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Author SHA1 Message Date
Gabriel Ferns
254293b777 Add flag _metrics_log_runtime to disable runtime metric logging by default (#153506) 
https://github.com/pytorch/pytorch/pull/152708 expanded support of `get_estimated_runtime` to many more types of `SchedulerNodes`. This caused an increase in compile time because we're always calling `get_estimated_runtime` to populate the metrics table. This PR adds a flag for this logging, which reduces the instruction count by 8%. Long term, we should probably merge metrics.py with TORCH_LOGS/tlparse (suggestion from @xmfan).

Update: added support for TORCH_LOGS for the metrics logging.

Test Plan:
mm_loop.py and many existing tests cover.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153506
Approved by: https://github.com/eellison
 2025-05-22 01:02:11 +00:00
Anita Katahoire
996c4d803d Removing conda references from PyTorch Docs (#152702) 
Addresses #148339

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152702
Approved by: https://github.com/svekars, https://github.com/albanD, https://github.com/atalman
 2025-05-20 20:33:28 +00:00
PyTorch MergeBot
3443627e07 Revert "[BE]: Enable RUFF TRY400 rule - log.exception (#153473)" 
This reverts commit 4f4ecc583e.

Reverted https://github.com/pytorch/pytorch/pull/153473 on behalf of https://github.com/jeanschmidt due to seems to have broken internal signals, @albanD may I count on you to help the author merge his PR? D74837988 ([comment](https://github.com/pytorch/pytorch/pull/153473#issuecomment-2886017075))
 2025-05-16 08:29:26 +00:00
PyTorch MergeBot
4d073af58c Revert "[inductor][dynamo] Include operator name in size/stride/alignment assertion (#152353)" 
This reverts commit 725bbb6b5f.

Reverted https://github.com/pytorch/pytorch/pull/152353 on behalf of https://github.com/jeanschmidt due to seems to have broken a few internal tests, @jansel may you help the author get his PR merged? ([comment](https://github.com/pytorch/pytorch/pull/152353#issuecomment-2885997862))
 2025-05-16 08:20:39 +00:00
Nikita Shulga
754b758ea1 [BE] Extend empty_gpu_cache to mps (#153657) 
And replace `if: elif:` with `getattr()`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153657
Approved by: https://github.com/atalman, https://github.com/wdvr, https://github.com/ZainRizvi
 2025-05-16 01:08:54 +00:00
Aaron Gokaslan
4f4ecc583e [BE]: Enable RUFF TRY400 rule - log.exception (#153473) 
Change logging.error to logging.exception to log additional information when relevant.  A few places have slipped in logging.errors in try except since I last did a clean up here and the rule is stabilized so I am enabling it codebase wide. I have NOQA'd much of our custom exception stack trace handling for RPC calls and distributed and tried to a fix a few errors based on whether we immediately reraised it or if we didn't print any exception handling where it could be useful.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153473
Approved by: https://github.com/albanD, https://github.com/cyyever
 2025-05-15 13:36:59 +00:00
Robert Burke
d2f6c6df1d unbreak fb:operator_benchmark_test (#152049) 
Summary: unbreak fb:operator_benchmark_test

Test Plan: works on my machine

Differential Revision: D73540912

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152049
Approved by: https://github.com/hl475
 2025-05-15 03:38:48 +00:00
karthickai
725bbb6b5f [inductor][dynamo] Include operator name in size/stride/alignment assertion (#152353) 
Fixes #151930

This PR updates the `assert_size_stride` and `assert_alignment` functions in [guards.cpp](https://github.com/pytorch/pytorch/blob/main/torch/csrc/dynamo/guards.cpp) to accept an optional `op_name` argument and includes it in the error messages.

The corresponding type stubs in [guards.pyi](https://github.com/pytorch/pytorch/blob/main/torch/_C/_dynamo/guards.pyi) are updated to match the new function arg.

In [inductor/ir.py](https://github.com/pytorch/pytorch/blob/main/torch/_inductor/ir.py) extracts the operator name from the FX graph and passes it into the `codegen_size_asserts` and `codegen_alignment_asserts` functions, so that generated assertions in Triton code include the op name for better debugging.

