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cuda_stream_jit
65 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Guilherme Leobas
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9679e1affc |
annotate torch.autograd.* modules (#45004)
Summary: Fixes https://github.com/pytorch/pytorch/issues/44638 Pull Request resolved: https://github.com/pytorch/pytorch/pull/45004 Reviewed By: VitalyFedyunin Differential Revision: D24113562 Pulled By: ezyang fbshipit-source-id: a85018b7e08b2fe6cf2bc14a217eb418cb2b9de4 |
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Ilia Cherniavskii
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f5c95d5cf1 |
Source code level attribution in profiler (#43898)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43898 Adding with_source parameter to enable tracking source code (filename and line) in profiler for eager, torchscript and autograd modes Test Plan: python test/test_profiler.py ``` Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls Source Location ----------------------------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------------------------------------- ts_method_1 10.43% 235.364us 36.46% 822.920us 822.920us 1 test/test_profiler.py(70): test_source aten::add 7.52% 169.833us 8.88% 200.439us 200.439us 1 test/test_profiler.py(69): test_source aten::normal_ 6.26% 141.380us 6.26% 141.380us 141.380us 1 test/test_profiler.py(67): test_source aten::add 5.80% 130.830us 8.41% 189.800us 63.267us 3 test/test_profiler.py(72): test_source aten::sum 5.02% 113.340us 8.39% 189.475us 189.475us 1 test/test_profiler.py(64): ts_method_1 aten::add 4.58% 103.346us 6.33% 142.847us 142.847us 1 test/test_profiler.py(62): ts_method_1 aten::mul 4.05% 91.498us 9.62% 217.113us 217.113us 1 test/test_profiler.py(71): test_source aten::add 4.03% 90.880us 5.60% 126.405us 126.405us 1 test/test_profiler.py(58): ts_method_2 aten::empty 3.49% 78.735us 3.49% 78.735us 19.684us 4 test/test_profiler.py(72): test_source ``` Reviewed By: ngimel Differential Revision: D23432664 Pulled By: ilia-cher fbshipit-source-id: 83ad7ebe0c2502494d3b48c4e687802db9c77615 |
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Natalia Gimelshein
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50b91103a9 |
add self cuda time to avoid double/quadruple counting (#45209)
Summary:
In profiler, cuda did not report self time, so for composite functions there was no way to determine which function is really taking time. In addition, "total cuda time" reported was frequently more than total wallclock time. This PR adds "self CUDA time" in profiler, and computes total cuda time based on self cuda time, similar to how it's done for CPU. Also, slight formatting changes to make table more compact. Before:
```
-------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls
-------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
aten::matmul 0.17% 890.805us 99.05% 523.401ms 5.234ms 49.91% 791.184ms 7.912ms 100
aten::mm 98.09% 518.336ms 98.88% 522.511ms 5.225ms 49.89% 790.885ms 7.909ms 100
aten::t 0.29% 1.530ms 0.49% 2.588ms 25.882us 0.07% 1.058ms 10.576us 100
aten::view 0.46% 2.448ms 0.46% 2.448ms 12.238us 0.06% 918.936us 4.595us 200
aten::transpose 0.13% 707.204us 0.20% 1.058ms 10.581us 0.03% 457.802us 4.578us 100
aten::empty 0.14% 716.056us 0.14% 716.056us 7.161us 0.01% 185.694us 1.857us 100
aten::as_strided 0.07% 350.935us 0.07% 350.935us 3.509us 0.01% 156.380us 1.564us 100
aten::stride 0.65% 3.458ms 0.65% 3.458ms 11.527us 0.03% 441.258us 1.471us 300
-------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
Self CPU time total: 528.437ms
CUDA time total: 1.585s
Recorded timeit time: 789.0814 ms
```
Note recorded timeit time (with proper cuda syncs) is 2 times smaller than "CUDA time total" reported by profiler
After
```
-------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls
-------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
aten::matmul 0.15% 802.716us 99.06% 523.548ms 5.235ms 302.451us 0.04% 791.151ms 7.912ms 100
aten::mm 98.20% 519.007ms 98.91% 522.745ms 5.227ms 790.225ms 99.63% 790.848ms 7.908ms 100
aten::t 0.27% 1.406ms 0.49% 2.578ms 25.783us 604.964us 0.08% 1.066ms 10.662us 100
aten::view 0.45% 2.371ms 0.45% 2.371ms 11.856us 926.281us 0.12% 926.281us 4.631us 200
aten::transpose 0.15% 783.462us 0.22% 1.173ms 11.727us 310.016us 0.04% 461.282us 4.613us 100
aten::empty 0.11% 591.603us 0.11% 591.603us 5.916us 176.566us 0.02% 176.566us 1.766us 100
aten::as_strided 0.07% 389.270us 0.07% 389.270us 3.893us 151.266us 0.02% 151.266us 1.513us 100
aten::stride 0.60% 3.147ms 0.60% 3.147ms 10.489us 446.451us 0.06% 446.451us 1.488us 300
-------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 528.498ms
CUDA time total: 793.143ms
Recorded timeit time: 788.9832 ms
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45209
Reviewed By: zou3519
Differential Revision: D23925491
Pulled By: ngimel
fbshipit-source-id: 7f9c49238d116bfd2db9db3e8943355c953a77d0
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Rohan Varma
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5dbcbea265 |
