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
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
PR https://github.com/pytorch/pytorch/pull/146939/ added an argument for evaluate_expr for the purpose of logging.
This caused a regression that we thought is due to calling id on symnode.
I digged deeper and found that adding that argument although does not effect results of evaluate_expr it mess the cache
lookups.
I refactored the code to avoid using expr_sym_node_id in the cache lookup, I also introduced evaluate_sym_node to and simplified the calls to evaluate_expr
#suppress-bc-linter
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147836
Approved by: https://github.com/oulgen
tts_angular with cudagraph is flaky. Its speedup varies from .05 to 1.01. This PR disables cudagraph for tts_angular to avoid the noise. Since tts_angular shows ~1x speedup while other torchbench models show ~2x speedup, skipping tts_angular would wrongly bump the cudagraph speedup. So this PR only disables cudagraph for tts_angular instead of skipping tts_angular.
[Dashboard ](https://github.com/pytorch/pytorch/actions/runs/13597394087)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148221
Approved by: https://github.com/eellison
Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
As `cuBLAS` workspaces are already per-stream, there shouldn't be kernel execution overlap with `cuBLASLt` kernels.
This PR reuses `cuBLAS` workspaces for `cuBLASLt` for the following benefits:
+ caching (`cuBLAS` workspaces were already cached, so now we get that for `cuBLASLt`)
+ "free" workspace size bump for `cuBLASLt` `cuBLASLt` workspace sizes were previously smaller than those for `cuBLAS` by default which potentially hurts performance, and we encountered difficulty in increasing the size due to downstream OOMs , see also #120925
+ fixes behavior broken behavior with the memtracker; https://github.com/pytorch/pytorch/pull/139442 attempted to handle peaky allocation behavior that broke memtracker equivalence tests but it didn't seem to fully work, here the cached/reused `cuBLAS` workspace seems to fix it
+ one environment variable to rule them all: `CUBLAS_WORKSPACE_CONFIG` applies directly to `cuBLASLt` without a confusing `CUBLASLT_WORKSPACE_SIZE` that users would also need to consider
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145130
Approved by: https://github.com/ngimel
We want to log each symnode created so that we can do provenance tracking in the tlparse report generated for draft export. To do this, we want to assign a unique id to every symnode, which python's `id` function already does, and then for every expression created, we can find the provenance by tracing back through its arguments ids. This logging only happens when dtrace_structured is enabled, which is only when running draft export.
An example output is as follows:
<img width="799" alt="image" src="https://github.com/user-attachments/assets/88bb31b4-8c31-43fb-aa88-08b573b9f71d" />
For the increase in the compile_time_instruction_count benchmark, this seems unavoidable because I need to call `id` to get the unique identifier for each symnode. But I believe `id` is an inexpensive operation, so hopefully it should be ok? I tried doing the following:
* Originally I was passing around `self`, which is a SymNode, which caused the compile time to be ~6.36M
* I changed it to pass around `id(self)` instead, which reduced the compile time to ~6.33M
* Then I changed it to be passed as a positional arg instead of a kwarg, which reduced the compile time to ~6.22M, but this doesn't seem to be a super worthwhile fix?
#suppress-bc-linter
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146939
Approved by: https://github.com/oulgen
## What's the problem?
The popular `fx.node.map_arg()` and `fx.node.map_aggregate()` apply operations recursively on `dict`s, `tuples`, `list`s, etc, and return a new collection of the same type.
Unfortunately, their base input type is `Argument`, which is [very unspecific indeed](5d55a6585d/torch/fx/node.py (L48-L58)): most type information is just thrown away at the call site of either of these functions, as far as the type checker goes.
As `torch` moves to a more typed code base, this would force innocent, unsuspecting developers to add logically unnecessary casts or `# type: ignore` statements.
## What's the solution?
Making these two `node.map_*` functions generic on the first argument and return type means that type information is preserved for the type checker. (The signature of the other parameter, the function that visits the nodes and subnodes, has not changed, nor should it.)
## Won't it break everything?
It doesn't break the type checker - one place needed an extra hint.