Added unit tests inside [test_torchinductor.py](https://github.com/pytorch/pytorch/blob/main/test/inductor/test_torchinductor.py).
- Verified both successful and failing assertion cases include the operator name.
- Verified that generated Triton code contains the op name inside the asserts.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152353
Approved by: https://github.com/jansel
 2025-05-15 02:33:57 +00:00
Animesh Jain
03d01860fd [dynamo][compile-time] Compute logging related flags once (#153426) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153426
Approved by: https://github.com/jansel
 2025-05-14 21:19:06 +00:00
Animesh Jain
8f3d7972ad [dynamo][compile-time] Cache the function signature to speedup inlining (#153396) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153396
Approved by: https://github.com/jansel, https://github.com/StrongerXi
ghstack dependencies: #153333
 2025-05-14 14:01:46 +00:00
Animesh Jain
864a5f4434 [dynamo][compile-time] Cache the cleaned insturctions while inlining (#153333) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153333
Approved by: https://github.com/StrongerXi, https://github.com/jansel, https://github.com/williamwen42
 2025-05-14 09:26:26 +00:00
Animesh Jain
11c64b7cf8 [dynamo][compile-time] Cache whether a function is inlineable (#153192) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153192
Approved by: https://github.com/StrongerXi, https://github.com/jansel, https://github.com/williamwen42
ghstack dependencies: #153458
 2025-05-14 05:40:25 +00:00
Benjamin Glass
e8596c291b Fix misleadingly high AOT Inductor dashboard performance (#153060) 
Fixes misleadingly high AOTInductor performance benchmark numbers in scenarios where a model updates internal parameters during `torch.export.export`. Since `FakeTensorMode` is enabled during export, all such parameters become `FakeTensor`s, slowing down future eager-mode runs using that model substantively. This, in turn, causes misleading performance stats, where the slowness of eager-mode makes `AOTInductor` look _very_ good.

An [example benchmark](https://hud.pytorch.org/benchmark/timm_models/inductor_aot_inductor?dashboard=torchinductor&startTime=Wed%2C%2030%20Apr%202025%2015%3A54%3A04%20GMT&stopTime=Wed%2C%2007%20May%202025%2015%3A54%3A04%20GMT&granularity=hour&mode=inference&dtype=bfloat16&deviceName=cuda%20(h100)&lBranch=main&lCommit=1dd36ad2d440a4f3faf724b3a8e13925e3180c24&rBranch=main&rCommit=cc7346bf19c019255dcb4484694a75850ed74d5a&model=convit_base) with this issue. The equivalent `cpp_wrapper` benchmark run shows a 2x performance gain, not 20x.

Only two benchmarks we regularly run are affected by this, both in the TIMM set.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153060
Approved by: https://github.com/desertfire
 2025-05-13 20:59:59 +00:00
Michael Lazos
ff039d39ec [Dynamo] Optimize dedupe region ancestor tracking (#152589) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152589
Approved by: https://github.com/anijain2305
ghstack dependencies: #152389, #152505, #152410, #152506, #152570, #152572
 2025-05-13 12:17:59 +00:00
Laith Sakka
c4fb0b6f33 refresh expected results (#150166) 
@huydhn when do you think we will have the APIs to access results on oss storage available so we do not
have to worry about this racing again?
Also is there a way to accelerate unstability in this after we land it?

Pull Request resolved: https://github.com/pytorch/pytorch/pull/150166
Approved by: https://github.com/bobrenjc93, https://github.com/eellison, https://github.com/anijain2305
 2025-05-13 04:04:42 +00:00
Aaron Gokaslan
3555ebb63d [BE]: Update ruff to 0.11.8 (#153249) 
Fixes a ton of false negatives throughout the codebase. RUFF also properly validates NOQA comments now and most of the changes are fixing typos there or removing filewide flake8 suppressions that were also silencing ruff issues.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153249
Approved by: https://github.com/cyyever, https://github.com/albanD, https://github.com/seemethere
 2025-05-12 18:30:52 +00:00
PyTorch MergeBot
aa7fe6af41 Revert "[Dynamo] Optimize dedupe region ancestor tracking (#152589)" 
This reverts commit b5f1345f72.

