Summary: We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize.
Test Plan:
# unit test
```
buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten
```
Buck UI: https://www.internalfb.com/buck2/776d3911-bb86-4ac8-a527-540cf1510b9d
Test UI: https://www.internalfb.com/intern/testinfra/testrun/4785074873051017
Network: Up: 4.3MiB Down: 42MiB (reSessionID-fef7e727-68b1-4645-a519-5652854df38d)
Executing actions. Remaining 0/4 6.7s exec time total
Command: test. Finished 2 local
Time elapsed: 3:11.5s
Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0
# E2E
### how to enable (you can overrite the dtype, if nothing given, the default is fp8)
```
post_grad_fusion_options={
"activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}
},
```
Differential Revision: D70522237
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148380
Approved by: https://github.com/Mingming-Ding, https://github.com/Hahu803
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
Summary: There are a few issues I'm solving:.
1. It's too hard to measure total pt2 overhead using the dynamo_compile table because users need to know the columns representing all the top-level events (dynamo_cumulative_compile_time_us, etc.). Instead, let's populate the existing duration_us field for all top-level events. The complication is that runtime events in particular (Triton autotuning, cudagraphify) can be collapsed into a single row, with gaps in between, so we can't simply use `end_time - start_time` in all cases. Instead, we'll sum durations for all outer events when updating the compile-time or runtime metrics context. Introduce a 'depth' counter in TLS to track the nesting of CompilationMetrics events.
2. The existing implementation relies on callers of dynamo_timed to specify whether the event is a runtime or compile-time event. That doesn't work because some methods can be called in both situations, e.g., `CachingAutotuner.benchmark_all_configs`. For example `TORCHINDUCTOR_BENCHMARK_FUSION=1` enables benchmarking during compile-time. Instead, we can figure out automatically whether we're measuring a compile-time or runtime event and log accordingling.
3. If `log_compilation_events` were to throw an exception, we'd fail to clear the aggregated counters for runtime logs and they could be attributed to the wrong compile ID. I didn't actually find evidence of this in practice, but I added exception handling for extra safety.
Test Plan:
Ran internal models and compared dynamo_compile to pt2_compile_events:
`TORCHINDUCTOR_BENCHMARK_FUSION=0`
* tlparse: https://fburl.com/itciwnxc
* dynamo_compile: https://fburl.com/scuba/dynamo_compile/yvkif5vb
* pt2_compile_events: https://fburl.com/scuba/pt2_compile_events/segijet7
`TORCHINDUCTOR_BENCHMARK_FUSION=1`
* tlparse: https://fburl.com/jgurcvkw
* dynamo_compile: https://fburl.com/scuba/dynamo_compile/uum91ceb
* pt2_compile_events: https://fburl.com/scuba/pt2_compile_events/x4xnisez
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151749
Approved by: https://github.com/Skylion007
This PR makes it so that we don't crash due to logging if we invoke AOTAutogradCache/FXGraphCache without using dynamo. This is preparation for supporting certain VLLM use cases where they store graph modules and have special handling in conjunection with the caches.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150423
Approved by: https://github.com/oulgen
This patch effectively ignores traceable_tensor_subclasses, allowing
Dynamo to always try tracing into the `__torch_function__` of tensor
subclass. This helps us with 2 things:
1. allowing users to directly benefit from better compilation of tensor
subclass, by just upgrading pytorch, without having to change legacy
library code (see earlier patches in the stack for examples).
2. potentially exposing more issues in compiling tensor subclass, so we
can get signals and improve them.
As a consequence, it exposed and fixes 2 subtle bugs:
1. In `build_torch_function_fn`, we could get
`torch._C._disabled_torch_function_impl` because we have a
`Parameter` subclass without `__torch_function__` override or if we
have a tensor subclass with `__torch_dispatch__` override. We graph
break on this for now, and plan to add support -- the logic for
simulating `torch._C._disabled_torch_function_impl` is already in
`SuperVariable`, we just need to reuse it.