TorchScript with record_function (#44345)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44345 As part of enhancing profiler support for RPC, when executing TorchScript functions over RPC, we would like to be able to support user-defined profiling scopes created by `with record_function(...)`. Since after https://github.com/pytorch/pytorch/pull/34705, we support `with` statements in TorchScript, this PR adds support for `with torch.autograd.profiler.record_function` to be used within TorchScript. This can be accomplished via the following without this PR: ``` torch.opts.profiler._record_function_enter(...) # Script code, such as forward pass torch.opts.profiler._record_function_exit(....) ``` This is a bit hacky and it would be much cleaner to use the context manager now that we support `with` statements. Also, `_record_function_` type operators are internal operators that are subject to change, this change will help avoid BC issues in the future. Tested with `python test/test_jit.py TestWith.test_with_record_function -v` ghstack-source-id: 112320645 Test Plan: Repro instructions: 1) Change `def script_add_ones_return_any(x) -> Any` to `def script_add_ones_return_any(x) -> Tensor` in `jit/rpc_test.py` 2) `buck test mode/dev-nosan //caffe2/test/distributed/rpc:process_group_agent -- test_record_function_on_caller_rpc_async --print-passing-details` 3) The function which ideally should accept `Future[Any]` is `def _call_end_callbacks_on_future` in `autograd/profiler.py`. python test/test_jit.py TestWith.test_with_foo -v Reviewed By: pritamdamania87 Differential Revision: D23332074 fbshipit-source-id: 61b0078578e8b23bfad5eeec3b0b146b6b35a870 |
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Leon Gao
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69080e9e7e |
simplify profile text output by displaying only top-level ops statistics (#42262)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42262 Test Plan: Imported from OSS ``` ================================================================================================================================================================================== TEST ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls Input Shapes ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- aten::add_ 3.61% 462.489us 3.61% 462.489us 462.489us 1 [[3, 20], [3, 20], []] aten::slice 1.95% 249.571us 1.95% 250.018us 250.018us 1 [[3, 80], [], [], [], []] aten::lstm 1.89% 242.534us 22.41% 2.872ms 2.872ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.68% 215.852us 18.18% 2.330ms 2.330ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.68% 215.767us 18.49% 2.370ms 2.370ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.60% 205.014us 20.15% 2.582ms 2.582ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.55% 198.213us 18.53% 2.375ms 2.375ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::addmm 0.95% 122.359us 1.01% 129.857us 129.857us 1 [[80], [3, 20], [20, 80], [], []] aten::stack 0.29% 36.745us 0.63% 80.179us 80.179us 1 [[], []] aten::add_ 0.28% 35.694us 0.28% 35.694us 35.694us 1 [[3, 20], [3, 20], []] ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Self CPU time total: 12.817ms ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls Input Shapes ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- aten::mul 11.45% 1.467ms 12.88% 1.651ms 11.006us 150 [[3, 20], [3, 20]] aten::lstm 8.41% 1.077ms 97.76% 12.529ms 2.506ms 5 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::addmm 7.65% 979.982us 11.38% 1.459ms 29.182us 50 [[80], [3, 20], [20, 80], [], []] aten::sigmoid_ 6.78% 869.295us 9.74% 1.249ms 8.327us 150 [[3, 20]] aten::add_ 5.82% 745.801us 5.82% 745.801us 14.916us 50 [[3, 20], [3, 20], []] aten::slice 5.58% 715.532us 6.61% 847.445us 4.237us 200 [[3, 80], [], [], [], []] aten::unsafe_split 4.24% 544.015us 13.25% 1.698ms 33.957us 50 [[3, 80], [], []] aten::tanh 3.11% 398.881us 6.05% 775.024us 15.500us 50 [[3, 20]] aten::empty 3.04% 389.055us 3.04% 389.055us 1.319us 295 [[], [], [], [], [], []] aten::sigmoid 2.96% 379.686us 2.96% 379.686us 2.531us 150 [[3, 20], [3, 20]] ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Self CPU time total: 12.817ms ================================================================================================================================================================================== TEST ================================================================================================================================================================================== This report only display top-level ops statistics ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls Input Shapes ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- aten::lstm 1.89% 242.534us 22.41% 2.872ms 2.872ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.68% 215.852us 18.18% 2.330ms 2.330ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.68% 215.767us 18.49% 2.370ms 2.370ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.60% 205.014us 20.15% 2.582ms 2.582ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] aten::lstm 1.55% 198.213us 18.53% 2.375ms 2.375ms 1 [[5, 3, 10], [], [], [], [], [], [], [], []] ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Self CPU time