantly: `dynamo_timed` can also update the in-progress MetricsContext. In the proposal here, we tell `dynamo_timed` that we want it to do so by providing the name of the MetricsContext field to increment. There can be many `dynamo_timed` calls in different parts of the code updating different fields. Then when the MetricsContext exits, that's when the logging of everything gathered finally happens. One potential footgun is trying to use `dynamo_timed` when we haven't entered the MetricsContext, but we assert on that problem. Another problem is that we re-enter the context recursively, but we watch for that and do the logging only when the outermost exits.
Some specifics:
* Introduce MetricsContext - a context manager that on exit, records the CompilationMetrics (which also logs to dynamo_compile).
* Completely remove the concept of frame_phase_timing. Instead, update the MetricsContext during compilation, either directly or via dynamo_timed.
* Remove some globals we previously used to accumulate counters to later populate a CompilationMetrics. We use CompilationMetrics set/update/increment APIs instead.
* `record_compilation_metrics` is now called on exit from MetricsContext.
* Populate legacy CompilationMetrics fields right before logging, inside `record_compilation_metrics`.
* Remove the one-off `add_remote_cache_time_saved` helper; capture that timing directly into the MetricsContext.
And specifically, several changes to dynamo_timed:
* "Modernize" the parameters and update all callsites accordingly.
* Move the backwards logging of the CompilationMetrics to the backwards compile location.
* Add a parameter for which CompilationMetrics field to update
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139849
Approved by: https://github.com/ezyang
Here's the overview:
There's a new contextmanager singleton called MetricsContext. Entering the MetricsContext is how we demarcate the boundary on which we'll create a single CompilationMetrics object, and therefore, a single dynamo_compile log entry. While we're inside the MetricsContext, we can update/set many different metrics. Most importantly: `dynamo_timed` can also update the in-progress MetricsContext. In the proposal here, we tell `dynamo_timed` that we want it to do so by providing the name of the MetricsContext field to increment. There can be many `dynamo_timed` calls in different parts of the code updating different fields. Then when the MetricsContext exits, that's when the logging of everything gathered finally happens. One potential footgun is trying to use `dynamo_timed` when we haven't entered the MetricsContext, but we assert on that problem. Another problem is that we re-enter the context recursively, but we watch for that and do the logging only when the outermost exits.
Some specifics:
* Introduce MetricsContext - a context manager that on exit, records the CompilationMetrics (which also logs to dynamo_compile).
* Completely remove the concept of frame_phase_timing. Instead, update the MetricsContext during compilation, either directly or via dynamo_timed.
* Remove some globals we previously used to accumulate counters to later populate a CompilationMetrics. We use CompilationMetrics set/update/increment APIs instead.
* `record_compilation_metrics` is now called on exit from MetricsContext.
* Populate legacy CompilationMetrics fields right before logging, inside `record_compilation_metrics`.
* Remove the one-off `add_remote_cache_time_saved` helper; capture that timing directly into the MetricsContext.
And specifically, several changes to dynamo_timed:
* "Modernize" the parameters and update all callsites accordingly.
* Move the backwards logging of the CompilationMetrics to the backwards compile location.
* Add a parameter for which CompilationMetrics field to update
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139849
Approved by: https://github.com/ezyang
ghstack dependencies: #140094
Summary: I'm refactoring dynamo_timed and updating the params. It will be much easier to do this refactor entirely in OSS. So this diff essentially provides a couple aliases in the OSS area that I can update without affecting the internal usage.
Test Plan: Ran locally and made sure I still got samples: https://fburl.com/scuba/dynamo_compile/sandbox/qub89lwj
Reviewed By: oulgen
Differential Revision: D65580302
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140016
Approved by: https://github.com/oulgen
Summary:
This diff reverts D65290089
This change is introducing more logging than I realized and could present problems for tlparsen
Test Plan: NA
Reviewed By: jamesjwu
Differential Revision: D65541060
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139893
Approved by: https://github.com/jamesjwu
Previously: https://github.com/pytorch/pytorch/pull/138052 but the implementation is done from scratch, so I open a new PR.