2. Sometimes we create `SyntheticLocalSource` and need to remove all the
guards installed on it, but we only removed the ones whose source
_is_ the created synthetic source `s`, but forgot about chained
source like `s.foo`, this showed up as
`SYNTHETIC_LOCAL['tmp_0'].__torch_function__.__func__`.
Differential Revision: [D71906141](https://our.internmc.facebook.com/intern/diff/D71906141)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149792
Approved by: https://github.com/jansel, https://github.com/mlazos
ghstack dependencies: #149482, #149483, #149484
This patch effectively ignores traceable_tensor_subclasses, allowing
Dynamo to always try tracing into the `__torch_function__` of tensor
subclass. This helps us with 2 things:
1. allowing users to directly benefit from better compilation of tensor
subclass, by just upgrading pytorch, without having to change legacy
library code (see earlier patches in the stack for examples).
2. potentially exposing more issues in compiling tensor subclass, so we
can get signals and improve them.
As a consequence, it exposed and fixes 2 subtle bugs:
1. In `build_torch_function_fn`, we could get
`torch._C._disabled_torch_function_impl` because we have a
`Parameter` subclass without `__torch_function__` override or if we
have a tensor subclass with `__torch_dispatch__` override. We graph
break on this for now, and plan to add support -- the logic for
simulating `torch._C._disabled_torch_function_impl` is already in
`SuperVariable`, we just need to reuse it.
2. Sometimes we create `SyntheticLocalSource` and need to remove all the
guards installed on it, but we only removed the ones whose source
_is_ the created synthetic source `s`, but forgot about chained
source like `s.foo`, this showed up as
`SYNTHETIC_LOCAL['tmp_0'].__torch_function__.__func__`.
Differential Revision: [D71906141](https://our.internmc.facebook.com/intern/diff/D71906141)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149792
Approved by: https://github.com/jansel, https://github.com/mlazos
ghstack dependencies: #149482, #149483, #149484
Summary:
This will log a wait counter with for backward compile and fixes weirdness with nested context managers.
Since the old wait counters added through dynamo_timed were never created with the nesting issue. I am also changing the key nomenclature from `pytorch.dynamo_timed` to `pytorch.wait_counter`. We want to use the same nomenclature, to make it easy to find keys.
Reviewed By: jamesjwu
Differential Revision: D72032055
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150235
Approved by: https://github.com/jamesjwu, https://github.com/masnesral
We weren't handling `setattr(tensor_obj, "real", 42)` correctly, because
the attribute is a `GetSetDescriptorType` that has special setter logic.
See added test and comments for more explanations.
This patch makes it so that we graph break in those cases, rather than
resulting in silent incorrectness.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149791
Approved by: https://github.com/mlazos
ghstack dependencies: #149481
Summary: This adds a version field like the following: `3.10.9+fb (3.10:1dd9be6, May 4 2022, 01:23:45) [Clang 15.0.7 (mononoke://mononoke.internal.tfbnw.net/fbsource 5d1601b0eed7426ac`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149419
Approved by: https://github.com/c00w
PR does following
* Turns `inference_mode` to False and `no_grad` for `convert_frame`, if the inference_mode is on globally.
* Turns off inference_mode for fake tensor prop. This ensures that converting from real inference tensor to a fake tensor removes the inference-ness.
* Graph breaks on is_inference and is_inference_mode_enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149321
Approved by: https://github.com/jansel, https://github.com/zou3519
This allows for each device type to check current devices for Triton compatibility and ensure their Triton backend is present.
This PR replaces the `has_triton()` global method which was previously used for this task, and moves the initial check for each Inductor backend on to their associated `BaseScheduler` subclass. This means that other backends, such as Halide, can also implement their own availability checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139171
Approved by: https://github.com/jansel
This gives us a decent proxy for how big of a graph we functionally had to parse.