total: 12.817ms ================================================================================================================================================================================== This report only display top-level ops statistics ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls Input Shapes ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- aten::lstm 8.41% 1.077ms 97.76% 12.529ms 2.506ms 5 [[5, 3, 10], [], [], [], [], [], [], [], []] ----------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------------------------------------- Self CPU time total: 12.817ms Total time based on python measurements: 13.206ms CPU time measurement python side overhead: 3.03% ``` Reviewed By: ilia-cher Differential Revision: D22830328 Pulled By: ilia-cher fbshipit-source-id: c9a71be7b23a8f84784117c788faa43caa96f545 |
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Ilia Cherniavskii
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f9a6c14364 |
Fix sequence numbers in profiler output (#42565)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42565 After recent changes to the record function we record more ranges in profiler output and also keep emitting sequence numbers for all ranges. Sequence numbers are used by external tools to correlate forward and autograd ranges and with many ranges having the same sequence number it becomes impossible to do this. This PR ensures that we set sequence numbers only for the top-level ranges and only in case when autograd is enabled. Test Plan: nvprof -fo trace.nvvp --profile-from-start off python test_script.py test_script https://gist.github.com/ilia-cher/2baffdd98951ee2a5f2da56a04fe15d0 then examining ranges in nvvp Reviewed By: ngimel Differential Revision: D22938828 Pulled By: ilia-cher fbshipit-source-id: 9a5a076706a6043dfa669375da916a1708d12c19 |
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wudenggang
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9600ed9af3 |
typo fixes (#41632)
Summary: typo fixes Pull Request resolved: https://github.com/pytorch/pytorch/pull/41632 Reviewed By: ezyang Differential Revision: D22617827 Pulled By: mrshenli fbshipit-source-id: c2bfcb7cc36913a8dd32f13fc9adc3aa0a9b682f |
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Ilia Cherniavskii
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e7a09b4d17 |
RecordFunction in Dispatcher (#37587)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37587 Lifting RecordFunction up into the dispatcher code Test Plan: Imported from OSS Differential Revision: D21374246 fbshipit-source-id: 19f9c1719e6fd3990e451c5bbd771121e91128f7 |
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Michael Wootton
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7eb71b4beb |
Profiler: Do not record zero duration kernel events (#41540)
Summary: Changes in the ROCm runtime have improved hipEventRecord. The events no longer take ~4 usec to execute on the gpu stream, instead they appear instantaneous. If you record two events, with no other activity in between, then they will have the same timestamp and the elapsed duration will be 0. The profiler uses hip/cuda event pairs to infer gpu execution times. It wraps functions whether they send work to the gpu or not. Functions that send no gpu work will show as having zero duration. Also they will show as running at the same time as neighboring functions. On a trace, all those functions combine into a 'call stack' that can be tens of functions tall (when indeed they should be sequential). This patch suppresses recording the zero duration 'kernel' events, leaving only the CPU execution part. This means functions that do not use the GPU do not get an entry for how long they were using the GPU, which seams reasonable. This fixes the 'stacking' on traces. It also improves the signal to noise of the GPU trace beyond what was available previously. This patch will not effect CUDA or legacy ROCm as those are not able to 'execute' eventRecord markers instantaneously. Pull Request resolved: https://github.com/pytorch/pytorch/pull/41540 Reviewed By: zou3519 Differential Revision: D22597207 Pulled By: albanD fbshipit-source-id: 5e89de2b6d53888db4f9dbcb91a94478cde2f525 |
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Ilia Cherniavskii
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b82bd654cc |
Increase shapes column length (#40440)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40440 Shapes sometimes need more than 35 symbols (Note: this ignores all push blocking failures!) Test Plan: found during testing the recipe https://github.com/pytorch/tutorials/pull/1019 Differential Revision: D22188679 Pulled By: ilia-cher fbshipit-source-id: efcf5d10882af7d9225897ec87debcf4abdc523f |
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Ilia Cherniavskii
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a54bb4e907 |
Fix demangle 't' issue in profiler (#40416)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40416 Fix demangle 't' that produces 'unsigned short' Test Plan: >>> import torch >>> from torch.autograd.profiler import profile >>> >>> t = torch.rand(4, 5) >>> with profile() as prof: ... t.t() >>> print(prof.key_averages().table()) Differential Revision: D22179508 Pulled By: ilia-cher fbshipit-source-id: b502af2f2547317c1a6447f2225d50b2376bfc76 |