Reverted https://github.com/pytorch/pytorch/pull/152589 on behalf of https://github.com/jeanschmidt due to Breaking internal signal citadel-fbcode-test-mode-opt-for-pt2_stack_for_internal-linux-0 please see diff [D74531503](https://www.internalfb.com/diff/D74531503) for more details ([comment](https://github.com/pytorch/pytorch/pull/152410#issuecomment-2871168679))
 2025-05-12 07:15:09 +00:00
Michael Lazos
b5f1345f72 [Dynamo] Optimize dedupe region ancestor tracking (#152589) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152589
Approved by: https://github.com/anijain2305
ghstack dependencies: #152389, #152505, #152410, #152506, #152570, #152572
 2025-05-10 08:27:56 +00:00
Pian Pawakapan
d808a3e203 [dynamic shapes] guard_or_false for computeStorageNbytes (#150483) 
removes fast path for computing storage, fixes some adjacent tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/150483
Approved by: https://github.com/laithsakka
 2025-05-09 19:31:19 +00:00
Pian Pawakapan
8ea95d2e73 [inductor] dtype promotion error in cat decomp (#152995) 
cloning single tensor wasn't following dtype promotion rules
for SAM model: https://github.com/pytorch/pytorch/issues/152606

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152995
Approved by: https://github.com/yushangdi, https://github.com/eellison
 2025-05-09 16:58:58 +00:00
Animesh Jain
ab829ec629 [dynamo][pr_time_benchmark] Add dynamo benchmark to stress test inlining (#153159) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153159
Approved by: https://github.com/laithsakka
ghstack dependencies: #152883, #153105
 2025-05-09 00:09:19 +00:00
Pian Pawakapan
4166373908 [dynamic shapes] guard_or_false for infer_size (#152146) 
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152146
Approved by: https://github.com/laithsakka
 2025-05-08 21:27:22 +00:00
Animesh Jain
ecd74c953f [dynamo] Recursively realize the stack_values (#152853) 
Might also fix - https://github.com/pytorch/pytorch/issues/135696

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152853
Approved by: https://github.com/Lucaskabela, https://github.com/mlazos, https://github.com/jansel
 2025-05-07 02:36:44 +00:00
Aaron Gokaslan
07a29dbe81 [BE]: Update cutlass submodule to 3.9.2 (#152779) 
A lot of last minute bugfixes for CUTLASS blackwell that we should upstream. It's a header only library and a minor release so this should strictly improve compiler support and fix some bugs. Needed to update some instruction numbers in torch compile baselines for the new kernels

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152779
Approved by: https://github.com/henrylhtsang
 2025-05-06 16:08:24 +00:00
PyTorch MergeBot
fcd5e49138 Revert "[dynamo] Recursively realize the stack_values (#152853)" 
This reverts commit 460888f908.

Reverted https://github.com/pytorch/pytorch/pull/152853 on behalf of https://github.com/malfet due to Looks like it broke inductor tests ([comment](https://github.com/pytorch/pytorch/pull/152853#issuecomment-2854897485))
 2025-05-06 15:02:57 +00:00
Animesh Jain
460888f908 [dynamo] Recursively realize the stack_values (#152853) 
Might also fix - https://github.com/pytorch/pytorch/issues/135696

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152853
Approved by: https://github.com/Lucaskabela, https://github.com/mlazos, https://github.com/jansel
 2025-05-06 06:30:31 +00:00
cyy
45efa1aaa8 [3/N] Use internal linkage in C++ files (#151297) 
Follows #151070.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151297
Approved by: https://github.com/Skylion007
 2025-05-05 17:48:39 +00:00
rzou
2b37a726e0 Refactor layout constraint selection logic (#148104) 
This PR:

- cleans up some existing comments that don't make sense anymore
- hooks up the "custom_op_default_layout_constraint" back (that seems to
have broken)
- cleans up the "lazy registration path" which seems to never get hit
anymore
- adds dislike_padding to nodes that require exact strides

Test Plan:
- tests + CI

disable padding

Pull Request resolved: https://github.com/pytorch/pytorch/pull/148104
Approved by: https://github.com/shunting314, https://github.com/eellison
 2025-05-03 00:02:24 +00:00
rzou
64957db6c9 Fix some inductor periodic benchmarks (#152605) 
Some were reporting "pass" consistently on https://hud.pytorch.org/
Those are fine to flip.

