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Author SHA1 Message Date
jmaczan
cff1b20771 Patch the flex_attention._get_mod_type to not use inspect.signature when computing num_positional_args (an alternative fix for flex attention graph break on create_block_mask) (#164923) 
The initial fix for inspect.signature uses not a right approach (https://github.com/pytorch/pytorch/pull/164349#pullrequestreview-3306614010). As @williamwen42 suggests (https://github.com/pytorch/pytorch/pull/164349#issuecomment-3379222885) we can just for now get rid of `inspect.signature` call in flex_attention to resolve this high priority issue (https://github.com/pytorch/pytorch/issues/164247#issuecomment-3378673179). In this PR I did exactly this - limited the scope of fix to just computing `num_positional_args` in `flex_attention._get_mod_type` based on properties returned by `NestedUserFunctionVariable.const_getattr` (some were missing so I added them)

Fixes #164247

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164923
Approved by: https://github.com/williamwen42
trunk/cff1b207717b84b6ac3fdc95fc5ac91cc3802b63 		2025-10-17 17:44:45 +00:00
Jeff Daily
da8517fa63 [ROCm][CI] upgrade wheels to 7.0.2 and 6.4.4 patch release (#165756) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165756
Approved by: https://github.com/jeffdaily

Co-authored-by: Jeff Daily <jeff.daily@amd.com>
trunk/da8517fa634e9922e3299e14b86428bcbf2b373d 		2025-10-17 17:41:19 +00:00
Pian Pawakapan
45afaf08a1 [DebugMode][2/N] add nn.Module tracking (#165498) 
Uses ModTracker to record nn.Module entries, much like CommDebugMode.