Note that this is a cummulative counter. If people feel strongly, I can either write into the dynamo_timed datasets with metrics contexts, or clear the counters / write a counter per frame id as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147149
Approved by: https://github.com/jansel
Summary: this adds some new dynamo_timed calls in cudagraph_trees, primarily with the aim to add cudagraph-related timing to scuba. Things to note:
* Uses the changes in https://github.com/pytorch/pytorch/pull/141919 to log "runtime" entries
* The logging for chromium/tlparse/scuba relies on us providing a compile_id since it's not available in the environment. A lot of the changes here are just passing around the compile_id
* I believe the spirit of the scuba logging is to capture the overheads of `torch.compile`. Therefore, I'm not adding _every_ dynamo_timed to scuba. For example, "run_eager" is the first real execution of the inductor graph -- it's not cudagraph overhead, per se. Watch out for the two instances of `dynamo_compile_runtime_column_us="runtime_cudagraphify_time_us"`. Those are the spots I believe are _extra_ overhead we'd contribute to torch.compile.
Test Plan:
`python benchmarks/dynamo/torchbench.py --performance --training --amp --backend inductor --device cuda --print-compilation-time --repeat 5 --cold-start-latency --only dcgan`:
* tlparse: https://fburl.com/21yrdn8h
* scuba: https://fburl.com/scuba/dynamo_compile/sandbox/wt90wnjz
`python benchmarks/dynamo/torchbench.py --performance --training --amp --backend inductor --device cuda --print-compilation-time --repeat 5 --cold-start-latency --only nanogpt`
* tlparse: https://fburl.com/r9mp7uiv
* scuba: https://fburl.com/scuba/dynamo_compile/sandbox/1nvx94re
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143220
Approved by: https://github.com/eellison
Softmax need do some preparation work that access the input tensor in two passes
- compute amax of each row
- compute (x - amax).exp.sum for each row
When the row size is large, cache can not hold all the active data and accessing the input multiple passes increases execution time since the kernel is membw bounded.
Online softmax uses a customized reduction to compute max and sum at the same time by accessing the data in one pass. Check this paper for more details ( https://arxiv.org/abs/1805.02867 ).
Also here is an online softmax kernel generated by inductor as a reference: https://gist.github.com/shunting314/67ae4fffd45d4f2753c781780332fa54
## Microbenchmark
- `TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 TORCHINDUCTOR_ONLINE_SOFTMAX=0 DO_PERF_TEST=1 python test/inductor/test_online_softmax.py -k test_softmax` : without online softmax
- eager_ms=6.671296119689941
- opt_ms=8.06931209564209
- `TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 TORCHINDUCTOR_ONLINE_SOFTMAX=1 DO_PERF_TEST=1 python test/inductor/test_online_softmax.py -k test_softmax`: with online softmax
- eager_ms=6.634047985076904
- opt_ms=6.230591773986816
Ideally, online softmax should save about 2ms here. We saves about 1.84ms in practice.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127011
Approved by: https://github.com/jansel
Summary: Gather the compilation time of individual triton kernels and log them to dynamo_compile:
* Time compilation in `_worker_compile_triton` and pass back to the main process and logged from `get_result()`.
* Added a way to track the "top N" (or N most-expensive compiles) in the metrics_context. I did this because I doubt we really care to capture potentially thousands of kernel compile times. That would be problematic for scuba logging anyway, so let's limit the number we track from the beginning. Arbitrarily chose 25 for now.
* Format the list of compile times as a json string before logging.
Test Plan:
`python benchmarks/dynamo/torchbench.py --performance --training --amp --backend inductor --device cuda --print-compilation-time --repeat 5 --cold-start-latency --only nanogpt`
Scuba: https://fburl.com/scuba/dynamo_compile/sandbox/nc4dzm3r
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147022
Approved by: https://github.com/jamesjwu
## Context
> **Note:** `mark_traceable` got renamed to `nonstrict_trace` after
> offline discussion. The reasons are (1) it aligns with `torch.export`'s
> `nonstrict` notion, and (2) it's more definitive in behavior suggestion.