I filed a separate issue for the now-regressions for AOTI:
https://github.com/pytorch/pytorch/issues/152606. These should be looked
at.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152605
Approved by: https://github.com/eellison, https://github.com/huydhn
 2025-05-01 22:18:30 +00:00
Jason Ansel
15a3f58f91 Return ConstantVariable(None) from WithExitFunctionVariable.exit to prevent NoneType crash inside autocast exception path (#152503) 
Copy of #152013 with PR time benchmarks updated (regressions seem unrelated)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152503
Approved by: https://github.com/anijain2305, https://github.com/Skylion007

Co-authored-by: Witold Dziurdz <wdziurdz@habana.ai>
 2025-05-01 04:01:24 +00:00
Huy Do
3f10091d3c Clean up conda usage in benchmark scripts (#152552) 
Fixes https://github.com/pytorch/pytorch/issues/152123.

* Switch `benchmarks/dynamo/Makefile` to use uv.  Note that these scripts are only used locally, so it's kind of ok to keep conda here IMO.  But switching to uv is probably nicer to most folks.
* Delete some files that are outdated and not used anymore

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152552
Approved by: https://github.com/atalman, https://github.com/albanD
 2025-04-30 21:27:29 +00:00
henrylhtsang
00ebbbb701 [cutlass backend] add addmm and bmm for cutlass backend benchmark (#152163) 
Copying what @kadeng did.

```
FINAL results...

Experiment group: bmm (BS: 8, 1024x1024, 1024x1024) torch.float16
+-----------------------+--------------------+----------------------+---------------------+
|         name          | forward_time (us)  | compilation_time (s) | perf_over_aten (%)  |
+-----------------------+--------------------+----------------------+---------------------+
|         aten          | 44.454172253608704 |  3.0991086587309837  |         NA          |
|        triton         | 44.06978189945221  | 0.07496077567338943  | -0.8646890374284049 |
| triton_persistent_tma | 43.598245829343796 | 0.06154991965740919  | -1.9254130284597197 |
|  cutlass_lvl_default  | 39.91834074258804  | 0.056073310784995556 | -10.20338762612423  |
+-----------------------+--------------------+----------------------+---------------------+

Experiment group: bmm (BS: 8, 1024x1024, 1024x1024) torch.bfloat16
+-----------------------+-------------------+----------------------+---------------------+
|         name          | forward_time (us) | compilation_time (s) | perf_over_aten (%)  |
+-----------------------+-------------------+----------------------+---------------------+
|         aten          | 49.05610531568527 |  0.160279156640172   |         NA          |
|        triton         | 43.97720843553543 |  0.0660805031657219  | -10.353241145961718 |
| triton_persistent_tma | 43.94153505563736 | 0.061738294549286366 | -10.425960697724962 |
|  cutlass_lvl_default  | 40.2066633105278  | 0.034127906896173954 | -18.039430460713596 |
+-----------------------+-------------------+----------------------+---------------------+

Average edge over aten (max(-edge, 0), higher is better):
triton: 5.608965091695062 (from 2 valid values)
triton_persistent_tma: 6.175686863092341 (from 2 valid values)
cutlass_lvl_default: 14.121409043418913 (from 2 valid values)
```

Differential Revision: [D73625766](https://our.internmc.facebook.com/intern/diff/D73625766/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152163
Approved by: https://github.com/jingsh
 2025-04-28 20:16:17 +00:00
Gabriel Ferns
ce00ec7ecf Enable max autotune for AOTInductor benchmark (#149309) 
With this PR, AOTinductor can choose to run into max-autotune mode when benchmarking.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/149309
Approved by: https://github.com/desertfire

Co-authored-by: Gabriel Ferns <gabeferns@meta.com>
 2025-04-28 06:54:26 +00:00
Anthony Shoumikhin
e2f9759bd0 Fix broken URLs (#152237) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152237
Approved by: https://github.com/huydhn, https://github.com/malfet
 2025-04-27 09:56:42 +00:00
Animesh Jain
3c1a17a08b [Dynamo] Use LazyVariableTracker in base VT (#151847) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151847
Approved by: https://github.com/StrongerXi
 2025-04-23 18:18:01 +00:00
Eddie Yan
dcc32ff5bf [CUDA][cuBLAS][cuBLASLt] Opt-in unified cuBLAS + cuBLASLt workspaces (#151163) 
opt-in version of https://github.com/pytorch/pytorch/pull/145130 as there was a lack of repro for the 70% forward issue
`TORCH_CUBLASLT_UNIFIED_WORKSPACE=1`

@izaitsevfb could you comment if it was repeatable per every forward pass, on startup, or something else?