Can be switched on with `DebugMode(record_nn_module=True)`:
```
    [nn.Mod] Bar
      [nn.Mod] Bar.abc
        [nn.Mod] Bar.abc.l1
          aten::t(t: f32[4, 4])
          aten::addmm(t: f32[4], t: f32[4, 4], t: f32[4, 4])
        [nn.Mod] Bar.abc.l2
          aten::t(t: f32[4, 4])
          aten::addmm(t: f32[4], t: f32[4, 4], t: f32[4, 4])
      [nn.Mod] Bar.xyz
        aten::t(t: f32[4, 4])
        aten::addmm(t: f32[4], t: f32[4, 4], t: f32[4, 4])"""
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165498
Approved by: https://github.com/SherlockNoMad
ghstack dependencies: #165376
trunk/45afaf08a14ab760d86ea80dea6d50cec8626513 		2025-10-17 17:39:48 +00:00
Turner Richmond
080365b7d8 Escaped html tags name and target to appear as strings (#165543) 
Fixes small typo in markdown documentation file - Added escape characters to precede tag pattern.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165543
Approved by: https://github.com/mikaylagawarecki
trunk/080365b7d82a3c99c995cab6dc912b7dfe22aa41 		2025-10-17 17:35:18 +00:00
PyTorch MergeBot
2928c5c572 Revert "Pyrefly suppressions 2 (#165692)" 
This reverts commit 43d78423ac224cce432bf34ed9627035169d5433.

Reverted https://github.com/pytorch/pytorch/pull/165692 on behalf of https://github.com/seemethere due to This is causing merge conflicts when attempting to land internally, see D84890919 for more details ([comment](https://github.com/pytorch/pytorch/pull/165692#issuecomment-3416397240))
trunk/2928c5c5724bec7da91f5a3b24bbd15d5658a0cc 		2025-10-17 17:13:04 +00:00
Animesh Jain
630520b346 [dynamo][misc] Replace UserFunctionVariable with VariableTracker build (#165707) 
Audit: To prevent future issues with functools.partial or callable
objects.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165707
Approved by: https://github.com/Lucaskabela
ghstack dependencies: #165683, #165706
trunk/630520b346b8883db7821562e589ccde7d12687a viable/strict/1760736251 		2025-10-17 17:02:18 +00:00
Animesh Jain
1dc9a05d03 [dynamo][user_defined] Replace UserFunctionVariable with VariableTracker build (#165706) 
Audit: To prevent future issues with functools.partial or callable
objects.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165706
Approved by: https://github.com/Lucaskabela
ghstack dependencies: #165683
 2025-10-17 17:02:18 +00:00
Han, Xu
bfcdbd0a97 fix wrong accuracy_status when exception. (#165731) 
When I debug `XPU` accruacy issue, I found the script output wrong accuracy_status.
When the `try` block raise an exception, we should process the exception, but not return the `fail_accuracy`.

Before fixing, it returned as `fail_accuracy`:
<img width="1109" height="216" alt="image" src="https://github.com/user-attachments/assets/385c354f-fbf6-48e4-a1be-3e37e987341b" />

After fixing, it returned the exception message:
<img width="1101" height="292" alt="image" src="https://github.com/user-attachments/assets/f18c0e3c-8358-4ec7-a6bb-c2e01b69d27f" />

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165731
Approved by: https://github.com/Stonepia, https://github.com/chuanqi129, https://github.com/Lucaskabela
trunk/bfcdbd0a970e5ce08cecd0aa33dd389819f0ec4f 		2025-10-17 16:37:06 +00:00
PyTorch MergeBot
faff826a46 Revert "[ROCm] new implementation of upsample_bilinear2d_backward (#164572)" 
This reverts commit 53f9ae0e50d4dcc47f2ca4bf854803f9d4f875ae.

Reverted https://github.com/pytorch/pytorch/pull/164572 on behalf of https://github.com/seemethere due to Looks like this is failing in our internal builds, will post a suggestion for a fix but want you to double verify that this behavior is correct ([comment](https://github.com/pytorch/pytorch/pull/164572#issuecomment-3416262676))
trunk/faff826a46c1569eb1c94b0a02299578d1f0e715 viable/strict/1760734180 		2025-10-17 16:27:59 +00:00
PyTorch MergeBot
85c5433d38 Revert "Fix _StridedShard incorrect split (#165533)" 
This reverts commit dfc8a1c5ddc8401197e9ab546e03b0f745edc27b.

Reverted https://github.com/pytorch/pytorch/pull/165533 on behalf of https://github.com/seemethere due to Causing a merge conflict internally, see D84829161 ([comment](https://github.com/pytorch/pytorch/pull/165533#issuecomment-3416143176))
trunk/85c5433d38146dbb30ee410c45fc875ea70b673f viable/strict/1760732726 		2025-10-17 15:57:01 +00:00
inventshah
935ccdbe75 [MPS] Fix internal assertion in torch.linalg.solve for singular matrices (#165254) 
Fixes #163962 by special casing MPS in the negative status code branch in `_linalg_check_errors`.

Checks if info is [`MPSMatrixDecompositionStatus.singular`](https://developer.apple.com/documentation/metalperformanceshaders/mpsmatrixdecompositionstatus/singular) (which has a raw value of -2). I didn't find an official Apple source with this raw value (besides printing the enum value), so I'm not sure if we can (or should) depend on it? Is there a way to directly get the Objective-C enum value in C++?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165254
Approved by: https://github.com/malfet
trunk/935ccdbe75c9c24c63a1131fecb119fc2eb441f3 viable/strict/1760730578 		2025-10-17 15:35:49 +00:00
Isuru Fernando
3af2f0c12a [inductor] require shape in TritonCSEVariable (#162275) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162275
Approved by: https://github.com/mlazos
ghstack dependencies: #164158
trunk/3af2f0c12accc6bd10ef2b76fb5c51aa0f6b73a3 viable/strict/1760727234 		2025-10-17 14:47:45 +00:00
Nikita Shulga
6ece527fc5 [CI] Add aarch64 operator benchmark (#165585) 
Running on Graviton4
Skip ConvTranspose1d benchmarks if PyTorch is compiled with ACL, due to https://github.com/pytorch/pytorch/issues/165654
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165585
Approved by: https://github.com/huydhn
trunk/6ece527fc5b9fa35a210f410e73a0a65d8f98e5d 		2025-10-17 14:42:14 +00:00
PaulZhang12
ce29d0d796 [ATen] Vectorize 8 elements on 16 bit data types for sum/mean (#165055) 
Benchmarks for a full reduction + reduction on the contiguous dimension. Vectorized loads do not occur on the non contiguous dimension. Benchmarking done for FP16/BF16, ~6% improvement on average across shapes, up to ~24% for single reduction on contiguous dimension and 46% for full reduce:
**BF16**
```
Tensor Shape         Operation    Full reduce (ms)     Contiguous dim (ms)  Full reduce (ms)     Contiguous dim (ms)  Full reduce diff %   Contiguous diff %
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(256, 256)           mean         0.022686             0.008263             0.015498             0.008117                          +46.38%               +1.80%
(256, 256)           sum          0.022769             0.008269             0.015628             0.008185                          +45.69%               +1.03%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(512, 512)           mean         0.014116             0.009545             0.012892             0.008839                           +9.49%               +7.99%
(512, 512)           sum          0.014110             0.009892             0.012891             0.008878                           +9.46%              +11.42%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 1024)         mean         0.014727             0.012642             0.014061             0.010519                           +4.74%              +20.18%
(1024, 1024)         sum          0.014376             0.012636             0.014069             0.010595                           +2.18%              +19.26%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(2048, 2048)         mean         0.018663             0.018294             0.018171             0.014678                           +2.71%              +24.64%
(2048, 2048)         sum          0.018638             0.017931             0.018142             0.014713                           +2.73%              +21.87%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(4096, 4096)         mean         0.034216             0.036953             0.033520             0.030585                           +2.08%              +20.82%
(4096, 4096)         sum          0.034196             0.036942             0.033518             0.030676                           +2.02%              +20.43%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 8192)         mean         0.087763             0.095201             0.085439             0.084960                           +2.72%              +12.05%
(8192, 8192)         sum          0.088079             0.095592             0.085353             0.084632                           +3.19%              +12.95%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 16384)        mean         0.148174             0.149705             0.146274             0.138865                           +1.30%               +7.81%
(8192, 16384)        sum          0.147820             0.149371             0.146419             0.138752                           +0.96%               +7.65%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 32768)        mean         0.266144             0.260807             0.265953             0.253330                           +0.07%               +2.95%
(8192, 32768)        sum          0.266572             0.261163             0.265729             0.253294                           +0.32%               +3.11%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 65536)        mean         0.502034             0.486312             0.498417             0.481246                           +0.73%               +1.05%
(8192, 65536)        sum          0.501597             0.486351             0.497735             0.481579                           +0.78%               +0.99%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 131072)       mean         0.971178             0.942988             0.957164             0.938316                           +1.46%               +0.50%
(8192, 131072)       sum          0.971189             0.943232             0.956814             0.937816                           +1.50%               +0.58%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 262144)       mean         1.953728             1.877648             1.904937             1.861692                           +2.56%               +0.86%
(8192, 262144)       sum          1.953969             1.877538             1.905990             1.862547                           +2.52%               +0.80%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(4096, 262144)       mean         0.970408             0.940965             0.957871             0.936732                           +1.31%               +0.45%
(4096, 262144)       sum          0.970919             0.941652             0.957765             0.936676                           +1.37%               +0.53%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(2048, 262144)       mean         0.501477             0.486976             0.497964             0.483570                           +0.71%               +0.70%
(2048, 262144)       sum          0.501955             0.487213             0.498210             0.483218                           +0.75%               +0.83%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 262144)       mean         0.266536             0.257111             0.265642             0.255439                           +0.34%               +0.65%
(1024, 262144)       sum          0.266613             0.257096             0.265427             0.255472                           +0.45%               +0.64%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(512, 131072)        mean         0.087805             0.091200             0.085818             0.087851                           +2.32%               +3.81%
(512, 131072)        sum          0.087788             0.091249             0.085373             0.087944                           +2.83%               +3.76%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1000, 1000)         mean         0.014503             0.012328             0.013663             0.010190                           +6.15%              +20.98%
(1000, 1000)         sum          0.014545             0.012378             0.013662             0.010579                           +6.46%              +17.01%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 129)          mean         0.014163             0.008371             0.012893             0.008828                           +9.85%               -5.18%
(1024, 129)          sum          0.014132             0.008751             0.013234             0.008868                           +6.79%               -1.32%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 257)          mean         0.014296             0.009101             0.013334             0.008563                           +7.21%               +6.28%