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Rohan Varma
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14f7e95c1a |
Add prefix of remote events for RPC profiling (#40066)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40066 Builds on top of the previous PR to ensure that all remotely profiled events are prefixed with the key for the RPC that generated them. The key is generated by the result of `_build_rpc_profiling_key` in `rpc/internal.py` and prefixed onto the event name. In order to do this, we set the current-key when creating the RPC in Python, retrieve the currently-set key in C++ and save a GloballyUniqueId -> key mapping to an in-memory map. When we receive an RPC with profiling information, we expect to receive this ID back, and look up the corresponding profiling key in the map. The key is then added to all the remote events. Tested by adding tests to ensure the key is added to all the remote events. Also added a UT which tests in under the multi-threading scenario, to ensure that the mapping's correctness is maintained when several RPCs are in the process of being created at once. ghstack-source-id: 106316106 Test Plan: Unit test Differential Revision: D22040035 fbshipit-source-id: 9215feb06084b294edbfa6e03385e13c1d730c43 |
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Rohan Varma
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7e82382ad5 |
Allow profiler to be enabled remotely with RPC (#38748)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38748 This diff contains the message scaffolding and profiler changes in order to be able to remotely run the profiler across different nodes and aggregate the results on a single node. As discussed, we have implemented this by creating new message types, that similar to autograd messages, wrap the profiling information with the original message, and send this new message over the wire. On the receiving end, this wrapped message is detected, we fetch the original message from it, and process the original message with the profiler enabled. When sending a response with profiling information, we serialize the profiled `Events` and send them back over RPC. When such a message is received, the events profiled on the remote node are stored (added back to the local profiler). Changes in this PR: - New message types (run_with_profiling_req, run_with_profiling_resp) to send profiling info over the wire. Message parsing logic is added to handle these wrapped types. - Handling of sending profiler data over the wire, in particular, the attributes of the `ProfilerConfig` and the serialized profiled `Event`s - The logic for wrapping RPC messages is deduped with that in `rpc_with_autograd`, and the common payload wrapping/unwrapping logic is moved to helper functions in `rpc/utils.cpp` - Changes in `autograd/utils.cpp` to detect if we have enabled the profiler and are sending an RPC, if so, uses the above new message types - Changes in request_callback to parse and turn on the profiler in a thread-local fashion - Serialization and deserialization of profiling `Events`, and support to add the remote events to the thread-local profiler - Introduction of the concept of `node_id`, which as discussed with ilia-cher , will be used along with the `Event`s handle attribute to distinguish between events. When there are events from different nodes, this node information is rendered in the profile output (e.g. when printing tables), otherwise, it is not, since it is irrelevant. - Some changes to profiler.cpp to add useful helper methods/guards - toHere() is now profiled for RRefs - Unittests ghstack-source-id: 106134626 Test Plan: Added unittests, existing profiler unittests. Differential Revision: D19510010 fbshipit-source-id: 044347af992f19a9e3b357c9567f6fc73e988157 |
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Shihao Xu
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45baf0e1a0 |
[Profiler x RPC] Enable RPC Server Global Profiler (#38847)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38847 See motivation and design in https://github.com/pytorch/pytorch/issues/38845. Close https://github.com/pytorch/pytorch/issues/38845. Changes, - Add pre-request and post-response hooks to RPC "request_callback_impl.cpp". For a thread that executes RPC handler, check if the server-side global profiling is on. If it's on, enable profiling on this thread and after response, merge the thread-local profiling result into the global profiling state. - Add context-style Python API to parse the profiling Events into ranges represented by FunctionEvent. - Add data-structures to work as global profiling state that support nesting and container for consolidating results from multiple threads. Test, - Add a test that uses nested profiling range and inspect the profiling events. ghstack-source-id: 104991517 Test Plan: buck test mode/dev-nosan //caffe2/test/distributed/rpc/:rpc_fork buck build mode/dev-nosan //caffe2/test/distributed/rpc/:rpc_fork && \ buck-out/gen/caffe2/test/distributed/rpc/rpc_fork\#binary.par -r test_server_process_global_profiler Differential Revision: D5665992 fbshipit-source-id: 07f3bef5efd33d1214ef3404284c3803f5deca26 |