This implements the ability to save and load profiles of automatic dynamic decisions, so on subsequent runs we can directly make something automatically dynamic. Unlike the previous implementation, this cache is never enabled by default; instead, you have to specify a "job id" that says it's OK to share results. We will be able to automatically populate this id for internal MAST jobs but for generic OSS users you will have to explicitly opt into it.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139001
Approved by: https://github.com/oulgen
Previously: https://github.com/pytorch/pytorch/pull/138052 but the implementation is done from scratch, so I open a new PR.
This implements the ability to save and load profiles of automatic dynamic decisions, so on subsequent runs we can directly make something automatically dynamic. Unlike the previous implementation, this cache is never enabled by default; instead, you have to specify a "job id" that says it's OK to share results. We will be able to automatically populate this id for internal MAST jobs but for generic OSS users you will have to explicitly opt into it.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Differential Revision: [D65065497](https://our.internmc.facebook.com/intern/diff/D65065497)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139001
Approved by: https://github.com/oulgen
Previously: https://github.com/pytorch/pytorch/pull/138052 but the implementation is done from scratch, so I open a new PR.
This implements the ability to save and load profiles of automatic dynamic decisions, so on subsequent runs we can directly make something automatically dynamic. Unlike the previous implementation, this cache is never enabled by default; instead, you have to specify a "job id" that says it's OK to share results. We will be able to automatically populate this id for internal MAST jobs but for generic OSS users you will have to explicitly opt into it.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Differential Revision: [D65065497](https://our.internmc.facebook.com/intern/diff/D65065497)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139001
Approved by: https://github.com/oulgen
Summary: Implement Remote AOTAutogradCache. It uses all the same tech as Remote FXGraphCache, just with its own name.
Test Plan:
Run benchmark:
TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE=1 TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE=1 TORCHINDUCTOR_AUTOGRAD_CACHE=0 TORCHINDUCTOR_FX_GRAPH_CACHE=0 TORCH_LOGS=+torch._functorch._aot_autograd.autograd_cache buck run mode/opt benchmarks/dynamo:torchbench -- --training --backend=inductor --only nanogpt --repeat 5 --performance --cold-start-latency
See that it cache hits even with local cache removed.
Results show up in remote cache logs https://fburl.com/scuba/pt2_remote_cache/5893dbaj
New unit tests
Reviewed By: oulgen
Differential Revision: D63323958
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137278
Approved by: https://github.com/oulgen
Summary:
- Added TORCH_LOGS=cache to dump cache stats on exit - supported by RemoteCache.
- Split REMOTE_CACHE_VERSION - it was used for both JKs fx_graph_memcache_version and autotune_memcache_version but they really should be separate (just in case we need to change one but not the other)
- Prepare `_ManifoldCache` for use with other subpath keys
- Move create_cache to be more public and use it in codecache
- Add _InductorMetaTy alias (still just a dict)
- Cleaned up some common cached_autotune calls in triton_heuristics
Test Plan: unit tests
Reviewed By: oulgen
Differential Revision: D62648249
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136456
Approved by: https://github.com/oulgen
Summary:
The existing RemoteCacheBackend classes were a bit haphazard - some of them accepted bytes only, some accepted objects, some returned different types of objects than were passed in.
Update them to be more consistent:
1. RemoteCacheBackend is an implementation of a backend: Redis, Memcache, Manifold, LocalFile
2. RemoteCacheSerde is an implementation of a serde protocol - to turn structured objects (dict, list, etc) into bytes: RemoteCacheJsonSerde (json encoding), RemoteCachePassthroughSerde (strictly bytes only)
3. RemoteCache is the cache implementation itself, mixing a RemoteCacheBackend along with an RemoteCacheSerde to provide structured caching.
Other than simply reorganizing the existing cache code this also fixes the Redis autotune caching for OSS.
Test Plan: unit tests
Reviewed By: oulgen
Differential Revision: D61178859
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134032
Approved by: https://github.com/oulgen, https://github.com/bhack
Summary: We need an implementation of RedisRemoteCacheBackend with the same API that we're using for FbMemcacheRemoteFxGraphCacheBackend. So we'll stop using the Triton implementation and adapt a version for use by inductor. I also renamed parameters and cache entries to match our cache terminology.
Test Plan: Ran this command twice and inspected log output to ensure I got cache hits:
```
TORCH_LOGS=+torch._inductor.codecache TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE=1 python benchmarks/dynamo/torchbench.py --performance --inductor --device cuda --training --amp --print-compilation-time --only dcgan
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127480
Approved by: https://github.com/oulgen