(1024, 257)          sum          0.014302             0.009058             0.013020             0.008672                           +9.85%               +4.45%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 587)          mean         0.014127             0.010997             0.013443             0.009944                           +5.09%              +10.59%
(1024, 587)          sum          0.014471             0.011373             0.013123             0.010354                          +10.27%               +9.84%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(2048, 977)          mean         0.015607             0.013566             0.015089             0.012152                           +3.43%              +11.64%
(2048, 977)          sum          0.015953             0.013580             0.015039             0.011861                           +6.08%              +14.49%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 128)          mean         0.013982             0.008058             0.012747             0.008139                           +9.69%               -1.00%
(1024, 128)          sum          0.013967             0.008071             0.012726             0.007859                           +9.75%               +2.70%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 128)          mean         0.014378             0.009627             0.013712             0.009395                           +4.86%               +2.47%
(8192, 128)          sum          0.014389             0.009965             0.013718             0.009521                           +4.89%               +4.66%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 130)          mean         0.014156             0.008267             0.012895             0.008833                           +9.78%               -6.41%
(1024, 130)          sum          0.013797             0.008277             0.012903             0.008512                           +6.93%               -2.76%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 130)          mean         0.014977             0.010026             0.013911             0.009876                           +7.66%               +1.52%
(8192, 130)          sum          0.014994             0.010043             0.014235             0.009604                           +5.33%               +4.57%
====================================================================================================================================================================================
```

**FP16**
```
Tensor Shape         Operation    Full reduce (ms)     Contiguous dim (ms)  Full reduce (ms)     Contiguous dim (ms)  Full reduce diff %   Contiguous diff %
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(256, 256)           mean         0.022804             0.008298             0.015888             0.007848                          +43.53%               +5.73%
(256, 256)           sum          0.023215             0.008328             0.015677             0.007850                          +48.08%               +6.09%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(512, 512)           mean         0.013777             0.009988             0.012884             0.008512                           +6.93%              +17.34%
(512, 512)           sum          0.013775             0.009622             0.012870             0.009028                           +7.03%               +6.58%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 1024)         mean         0.014740             0.012322             0.013708             0.010239                           +7.53%              +20.34%
(1024, 1024)         sum          0.014762             0.012756             0.013722             0.010307                           +7.58%              +23.76%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(2048, 2048)         mean         0.018700             0.018364             0.018135             0.015078                           +3.12%              +21.79%
(2048, 2048)         sum          0.018276             0.018415             0.018471             0.015127                           -1.06%              +21.74%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(4096, 4096)         mean         0.034518             0.037000             0.033838             0.030617                           +2.01%              +20.85%
(4096, 4096)         sum          0.034569             0.037448             0.033842             0.031100                           +2.15%              +20.41%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 8192)         mean         0.087675             0.095176             0.085328             0.084105                           +2.75%              +13.16%
(8192, 8192)         sum          0.088102             0.095211             0.085707             0.084090                           +2.79%              +13.23%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 16384)        mean         0.147800             0.149263             0.146388             0.138390                           +0.96%               +7.86%
(8192, 16384)        sum          0.148147             0.148957             0.146439             0.138801                           +1.17%               +7.32%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 32768)        mean         0.266316             0.260294             0.265829             0.253411                           +0.18%               +2.72%
(8192, 32768)        sum          0.266562             0.260717             0.265744             0.253308                           +0.31%               +2.92%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 65536)        mean         0.502035             0.486077             0.498139             0.481374                           +0.78%               +0.98%
(8192, 65536)        sum          0.501571             0.485733             0.498353             0.481350                           +0.65%               +0.91%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 131072)       mean         0.971343             0.943016             0.956600             0.938622                           +1.54%               +0.47%
(8192, 131072)       sum          0.971463             0.942991             0.957352             0.938334                           +1.47%               +0.50%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 262144)       mean         1.952722             1.877165             1.906406             1.861455                           +2.43%               +0.84%
(8192, 262144)       sum          1.952634             1.876388             1.904677             1.861282                           +2.52%               +0.81%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(4096, 262144)       mean         0.970697             0.941298             0.956964             0.936160                           +1.44%               +0.55%
(4096, 262144)       sum          0.969981             0.941078             0.957016             0.936260                           +1.35%               +0.51%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(2048, 262144)       mean         0.501577             0.487208             0.498422             0.483493                           +0.63%               +0.77%
(2048, 262144)       sum          0.502029             0.487124             0.497854             0.483643                           +0.84%               +0.72%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 262144)       mean         0.266416             0.257383             0.265928             0.255140                           +0.18%               +0.88%
(1024, 262144)       sum          0.266434             0.257081             0.265817             0.255143                           +0.23%               +0.76%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(512, 131072)        mean         0.087858             0.091296             0.085816             0.087745                           +2.38%               +4.05%
(512, 131072)        sum          0.088144             0.091314             0.085664             0.087864                           +2.90%               +3.93%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1000, 1000)         mean         0.014977             0.012393             0.014141             0.010614                           +5.91%              +16.76%
(1000, 1000)         sum          0.014589             0.012804             0.014118             0.010320                           +3.34%              +24.07%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 129)          mean         0.014208             0.008383             0.013273             0.008440                           +7.04%               -0.68%
(1024, 129)          sum          0.013804             0.008863             0.013265             0.009003                           +4.06%               -1.56%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 257)          mean         0.014378             0.009109             0.013037             0.009038                          +10.29%               +0.79%
(1024, 257)          sum          0.014387             0.009113             0.013396             0.008698                           +7.40%               +4.77%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 587)          mean         0.014207             0.011037             0.013182             0.010391                           +7.78%               +6.22%
(1024, 587)          sum          0.014588             0.011453             0.013539             0.010049                           +7.75%              +13.97%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(2048, 977)          mean         0.016024             0.013614             0.015448             0.011845                           +3.73%              +14.93%
(2048, 977)          sum          0.015990             0.014033             0.015406             0.012278                           +3.79%              +14.29%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 128)          mean         0.014037             0.007804             0.013143             0.008242                           +6.80%               -5.31%
(1024, 128)          sum          0.014041             0.007847             0.012759             0.007850                          +10.05%               -0.04%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 128)          mean         0.014361             0.009644             0.014075             0.009061                           +2.03%               +6.43%
(8192, 128)          sum          0.014366             0.010032             0.013702             0.009181                           +4.85%               +9.27%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(1024, 130)          mean         0.014226             0.008696             0.012894             0.008835                          +10.33%               -1.57%
(1024, 130)          sum          0.013830             0.008740             0.013288             0.008989                           +4.08%               -2.77%
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(8192, 130)          mean         0.015036             0.010019             0.013917             0.009538                           +8.04%               +5.04%
(8192, 130)          sum          0.014652             0.010403             0.013900             0.009565                           +5.41%               +8.76%
====================================================================================================================================================================================
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165055
Approved by: https://github.com/ngimel
ghstack dependencies: #165494, #164790
trunk/ce29d0d796df40f484884e7b8db8b60567dcd95b viable/strict/1760723515 		2025-10-17 13:39:36 +00:00
Yuanyuan Chen
7231118db3 Turn some const variables into constexpr in C++ code (#165401) 
This PR checks the C++ code and turns some const variables into constexpr.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165401
Approved by: https://github.com/Skylion007
trunk/7231118db3156de661fa76fb0ccc91ecfdbc1416 		2025-10-17 13:24:46 +00:00
PyTorch MergeBot
5d4da26ed0 Revert "[export] preserve_node_meta by default (#165524)" 
This reverts commit fdd560afd1d413a9f814cbf7cc2a72e0d39b0117.