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Ilia Cherniavskii
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a94fb71b12 |
Memory profiling (#37775)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37775 Adding memory usage into profiler table output Test Plan: BUILD_BINARY=1 USE_BLAS=MKL USE_MKLDNN=0 USE_CUDA=0 python setup.py develop install --cmake ``` import torch import torchvision.models as models model = models.resnet18() inp = torch.randn(5, 3, 224, 224) with torch.autograd.profiler.profile(profile_memory=True, record_shapes=True) as prof: model(inp) print(prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_memory_usage", row_limit=15)) ``` ``` --------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CPU Mem Total Number of Calls Input Shapes --------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- resize_ 0.37% 577.936us 0.37% 577.936us 9.796us 339.03 Mb 59 [[0]] empty 0.69% 1.061ms 0.74% 1.139ms 5.556us 47.42 Mb 205 [] stride 0.00% 0.853us 0.00% 0.853us 0.853us 19.53 Kb 1 [[5, 1000]] empty_strided 0.01% 21.393us 0.02% 26.033us 5.207us 252 b 5 [] is_complex 0.02% 37.425us 0.02% 37.425us 1.291us 208 b 29 [[]] masked_select 0.04% 55.333us 0.06% 93.616us 46.808us 120 b 2 [[30], [30]] conv2d 0.01% 18.009us 9.62% 14.902ms 14.902ms 0 b 1 [[5, 3, 224, 224], [64, 3, 7, 7], [ convolution 0.01% 12.436us 9.61% 14.884ms 14.884ms 0 b 1 [[5, 3, 224, 224], [64, 3, 7, 7], [ _convolution 0.03% 52.381us 9.60% 14.871ms 14.871ms 0 b 1 [[5, 3, 224, 224], [64, 3, 7, 7], [ size 0.00% 5.429us 0.00% 5.429us 0.339us 0 b 16 [[5, 3, 224, 224]] contiguous 0.00% 1.934us 0.00% 1.934us 0.967us 0 b 2 [[5, 3, 224, 224]] _convolution_nogroup 0.02% 27.505us 9.57% 14.814ms 14.814ms 0 b 1 [[5, 3, 224, 224], [64, 3, 7, 7], [ _nnpack_available 0.02% 34.267us 0.02% 34.267us 1.713us 0 b 20 [] thnn_conv2d 0.01% 13.274us 9.54% 14.771ms 14.771ms 0 b 1 [[5, 3, 224, 224], [64, 3, 7, 7], [ thnn_conv2d_forward 5.98% 9.264ms 19.02% 29.446ms 14.723ms 0 b 2 [[5, 3, 224, 224], [64, 3, 7, 7], [ --------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Self CPU time total: 154.855ms ``` Reviewed By: ngimel Differential Revision: D21384248 Pulled By: ilia-cher fbshipit-source-id: 31359cce2aa06f6255ed1ad8c60d03cb640bfec3 |
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Shawn Zhong
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724b2b6ebd |
Profiler: Call populate_cpu_children inside __str__ and fix typo (#37816)
Summary: Fix https://github.com/pytorch/pytorch/issues/37500 I messed up with the old PR https://github.com/pytorch/pytorch/pull/37755 during rebasing and thus opened this one. - Add call to `populate_cpu_children` for `__str__` to make sure that the printed result is correctly populated. - Add test `test_profiler_aggregation_table` - Fix a minor typo Pull Request resolved: https://github.com/pytorch/pytorch/pull/37816 Reviewed By: ilia-cher Differential Revision: D21627502 Pulled By: ngimel fbshipit-source-id: 9c908986b6a979ff08c2ad7e6f4afac1f5fbeebb |
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Rohan Varma
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4d4895a62a |
Use Future's then() API to fix RPC profiling (#38352)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38352 Fixes the RPC profiling by using the `then()` API added in https://github.com/pytorch/pytorch/pull/37311. Instead of adding a regular callback, we return a new future that completes when the profiling callback is finished. This is transparent to the user as the future still completes with the value of the original future (i.e. the RPC's return value) To make this work for RRef, we add a `_set_profiling_future` to set the profiling future, and `_get_profiling_future` to retrieve this future and wait on it in the tests. Re-enabled profiling tests and stress tested them 1000 times to verify the fix ghstack-source-id: 104086114 Test Plan: Re-enabled profiling tests Differential Revision: D21506940 fbshipit-source-id: 35cde22f0551c825c9bc98ddc24cca412878a63a |
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Rohan Varma
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291869d625 |
Remove unnecessary RPC profiling code after future merge (#38255)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38255 Now that the futures are consolidated after https://github.com/pytorch/pytorch/pull/35154, there is no `torch.distributed.rpc.Future` and we do not need a special path. All futures can now be profiled through the use of the jit operator defined in record_function_ops.cpp As a result, we also get rid of the record_function_ops.h file. RPC profiling tests are currently disabled, although I re-enabled them locally to ensure that they still work with this change. ghstack-source-id: 103869855 Test Plan: CI Differential Revision: D21506091 fbshipit-source-id: ad68341c9f2eab2dadc72fe6a6c59b05693434f2 |
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Ilia Cherniavskii
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facc5e0cc4 |