1. [Overall Design](https://docs.google.com/document/d/1O-dR2ZQaJQVt_v67AVcDCw2yJLtqgkZFwoXK0buEWRg/edit?tab=t.0)
2. [Dynamo graph representation with `torch._higher_order_ops.flat_apply`](https://docs.google.com/document/d/1YHl5nPTJvYeCPE5TO9uA18DPWNgUYGE4gCn6bFvXcBM/edit?tab=t.0#heading=h.xtw3hhbro4gn)
## Summary
This patch adds a `torch._dynamo.nonstrict_trace` decorator, which
currently is an enhanced version of `torch._dynamo.allow_in_graph` (see
docstring for their differences). Specifically, this patch focuses on
the UI and functionality prototyping/plumbing.
The main enhancement is supporting more input types, and the
implementation challenge lies in reconstructing the input objects from
Dynamo `VariableTracker` (while accounting for buffered side-effects and
guards). This patch takes a middle-ground (simple implementation with a
bit of user labor), by
1. asking the user to provide pytree registration for non-proxy-able
input types,
2. letting Dynamo trace through `pytree_flatten` (which accounts for
buffered side-effects and guards automatically),
3. and passing in the TreeSpec as a graph attribute constant into
`torch._higher_order_ops.flat_apply` (which unflattens the inputs and
invokes the underlying function).
## Next Steps
In subsequent patches, we will try to support the following:
- annotating on class method
- reads to global tensors
- inputs that contains `pytree.register_constant`-ed instances.
- function as input
- more output types (e.g., any pytree-registered type)
- `torch.nn.Module` as inputs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146367
Approved by: https://github.com/zou3519
ghstack dependencies: #146714
This is for "for some large number Z, make sure the error messages are readable English." - beginning to audit all `unimplemented` sites and making sure that all messages are at least English-readable. Hints may not necessarily be provided.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147385
Approved by: https://github.com/jansel
Part of the required fix for https://github.com/intel/torch-xpu-ops/issues/1264.
To support `roi_align`, torchvision uses `is_compile_supported` in `torch/_dynamo/utils.py` to compile a non-deterministic version of the op for backwards passes. This PR adds XPU device to the supported compile devices.
The `is_compile_supported()` util function has extremely limited usage, only being used in `torchvision.ops.roi_align` and `torch.utils._content_store.has_storage()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147541
Approved by: https://github.com/guangyey, https://github.com/jansel
Co-authored-by: lei,zhenyuan <zhenyuan.lei@intel.com>
Current implementation reads as: we will only actually use the "python_reducer" config if the DDP forward is compiled. Otherwise, we will silently fallback to C++ reducer + no DDPOptimizer.
I'm changing this behavior to always use the python reducer if the config is specified.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147123
Approved by: https://github.com/fegin
This PR adds support for list subclasses. Among other things are
1) Tracking the mutations on internal vts like `_dict_vt` and `_list_vt` using sources. This helps identify if there was a mutation in the underlying data structures, and we need to reconstruct it.
2) `UserDefinedObjectVariable` now has a new method - `is_modified` which `side_effect` infra relies upon to check mutations in the underlying vts (like `_dict_vt`).
3) `reconstruction` logic ensures that we use `dict.__getitem__` and `list.__getitem__` methods. This is super important because we don't want to call the overridden `__getitem__` methods.
If this PR is hard to review, please let me know. I can break it into several small PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146819
Approved by: https://github.com/StrongerXi, https://github.com/jansel
`get_top()` is really confusing when talking about a stack, because it can mean the most recently started event on the stack or the toplevel event in perfetto(which displays the stack upside down). Rename to `get_outermost` and fix the bug associated with it, so that it returns the correct value out of the stack.
Running nanogpt now puts `guard_latency_us` correctly in the `dynamo` event:
```
tlp python benchmarks/dynamo/torchbench.py --backend inductor --device cuda --only nanogpt --amp --cold-start-latency --print-compilation-time --training --performance 2>&1 --dynamic-shapes | tee out.log
```
<img width="1281" alt="image" src="https://github.com/user-attachments/assets/4eeb371a-4d81-415a-acc4-7d303a4b2a93" />
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146649
Approved by: https://github.com/masnesral, https://github.com/anijain2305