Reverted https://github.com/pytorch/pytorch/pull/165524 on behalf of https://github.com/lw due to test/functorch/test_control_flow.py::TestControlFlowTraced::test_cond_symint_closure [GH job link](https://github.com/pytorch/pytorch/actions/runs/18586312291/job/52991654051) [HUD commit link](fdd560afd1) ([comment](https://github.com/pytorch/pytorch/pull/165524#issuecomment-3415352522))
trunk/5d4da26ed067d2d70102f30967f1b09f8fb7018a 		2025-10-17 12:27:17 +00:00
PyTorch MergeBot
574c9fc950 Revert "Remove torch.serialization entries from the doc ignore list (#160224)" 
This reverts commit 9fe3b2afbeff12080b483af1ee23e1c9d9fb0421.

Reverted https://github.com/pytorch/pytorch/pull/160224 on behalf of https://github.com/lw due to [GH job link](https://github.com/pytorch/pytorch/actions/runs/18588004962/job/52997748336) [HUD commit link](9fe3b2afbe) ([comment](https://github.com/pytorch/pytorch/pull/160224#issuecomment-3415345175))
trunk/574c9fc9503e55f512693eedc52ac627e4330bb6 		2025-10-17 12:24:08 +00:00
PyTorch MergeBot
80d2ca7566 Revert "[annotate] add annotate_fn function decorator (#165703)" 
This reverts commit f1d882212afc3a73ce1e319d80b6406f9dc4a0c8.

Reverted https://github.com/pytorch/pytorch/pull/165703 on behalf of https://github.com/lw due to [GH job link](https://github.com/pytorch/pytorch/actions/runs/18585518705/job/52989521797) [HUD commit link](f1d882212a) ([comment](https://github.com/pytorch/pytorch/pull/165703#issuecomment-3415073467))
trunk/80d2ca7566cc38e68b964c1ce168b9320ed8e006 		2025-10-17 11:23:13 +00:00
Nikita Shulga
4a22139eea [MPS][BE] Fix unused variable warning (#165726) 
Namely this one
```
/Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/mps/kernels/Shape.metal:19:18: warning: unused variable 'output_sizes' [-Wunused-variable]
  constant auto& output_sizes = shared_params.output_sizes;
                 ^
/Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/mps/kernels/Shape.metal:85:1: note: in instantiation of function template specialization 'cat<long, float, float>' requested here
REGISTER_CAT_FOR_INDEX_TYPE(int64_t);
^
/Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/mps/kernels/Shape.metal:69:3: note: expanded from macro 'REGISTER_CAT_FOR_INDEX_TYPE'
  REGISTER_CAT_OP_ALL_INPUT_TYPES(I, float);  \
  ^
/Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/mps/kernels/Shape.metal:55:3: note: expanded from macro 'REGISTER_CAT_OP_ALL_INPUT_TYPES'
  REGISTER_CAT_OP(I, float, T_out);               \
  ^
/Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/mps/kernels/Shape.metal:47:15: note: expanded from macro 'REGISTER_CAT_OP'
  kernel void cat<I, T_in, T_out>(                               \
```

Repeated about 20-30 times
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165726
Approved by: https://github.com/Skylion007
trunk/4a22139eeaa136c25461d87ee025714442d565ad 		2025-10-17 11:16:21 +00:00
Simon Layton
cb6e4d7d82 User-passed alpha to scaled_gemm (#165563) 
Summary:

Add optional user-passed `alpha` argument to
`at::cuda::blas::scaled_gemm`, necessary for two-level-scaled NVFP4 gemm
calls (where the global de-scales are folded into the `alpha` argument.