Make profiler thread local (#36291)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36291 Move profiler state to be a thread local property, reuse existing thread local propagation mechanism to ensure correct profiling of async tasks. This also makes push/pop callback thread safe and easier to use in e.g. distributed profilier Test Plan: USE_BLAS=MKL USE_MKLDNN=0 USE_CUDA=0 python setup.py develop install ./build/bin/test_jit ./build/bin/test_jit python test/test_autograd.py python test/test_jit.py Differential Revision: D20938501 Pulled By: ilia-cher fbshipit-source-id: c0c6c3eddcfea8fc7c14229534b7246a0ad25845 |
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James Reed
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c90955e3d1 |
[profiler] Sort by end interval as well when parsing CPU trace (#37297)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37297 Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D21245463 Pulled By: jamesr66a fbshipit-source-id: 8d307eaa32fa960b93dfd9a3b0b4c767fd903094 |
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Ralf Gommers
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78d5707041 |
Fix type annotations and make MyPy run on torch/ (#36584)
Summary: This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages. As a result, running `mypy` in the root dir of the repo now runs on: - `torch/` - `aten/src/ATen/function_wrapper.py` (the only file already covered in CI) In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`. Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through): <details> ``` torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx' torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch" torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2' torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2' torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx' torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util' torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework' torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer' torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...") torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items" torch/nn/_reduction.py:27: error: Name 'Optional' is not defined torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/_reduction.py:47: error: Name 'Optional' is not defined torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs torch/nn/modules/utils.py:27: error: Name 'List' is not defined torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment" torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction' caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed) caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs caffe2/python/__init__.py:7: error: Module has no attribute "CPU" caffe2/python/__init__.py:8: error: Module has no attribute "CUDA" caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN" caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL" caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL" caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP" caffe2/python/__init__.py:13: error: Module has no attribute "HIP" caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"? caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"? caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:35: error: Module has no attribute "cuda" caffe2/python/__init__.py:37: error: Module has no attribute "cuda" caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory" torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C' torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import) torch/_tensor_str.py:164: error: Not all arguments converted during string formatting torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/_linalg_utils.py:26: error: Name 'Optional' is not defined torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Optional' is not defined torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Optional' is not defined torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Optional' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched' torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...") torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...") torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm") torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Optional' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/nn/init.py:109: error: Name 'Tensor' is not defined torch/nn/init.py:126: error: Name 'Tensor' is not defined torch/nn/init.py:142: error: Name 'Tensor' is not defined torch/nn/init.py:165: error: Name 'Tensor' is not defined torch/nn/init.py:180: error: Name 'Tensor' is not defined torch/nn/init.py:194: error: Name 'Tensor' is not defined torch/nn/init.py:287: error: Name 'Tensor' is not defined torch/nn/init.py:315: error: Name 'Tensor' is not defined torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction' torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer' torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_" torch/jit/_builtins.py:80: error: Module has no attribute "stft" torch/jit/_builtins.py:81: error: Module has no