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151163
Approved by: https://github.com/ngimel
 2025-04-23 15:24:22 +00:00
Laith Sakka
09e8ff92cc refresh benchmark results (#151622) 
updating due to <1.5% increases in https://github.com/pytorch/pytorch/pull/151469
not all benchmarks were updated

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151622
Approved by: https://github.com/oulgen
 2025-04-18 02:39:13 +00:00
Oguz Ulgen
ef64beb232 Include post grad gm and fx runnable in cache artifacts for tlparse (#151469) 
Fixed #151462

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151469
Approved by: https://github.com/bdhirsh
 2025-04-17 17:14:13 +00:00
PyTorch MergeBot
41b82611ee Revert "[Reopen] [Intel GPU] Set higher tolerance for some models only on XPU Device (#144756)" 
This reverts commit 300e0ee13c.

Reverted https://github.com/pytorch/pytorch/pull/144756 on behalf of https://github.com/malfet due to Broke rocm torch bench runs with  TypeError: unsupported operand type(s) for |: 'set' and 'list' ([comment](https://github.com/pytorch/pytorch/pull/144756#issuecomment-2812525970))
 2025-04-17 11:09:01 +00:00
Mao Yunfei
300e0ee13c [Reopen] [Intel GPU] Set higher tolerance for some models only on XPU Device (#144756) 
Reopen the previous stale closed PR https://github.com/pytorch/pytorch/pull/134192

We need to increase the tolerance slightly to ensure that certain models pass accuracy check on the XPU device.
This pull request preserves the original tolerance threshold for the CUDA device and introduces a new key higher_fp16_bf16_xpu, which only impacts the XPU device.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144756
Approved by: https://github.com/chuanqi129, https://github.com/EikanWang, https://github.com/desertfire
 2025-04-17 00:26:55 +00:00
Brian Hirsh
eea4a7b424 update expected results for comptime benchmark (#151319) 
This PR https://github.com/pytorch/pytorch/pull/150594 bumped the benchmark up by ~1%, a bit under our 1.5% "regression" mark.

Modeled this PR after https://github.com/pytorch/pytorch/pull/144274

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151319
Approved by: https://github.com/jamesjwu, https://github.com/laithsakka
 2025-04-15 19:40:13 +00:00
Shunting Zhang
8f440a8e70 don't return logits for benchmark script (#151075) 
PT2 benchmark scripts has a pattern like:
```
    def forward_and_backward_pass(self, mod, inputs, collect_outputs=True):
        cloned_inputs = clone_inputs(inputs)
        self.optimizer_zero_grad(mod)
        with self.autocast(**self.autocast_arg):
            pred = mod(**cloned_inputs)
            loss = self.compute_loss(pred)
        self.grad_scaler.scale(loss).backward()
        self.optimizer_step()
        if collect_outputs:
            return collect_results(mod, pred, loss, cloned_inputs)
        return None
```
for training.

The collect_outputs argument is True only for accuracy testing and it's false for performance testing.

For HF benchmark suite, a model usually returns tuple (loss, logits). For performance testing, even though the logits is never used anywhere, dynamo has to keep it due to the control flow.

A few bad things if we keep logits here
1. the peak memory will be higher since the logits is large and we can not release its memory earlier.
2. we can not do optimization like chunking for the logits because the tensor needs to be returned from the pre-grad graph

Actually I think it's fine to not return logits at all.
- For training cases, checking loss and gradients for accuracy is good enough. It's hard to see two runs have mismatch logits but matching loss/gradients.
- Also, discarding logits as soon as possible for perf benchmarking makes it more fair for us.