Global de-scales are naturally device tensors, but using cublas'
device-pointer mode for `alpha`/`beta` has an interesting lifetime
implication - the `alpha` tensor must be valid & correct until the end
of the matmul call, *not* just the launch (as for host values). To
enable this, I added device-constant memory for `one` and `zero`, along
with a statically-held single-fp32-value tensor, which is valid from the
first passed-`alpha` invocation of `scaled_gemm` to the end of the
program. User-passed values are copied into this perpetual buffer to
ensure lifetime requirements are met.

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:
Signed-off-by: Simon Layton <simonlayton@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165563
Approved by: https://github.com/drisspg, https://github.com/eqy
trunk/cb6e4d7d825dfb23e4c4ff2547150cec6273048c 		2025-10-17 09:42:33 +00:00
Jerry Mannil
202f83dc4e [ROCm][layer_norm] Use __builtin_amdgcn_rcpf(x) instead of 1.f/x (#165589) 
Replace (more) exact calculation with hardware approximation.

Benefits:
Reduced code size.
Improved performance for certain scenarios.

Experiments show low reduction in precision.
Experiments show no significant performance regressions. bfloat16 as well as float16 related calculations may benefit largely from this change.

Co-author: @mhalk @amd-hhashemi

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165589
Approved by: https://github.com/jeffdaily
trunk/202f83dc4ed9a2fcc7ea43fef61fbcad0c2ee987 		2025-10-17 09:12:30 +00:00
Joel Schlosser
9fe3b2afbe Remove torch.serialization entries from the doc ignore list (#160224) 
Follows the approach done in #158581
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160224
Approved by: https://github.com/janeyx99
trunk/9fe3b2afbeff12080b483af1ee23e1c9d9fb0421 		2025-10-17 09:06:09 +00:00
Xilun Wu
d0c24b392c [APF Logging][Error Trait] To fill the errorTraits for ChildFailedError with signal abort (re-attempt of #165476) (#165688) 
**Summary**
Land @guoding83128 's PR https://github.com/pytorch/pytorch/pull/165476 on his behalf due to EasyCLA blocking.
Refer his original PR for detail. But in short, elastic leaves 'errorTraits' as unknown when the error dump file is missing,
this PR adds a "system terminated error" to such case so the internal scuba table can correctly aggregate.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165688
Approved by: https://github.com/fduwjj
trunk/d0c24b392cbb7b213d22e42c52c6c2d1ac2da1bd 		2025-10-17 08:23:27 +00:00
Yu, Guangye
b44fb14906 Remove unused parameter when query extension attribute (#165623) 
# Motivation
This code is no longer needed since SYCL compiler 2025.0. We are now using compiler 2025.2 (two tool uplifts later), so it can be safely removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165623
Approved by: https://github.com/EikanWang
ghstack dependencies: #165622
trunk/b44fb149069b44bb043f4b3374d08676c3f40635 		2025-10-17 08:16:13 +00:00
Yu, Guangye
51348c0219 Give a friendly message for older Intel GPU (#165622) 
# Motivation
Notify the user if the GPU is older than officially supported. This provides a friendly warning that the GPU may work, but the experience could be unstable.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165622
Approved by: https://github.com/EikanWang
 2025-10-17 08:16:13 +00:00
Pian Pawakapan
fdd560afd1 [export] preserve_node_meta by default (#165524) 
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165524
Approved by: https://github.com/malaybag
trunk/fdd560afd1d413a9f814cbf7cc2a72e0d39b0117 		2025-10-17 07:55:28 +00:00
Yuanyuan Chen
e925dfcc6b Enable all SIM rules except disabled ones (#164645) 
`SIM` rules are useful for simplifying boolean expressions and enhances code readability.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164645
Approved by: https://github.com/ezyang, https://github.com/mlazos
trunk/e925dfcc6b4fd76d744d04ecaa451fc2936155a8 		2025-10-17 07:27:11 +00:00
Shangdi Yu
f1d882212a [annotate] add annotate_fn function decorator (#165703) 
Example usage:

```
        @fx_traceback.annotate_fn({"pp_stage": 1})
        def example_function(x):
            return x * x

        class SimpleLinear(nn.Module):
            def __init__(self):
                super().__init__()
                self.linear = nn.Linear(3, 2)

            def forward(self, x):
                with fx_traceback.annotate({"pp_stage": 0}):
                    y = self.linear(x)
                y = example_function(y)
                return y - 1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165703
Approved by: https://github.com/SherlockNoMad
trunk/f1d882212afc3a73ce1e319d80b6406f9dc4a0c8 		2025-10-17 07:18:47 +00:00
Animesh Jain
24879f0de9 [dynamo] Use Variable Builder to build the property fget object (#165683) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165683
Approved by: https://github.com/ezyang, https://github.com/williamwen42
trunk/24879f0de97e0caaafa083ddc5ee28d6079fb1c0 		2025-10-17 06:29:24 +00:00
PyTorch MergeBot
9e94ec76b8 Revert "Turn some const variables into constexpr in C++ code (#165401)" 
This reverts commit 5b2afe4c5dc87786ca65bf22ca9a78f7c21a33a4.

Reverted https://github.com/pytorch/pytorch/pull/165401 on behalf of https://github.com/seemethere due to This is breaking test/distributions/test_distributions.py::TestDistributions::test_binomial_sample on HUD, see 5b2afe4c5d ([comment](https://github.com/pytorch/pytorch/pull/165401#issuecomment-3414023134))
viable/strict/1760696731 trunk/9e94ec76b8b29812a1c9dcbb46f00b44e8c3719d 		2025-10-17 06:14:09 +00:00
Richard Barnes
364624e209 [codemod][lowrisk] Remove unused exception parameter from some files (#165700) 
Summary:
`-Wunused-exception-parameter` has identified an unused exception parameter. This diff removes it.

This:
```
try {
    ...
} catch (exception& e) {
    // no use of e
}
```
should instead be written as
```
} catch (exception&) {
```

If the code compiles, this is safe to land.

Test Plan: Sandcastle

Differential Revision: D84868162

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165700
Approved by: https://github.com/Skylion007
trunk/364624e2091749d34aecbad843262643ad9a366f 		2025-10-17 05:30:06 +00:00
Tushar Jain
7e150467f7 allow providing full fr trace path (#165639) 
Summary:
- allow users to specify the full path instead of fr suffixing the rank id
- this will be used by torchft to provide the global rank id accross all replicas
- we can't just prefix the replica id because analysis tool expects the file name to provide a unique integer