There have been code breakages, resolved one, at least one new one... we'll see!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146248
Approved by: https://github.com/XuehaiPan, https://github.com/Skylion007
Enables a few ruff rules
* Ban print statements within asserts (likely bugs)
* ~Use string for Decimal literal to prevent loss of precision~
* ~Do not use default args for __post__init__ in dataclasses, they likely were meant to go into the factory method, the __init__, or somewhere else. The default values are useless here.~
Wait until ruff upgrade for the last 2
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146615
Approved by: https://github.com/jansel
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
This replaces the `__getattr__()` pattern used in remaining OpHandlers with a `DefaultHandler` class defined in part 2.
Some compile time wins from this as well:
```
2025-02-02T19:46:32.2033010Z
2025-02-02T19:46:32.2036607Z WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 29633182927 is -1.71% lower than expected 30150000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2037575Z
2025-02-02T19:46:32.2037907Z please update all results that changed significantly, and not only the failed ones
2025-02-02T19:46:32.2039291Z PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 43986879172 -1.02% is within expected 44440000000 ±2.50%
2025-02-02T19:46:32.2040131Z
2025-02-02T19:46:32.2041180Z WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26246225695 is -1.85% lower than expected 26740000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2042188Z
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146255
Approved by: https://github.com/shunting314
ghstack dependencies: #146252, #146254
As `cuBLAS` workspaces are already per-stream, there shouldn't be kernel execution overlap with `cuBLASLt` kernels.
This PR reuses `cuBLAS` workspaces for `cuBLASLt` for the following benefits:
+ caching (`cuBLAS` workspaces were already cached, so now we get that for `cuBLASLt`)
+ "free" workspace size bump for `cuBLASLt` `cuBLASLt` workspace sizes were previously smaller than those for `cuBLAS` by default which potentially hurts performance, and we encountered difficulty in increasing the size due to downstream OOMs , see also #120925
+ fixes behavior broken behavior with the memtracker; https://github.com/pytorch/pytorch/pull/139442 attempted to handle peaky allocation behavior that broke memtracker equivalence tests but it didn't seem to fully work, here the cached/reused `cuBLAS` workspace seems to fix it
+ one environment variable to rule them all: `CUBLAS_WORKSPACE_CONFIG` applies directly to `cuBLASLt` without a confusing `CUBLASLT_WORKSPACE_SIZE` that users would also need to consider
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145130
Approved by: https://github.com/ngimel
This replaces the `__getattr__()` pattern used in remaining OpHandlers with a `DefaultHandler` class defined in part 2.
Some compile time wins from this as well:
```
2025-02-02T19:46:32.2033010Z
2025-02-02T19:46:32.2036607Z WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 29633182927 is -1.71% lower than expected 30150000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2037575Z
2025-02-02T19:46:32.2037907Z please update all results that changed significantly, and not only the failed ones
2025-02-02T19:46:32.2039291Z PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 43986879172 -1.02% is within expected 44440000000 ±2.50%
2025-02-02T19:46:32.2040131Z
2025-02-02T19:46:32.2041180Z WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26246225695 is -1.85% lower than expected 26740000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2042188Z
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146255
Approved by: https://github.com/shunting314
ghstack dependencies: #146225, #146226, #146235, #146252, #146254
This enforces the invariant that every backend implements the same set of ops and removes a layer of indirection for BasicMathOps.
Interestingly this is a small compile time win:
```
...
WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 30151159301 is -6.13% lower than expected 32120000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 44447549162 -1.69% is within expected 45210000000 ±2.50%
WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26743557195 is -2.25% lower than expected 27360000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
PASS: benchmark ('basic_modules_ListOfLinears_eager', 'compile_time_instruction_count') pass, actual result 945129734 +0.93% is within expected 936400000 ±1.50%
WIN: benchmark ('basic_modules_ListOfLinears_inductor', 'compile_time_instruction_count') failed, actual result 18984384503 is -3.19% lower than expected 19610000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
WIN: benchmark ('basic_modules_ListOfLinears_inductor_gpu_force_shape_pad', 'compile_time_instruction_count') failed, actual result 17258025389 is -1.94% lower than expected 17600000000 ±1.50% please update the expected results.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146235
Approved by: https://github.com/shunting314
ghstack dependencies: #146225, #146226