On the other hand, it may be interesting to let dynamo support something like dynamo.constexpr (similar to tl.constexpr). A variable annotated as dynamo.constexpr will be specialized at compile time and we can do more optimization (DCE e.g.) at compile time. (A small [repro](https://gist.github.com/shunting314/0912a8947028a904c34f361021b8024d))

Benchmark results here [link](https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Fri%2C%2004%20Apr%202025%2018%3A03%3A26%20GMT&stopTime=Fri%2C%2011%20Apr%202025%2018%3A03%3A26%20GMT&granularity=hour&mode=training&dtype=amp&deviceName=cuda%20(h100)&lBranch=gh/shunting314/204/head&lCommit=fe25dab3f65e1b0e9db0af03f7664af70fcc9c66&rBranch=main&rCommit=55e62ff74ad5614faf80b060c7bfc551e3b7af5a)
- HF 15% (1.51 -> 1.66 compression ratio) peak memory improvement
- I also see 5% (2.74 -> 2.79x) perf win for HF. It could be true. We may generate more efficient kernels since we don't need keep logits and return it from the pre-grad graph. But I'll double check

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151075
Approved by: https://github.com/eellison, https://github.com/jansel
 2025-04-15 17:13:00 +00:00
Zhang, Jianyi
a756c50315 [Intel GPU] Avoid using fp32 in sdp math path when benchmark performance. (#150996) 
sdp on xpu will fallback to math path in some cases (i.e. training). In dynamo benchmark, we prefer to use fp16 for better performance. Although `allow_fp16_bf16_reduction_math_sdp` is under backends.cuda, its implementation is for all device.

I didn't add if device == xpu here, I suppose cuda devices will not run into math path anyway

Pull Request resolved: https://github.com/pytorch/pytorch/pull/150996
Approved by: https://github.com/drisspg, https://github.com/EikanWang
 2025-04-15 08:08:01 +00:00
henrylhtsang
5a51de5ab1 [cutlass backend] Add more logs for cutlass backend benchmark (#150639) 
Goal is to have a way to compare if a change make it better or worse.

```
Average edge over aten (max(-edge, 0), higher is better):
triton: 8.596507086950552 (from 6 valid values)
triton_persistent_tma: 9.517193693923307 (from 6 valid values)
cutlass_lvl_default: 3.3234737908691785 (from 6 valid values)
cutlass_lvl_1111: 7.088173348313991 (from 6 valid values)
cutlass_lvl_2222: 7.291869722320318 (from 6 valid values)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150639
Approved by: https://github.com/ColinPeppler
 2025-04-15 04:19:51 +00:00
Animesh Jain
7b1a2373e8 [dynamo][super variable] Fix bug to use correct source (#151154) 
Fixes https://github.com/pytorch/pytorch/issues/150994

We should cherry-pick to 2.7 branch if possible, because this breaks torch.compile on some HF models. Look at the issue referenced here.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151154
Approved by: https://github.com/jansel
 2025-04-13 04:48:52 +00:00
PyTorch MergeBot
67d3053d4b Revert "update benchamark result due to <1% regression (#150937)" 
This reverts commit 860765d621.

Reverted https://github.com/pytorch/pytorch/pull/150937 on behalf of https://github.com/laithsakka due to regression diff reverted ([comment](https://github.com/pytorch/pytorch/pull/150937#issuecomment-2797611127))
 2025-04-11 17:36:47 +00:00
Laith Sakka
91d1826539 Add dynamic version for mm_loop benchmark (#150865) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150865
Approved by: https://github.com/eellison
 2025-04-09 23:37:43 +00:00
Laith Sakka
860765d621 update benchamark result due to <1% regression (#150937) 
<img width="1503" alt="Screenshot 2025-04-09 at 9 07 13 AM" src="https://github.com/user-attachments/assets/e16f31b0-c5dc-4dd6-8adb-aac11ed988db" />

PR https://hud.pytorch.org/pr/148104
which is acceptable but we have to update this to avoid  flakiness in the future .

Pull Request resolved: https://github.com/pytorch/pytorch/pull/150937
Approved by: https://github.com/zou3519
 2025-04-09 20:25:48 +00:00
Bin Bao
6a8ab902a2 [AOTI][dashboard] Fix mis-calculated memory compression ratio (#150695) 
Summary: https://github.com/pytorch/pytorch/pull/149817 introduced an extra warmup run to compute AOTI memory compression ratio, but since weights are only loaded once in the AOTI run, the peak memory seen in the extra warmup won't include the weight, which causes an aritifically high memory compression ratio. This PR removes that extra warmup run, and calls reset_peak_memory_stats in the proper place instead.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/150695
Approved by: https://github.com/yushangdi
 2025-04-06 19:51:22 +00:00
Laith Sakka
3320efef6b Refresh expected results. (#150264) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150264
Approved by: https://github.com/bobrenjc93
 2025-04-05 01:11:19 +00:00