attribute "cdist" torch/jit/_builtins.py:82: error: Module has no attribute "norm" torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm" torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm" torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C' torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...") torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import) torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass" torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache" torch/_lowrank.py:11: error: Name 'Tensor' is not defined torch/_lowrank.py:13: error: Name 'Optional' is not defined torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Optional' is not defined torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Optional' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:82: error: Name 'Tuple' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:130: error: Name 'Tensor' is not defined torch/_lowrank.py:130: error: Name 'Optional' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:130: error: Name 'Tuple' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:167: error: Name 'Tensor' is not defined torch/_lowrank.py:167: error: Name 'Optional' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:167: error: Name 'Tuple' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases torch/quantization/observer.py:45: error: Invalid base class "ABC" torch/quantization/observer.py:127: error: Name 'Tensor' is not defined torch/quantization/observer.py:127: error: Name 'Tuple' is not defined torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:192: error: Name 'Tensor' is not defined torch/quantization/observer.py:192: error: Name 'Tuple' is not defined torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:534: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tuple' is not defined torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:894: error: Cannot determine type of 'max_val' torch/quantization/observer.py:894: error: Cannot determine type of 'min_val' torch/quantization/observer.py:899: error: Cannot determine type of 'min_val' torch/quantization/observer.py:902: error: Name 'Tensor' is not defined torch/quantization/observer.py:925: error: Name 'Tensor' is not defined torch/quantization/observer.py:928: error: Cannot determine type of 'min_val' torch/quantization/observer.py:929: error: Cannot determine type of 'max_val' torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric" torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:289: error: Module has no attribute "ops" torch/nn/quantized/functional.py:290: error: Module has no attribute "ops" torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops" torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops" torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig' torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__" torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views' torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16 torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15 torch/functional.py:114: error: Name 'Tuple' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:114: error: Name 'Optional' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]" torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:204: note: Possible overload variants: torch/functional.py:204: note: def __setitem__(self, int, int) -> None torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:204: note: def __getitem__(self, int) -> int torch/functional.py:204: note: def __getitem__(self, slice) -> List[int] torch/functional.py:207: error: "Tensor" has no attribute "copy_" torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:212: note: Possible overload variants: torch/functional.py:212: note: def __setitem__(self, int, int) -> None torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:212: note: def __getitem__(self, int) -> int torch/functional.py:212: note: def __getitem__(self, slice) -> List[int] torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:334: error: Name 'Optional' is not defined torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]" torch/functional.py:431: error: Module has no attribute "stft" torch/functional.py:766: error: Module has no attribute "cdist" torch/functional.py:768: error: Module has no attribute "cdist" torch/functional.py:770: error: Module has no attribute "cdist" torch/functional.py:775: error: Name 'Optional' is not defined torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'Optional' is not defined torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'number' is not defined torch/functional.py:780: error: Name 'norm' already defined on line 775 torch/functional.py:785: error: Name 'Optional' is not defined torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:785: error: Name 'number' is not defined torch/functional.py:785: error: Name 'norm' already defined on line 775 torch/functional.py:790: error: Name 'Optional' is not defined torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:790: error: Name 'norm' already defined on line 775 torch/functional.py:795: error: Name 'norm' already