---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/pytorch/pytorch/pull/165639).
* #165638
* #165640
* #165677
* #165642
* __->__ #165639

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165639
Approved by: https://github.com/fduwjj
trunk/7e150467f753360277c00585e4e689f91f3aef63 		2025-10-17 04:43:44 +00:00
Maggie Moss
43d78423ac Pyrefly suppressions 2 (#165692) 
This is the last directory to opt in for the regular mypy.ini file. Will put up a diff to remove unused ignores before making sure we're also type checking all the files in the mypy strict configurations

Test plan:
dmypy restart && python3 scripts/lintrunner.py -a
pyrefly check

step 1: delete lines in the pyrefly.toml file from the project-excludes field
step 2: run pyrefly check
step 3: add suppressions, clean up unused suppressions
before: https://gist.github.com/maggiemoss/4b3bf2037014e116bc00706a16aef199

after:
INFO 0 errors (6,884 ignored)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165692
Approved by: https://github.com/oulgen
trunk/43d78423ac224cce432bf34ed9627035169d5433 		2025-10-17 04:15:25 +00:00
Justin Chu
fcbde24c1c [ONNX] Remove common imports from torchlib (#165156) 
The Rank and IsScalar functions are no longer used in the torchlib. Requires onnxscript v0.5.4

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165156
Approved by: https://github.com/Skylion007, https://github.com/cyyever
trunk/fcbde24c1cb54f3e0417e123bdb9ae09da134c8d 		2025-10-17 03:25:34 +00:00
eellison
861cdb887b use statically_known_leq & *=2 instead of bound_sympy in persistent rblock (#165657) 
While these should be equivalent, we've found instances where they are not, and an error was caused. update until we figure out underlying issue.

Differential Revision: [D84835898](https://our.internmc.facebook.com/intern/diff/D84835898)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165657
Approved by: https://github.com/bobrenjc93
trunk/861cdb887b73818a7e96dc07c5aa6a308216daa4 		2025-10-17 02:48:03 +00:00
Eddie Yan
3154482072 [CUDA][cuBLAS] Only xFail addmm with reduced precision reductions on non-RTX skus (#165379) 
RTX Blackwells don't behave quite like their datacenter counterparts here

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165379
Approved by: https://github.com/Skylion007
trunk/3154482072cefc49b69bd377a0774707b021fea7 		2025-10-17 02:45:07 +00:00
Mu-Chu Lee
9fccbdd4f0 Fix incorrect function signature in template (#165567) 
Summary:
In https://github.com/pytorch/pytorch/pull/148305 we refactored the grid
argument out, but it's not reflected in our template.

Test Plan:
Included in commit.
python test/inductor/test_aot_inductor.py
AOTInductorTestABICompatibleGpu.test_cond_symint_input_disable_one_pass_cuda

Reviewers:

Subscribers:

Tasks:

Tags:

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165567
Approved by: https://github.com/desertfire
trunk/9fccbdd4f05820fed8ccf66422b056c932649d62 		2025-10-17 02:40:56 +00:00
bobrenjc93
7dabfb07cb [torchfuzz] add support for --stop-at-first-failure flag (#165529) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165529
Approved by: https://github.com/pianpwk
ghstack dependencies: #164749
trunk/7dabfb07cb896e9c31734c17d215e59418e071e0 		2025-10-17 02:18:07 +00:00
bobrenjc93
d0add0be43 [torchfuzz] check in some more ignore regexes (#164749) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164749
Approved by: https://github.com/pianpwk
 2025-10-17 02:18:07 +00:00
PyTorch MergeBot
11e2084308 Revert "[Mem Snapshot] Add Metadata Field (#165490)" 
This reverts commit 5b3ea758951558e7d9f681ae784acb57eaa07910.

Reverted https://github.com/pytorch/pytorch/pull/165490 on behalf of https://github.com/pytorch-auto-revert due to Reverted automatically by pytorch's autorevert, to avoid this behaviour add the tag autorevert: disable ([comment](https://github.com/pytorch/pytorch/pull/165490#issuecomment-3413491091))
trunk/11e20843086cf58b3976ed3ac75ac1bbbebd715d 		2025-10-17 02:01:53 +00:00
Aaron Gokaslan
9726553653 [BE][Ez]: Use sys.executable instead of hardcoded Python (#165679) 
Handles edgecase to ensure proper interpreter is called. Inspired by #165633
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165679
Approved by: https://github.com/FindHao
trunk/9726553653ee1c53fc9a1f436a92b29f456082ca 		2025-10-17 01:07:40 +00:00
Shangdi Yu
d82527b32a [Windows] Add AOTI cross-compilation CI (#165573) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165573
Approved by: https://github.com/malfet
ghstack dependencies: #165560
trunk/d82527b32ad0e09309ff874458139ecf6994e030 		2025-10-17 01:05:35 +00:00
Shangdi Yu
5d9b024276 Add mingw to docker (#165560) 
Add mingw to `pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11` docker image to support AOTI cross-compilation

This PR will make docker container rebuild, and upgrade python version from 3.13.7 to 3.13.8. and it relies on https://github.com/pytorch/pytorch/pull/165667
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165560
Approved by: https://github.com/malfet
trunk/5d9b0242762e7a416a789365e987b63dfe6b030a 		2025-10-17 00:47:01 +00:00
Yuanyuan Chen
5b2afe4c5d Turn some const variables into constexpr in C++ code (#165401) 
This PR checks the C++ code and turns some const variables into constexpr.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165401
Approved by: https://github.com/Skylion007
trunk/5b2afe4c5dc87786ca65bf22ca9a78f7c21a33a4 		2025-10-17 00:40:11 +00:00
Yuanyuan Chen
b2953f5643 [9/N] Apply ruff UP035 rule (#165515) 
This is follow-up of #165214 to continue applying ruff UP035 rule to the code base.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165515
Approved by: https://github.com/Lucaskabela
trunk/b2953f5643c6627d2bd0ceb9d2ccb32e2545c549 		2025-10-17 00:09:51 +00:00
PyTorch MergeBot
470e2f61c3 Revert "[Fix] Use sys.executable instead of hardcoded python (#165633)" 
This reverts commit 37f3ba274a8ccebc6b3409f52cf068a8b23617d4.

Reverted https://github.com/pytorch/pytorch/pull/165633 on behalf of https://github.com/malfet due to Looks like it broke test_collect_callgrind in slow workflows, see e0fe37fa68/1 ([comment](https://github.com/pytorch/pytorch/pull/165633#issuecomment-3413290813))
trunk/470e2f61c3b2083e8d895b6aae5ede198bba5696 		2025-10-17 00:06:40 +00:00
Kurt Mohler
e0fe37fa68 [MPS] Move torch.cat impl to Metal (#165373) 
After this change, all of the cases tested in [this performance measurement script](10de64c5ac/cat/perf0.py) take either roughly the same runtime or less.

Before:

```
idx: cpu time, mps time, speedup, op, args, kwargs
-----------------------------------------
0: 0.000857 ms, 0.016098 ms, 0.05, cat, [[tensor(shape[5, 5]), tensor(shape[5, 5])]], {'dim': -1}
1: 0.000858 ms, 0.014861 ms, 0.06, cat, [[tensor(shape[5, 5]), tensor(shape[5, 5])]], {'dim': 1}
2: 0.000806 ms, 0.015145 ms, 0.05, cat, [[tensor(shape[10, 5]), tensor(shape[5, 5])]], {'dim': 0}
3: 0.000829 ms, 0.015355 ms, 0.05, cat, [[tensor(shape[1, 2, 3]), tensor(shape[1, 2, 3])]], {'dim': -2}
4: 0.000591 ms, 0.000582 ms, 1.02, cat, [[tensor(shape[0]), tensor(shape[0])]], {'dim': 0}
5: 0.001076 ms, 0.022387 ms, 0.05, cat, [[tensor(shape[0]), tensor(shape[5, 5])]], {'dim': 1}
6: 0.000708 ms, 0.022300 ms, 0.03, cat, [[tensor(shape[0, 5]), tensor(shape[5, 5])]], {'dim': 0}
7: 0.000640 ms, 0.014367 ms, 0.04, cat, [[tensor(shape[1]), tensor(shape[1])]], {}
8: 0.000777 ms, 0.027506 ms, 0.03, cat, [[tensor(shape[2, 2, 2, 2])], 1], {}
9: 0.003383 ms, 0.269277 ms, 0.01, cat, "[[tensor(shape[3, 1, 2]), tensor(shape[3, 2, 2]), tensor(shape[3, 3, 2]), tensor(shape[3, 1, 2]), te...", {'dim': 1}
10: 0.526138 ms, 0.650852 ms, 0.81, cat, "[[tensor(shape[3, 1, 2]), tensor(shape[3, 2, 2]), tensor(shape[3, 3, 2]), tensor(shape[3, 1, 2]), te...", {'dim': 1}
11: 0.444091 ms, 0.628630 ms, 0.71, cat, "[[tensor(shape[1, 3, 2]), tensor(shape[2, 3, 2]), tensor(shape[3, 3, 2]), tensor(shape[1, 3, 2]), te...", {'dim': 0}
12: 2.011870 ms, 0.989525 ms, 2.03, cat, [[tensor(shape[1000000, 3, 2]), tensor(shape[1000000, 3, 2])]], {'dim': 0}
13: 3.100653 ms, 0.948178 ms, 3.27, cat, [[tensor(shape[3, 1000000, 2]), tensor(shape[3, 1000000, 2])]], {'dim': 1}
14: 3.112174 ms, 0.954174 ms, 3.26, cat, [[tensor(shape[3, 2, 1000000]), tensor(shape[3, 2, 1000000])]], {'dim': 2}
```

After:

```
idx: cpu time, mps time, speedup, op, args, kwargs
-----------------------------------------
0: 0.000790 ms, 0.013111 ms, 0.06, cat, [[tensor(shape[5, 5]), tensor(shape[5, 5])]], {'dim': -1}
1: 0.000800 ms, 0.014419 ms, 0.06, cat, [[tensor(shape[5, 5]), tensor(shape[5, 5])]], {'dim': 1}
2: 0.000748 ms, 0.010019 ms, 0.07, cat, [[tensor(shape[10, 5]), tensor(shape[5, 5])]], {'dim': 0}
3: 0.000767 ms, 0.010063 ms, 0.08, cat, [[tensor(shape[1, 2, 3]), tensor(shape[1, 2, 3])]], {'dim': -2}
4: 0.000591 ms, 0.000591 ms, 1.00, cat, [[tensor(shape[0]), tensor(shape[0])]], {'dim': 0}
5: 0.001220 ms, 0.009763 ms, 0.12, cat, [[tensor(shape[0]), tensor(shape[5, 5])]], {'dim': 1}
6: 0.000739 ms, 0.006203 ms, 0.12, cat, [[tensor(shape[0, 5]), tensor(shape[5, 5])]], {'dim': 0}
7: 0.000647 ms, 0.009905 ms, 0.07, cat, [[tensor(shape[1]), tensor(shape[1])]], {}
8: 0.000753 ms, 0.007818 ms, 0.10, cat, [[tensor(shape[2, 2, 2, 2])], 1], {}
9: 0.003823 ms, 0.192723 ms, 0.02, cat, "[[tensor(shape[3, 1, 2]), tensor(shape[3, 2, 2]), tensor(shape[3, 3, 2]), tensor(shape[3, 1, 2]), te...", {'dim': 1}
10: 0.576564 ms, 0.733920 ms, 0.79, cat, "[[tensor(shape[3, 1, 2]), tensor(shape[3, 2, 2]), tensor(shape[3, 3, 2]), tensor(shape[3, 1, 2]), te...", {'dim': 1}
11: 0.462957 ms, 0.692799 ms, 0.67, cat, "[[tensor(shape[1, 3, 2]), tensor(shape[2, 3, 2]), tensor(shape[3, 3, 2]), tensor(shape[1, 3, 2]), te...", {'dim': 0}
12: 2.017181 ms, 0.968345 ms, 2.08, cat, [[tensor(shape[1000000, 3, 2]), tensor(shape[1000000, 3, 2])]], {'dim': 0}
13: 3.203508 ms, 0.986382 ms, 3.25, cat, [[tensor(shape[3, 1000000, 2]), tensor(shape[3, 1000000, 2])]], {'dim': 1}
14: 3.181249 ms, 1.007773 ms, 3.16, cat, [[tensor(shape[3, 2, 1000000]), tensor(shape[3, 2, 1000000])]], {'dim': 2}
```

Fixes #165350
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165373
Approved by: https://github.com/kulinseth, https://github.com/malfet
trunk/e0fe37fa687a39e42ddeeb5c03986ffd5c40e662 		2025-10-17 00:03:04 +00:00
PyTorch MergeBot
d2c82bafb7 Revert "158232 Fix autocast cache incorrectly retaining no_grad state (#165068)" 
This reverts commit 5daef30b26b794d237fbbc399c1d47ec0380200a.

Reverted https://github.com/pytorch/pytorch/pull/165068 on behalf of https://github.com/jeffdaily due to This broke ROCm CI. test/test_transformers.py::TestTransformersCUDA::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_True_use_autocast_True_d_model_256_cuda [GH job link](https://github.com/pytorch/pytorch/actions/runs/18572589089/job/52952074008) [HUD commit link](5daef30b26) ([comment](https://github.com/pytorch/pytorch/pull/165068#issuecomment-3413184445))
trunk/d2c82bafb7086a1dd109a0a6407ca7fed27337f4 		2025-10-16 23:08:27 +00:00
Colin L Reliability Rice
98a488c9aa Start recording inductor provenance (#162669) 
Summary:
This stores information on where fx graphs come from, which makes it
significantly easier to debug.

One outstanding question

1) I only stored the kernel stack traces, do we also want the node mappings?

Test Plan:
I wrote a explicit logging test which makes a module, fx traces it, compiles it, and makes sure the logging infomration shows up.

```
clr@devvm17763 ~/fbsource/fbcode/caffe2/test/dynamo
 % buck2 test @//mode/opt fbcode//caffe2/test/dynamo:test_dynamo -- test_utils

File changed: fbsource//xplat/caffe2/test/dynamo/test_utils.py
File changed: fbcode//caffe2/test/dynamo/test_utils.py
Buck UI: https://www.internalfb.com/buck2/528dea32-2416-4a62-a1ec-39f3c0efdd2e
Test UI: https://www.internalfb.com/intern/testinfra/testrun/13229324015574003
Network: Up: 0B  Down: 0B
Executing actions. Remaining     0/2
Command: test.
Time elapsed: 17.3s
Tests finished: Pass 16. Fail 0. Fatal 0. Skip 0. Build failure 0
```

Rollback Plan:

Differential Revision: D82037582

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162669
Approved by: https://github.com/yushangdi
trunk/98a488c9aaadd4b137b7a63dad31543aee75c454 		2025-10-16 23:05:31 +00:00
Shivam Raikundalia
5b3ea75895 [Mem Snapshot] Add Metadata Field (#165490) 
Summary:
The implementation adds the ability to:

Set custom metadata strings that will be attached to all subsequent allocations
Clear or change the metadata at any point
View the metadata in memory snapshots via _dump_snapshot()

Test Plan: Added test in test_cuda.py and check manually in snapshot to see that metadata was added.

Differential Revision: D84654933

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165490
Approved by: https://github.com/yushangdi
trunk/5b3ea758951558e7d9f681ae784acb57eaa07910 		2025-10-16 22:54:27 +00:00