defined on line 775 torch/functional.py:960: error: Name 'Any' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any") torch/functional.py:960: error: Name 'Tuple' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized" torch/functional.py:1041: error: Name 'Optional' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1041: error: Name 'Tuple' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1056: error: Name 'Optional' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1056: error: Name 'Tuple' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]") torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]") torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]") torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase" torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]") torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None") torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric" torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric" torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module' torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68 torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass" torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524 torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer' torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching' torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]") torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]") torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...") torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/__init__.py:158: error: Name 'transforms' is not defined torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse' torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import) torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import) torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore' torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore' torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584 Reviewed By: seemethere, ailzhang Differential Revision: D21155985 Pulled By: ezyang fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1 |
||
|
Rohan Varma
|
752d3c281a |
[profiler] Allow record_function ctx manager to profile futures (#35055)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35055 This is the first step to improving the way RPCs are profiled as suggested by Ilia. For now, since RPC can return two different types of futures, we have to implement two different code paths, one for the python eager mode future and one for the jit future. This diff implements the python eager part. We have defined a method `_call_end_callbacks_on_future` that takes in a future and schedules a `RecordFunction` to be completed as a callback on the future. Once https://github.com/pytorch/pytorch/pull/35039 lands, we can implement the JIT codepath by registering an operator that takes a `Future(t)` as well. These code paths will be merged once the futures are merged. ghstack-source-id: 102478180 Test Plan: Added unit tests Differential Revision: D20452003 fbshipit-source-id: 1acdcb073bd1f63d6fb2e78277ac0be00fd6671d |
||
|
Xiao Wang
|
a5b5ea9852 |
use new cuda kernel launch code in nvprof parsing (#35016)
Summary: This PR would fix https://github.com/pytorch/pytorch/issues/33986. The meaning of cbid 13 and 211 can be found at here |
||
|
Rohan Varma
|
d98bd5e1f5 |
[test all] Back out "Revert D20171428: [profiler] fix chrome tracing for profiler run with cuda"
Summary: There was an error in https://github.com/pytorch/pytorch/pull/30724/files that resulted in export_chrome_trace generating invalid JSON. This only came up when the profiler is run with use_cuda=True from what it looks like. In the future, we should have tests that ensure we generate valid JSON because we no longer use the json library. ghstack-source-id: 99508836 Test Plan: Added a unit test. Differential Revision: D20237040 fbshipit-source-id: 510befbdf4ec39632ac56544afcddee6c8cc3aca |
||
|
Rohan Varma
|
c579976603 |
Revert D20171428: [profiler] fix chrome tracing for profiler run with cuda
Test Plan: revert-hammer Differential Revision: D20171428 Original commit changeset: ec135a154ce3 fbshipit-source-id: 51ef4351a0df33fd087edbca1b7cd753cdbf1fdf |
||
|
Rohan Varma
|
c93b1d427c |
[profiler] fix chrome tracing for profiler run with cuda (#33987)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33987 There was an error in https://github.com/pytorch/pytorch/pull/30724/files that resulted in `export_chrome_trace` generating invalid JSON. This only came up when the profiler is run with `use_cuda=True` from what it looks like. In the future, we should have tests that ensure we generate valid JSON because we no longer use the json library. Test Plan: Add UT to validate JSON. Differential Revision: D20171428 fbshipit-source-id: ec135a154ce33f62b78d98468174dce4cf01fedf |
||
|
Sameer Deshmukh
|
cfdfdf70d7 |
remove JSON dumping dependency (#30724)
Summary: Fix for https://github.com/pytorch/pytorch/issues/19420 So after actually writing a C++ JSON dumping class I figured that a faster and cleaner way would be simply rewrite the Python without the JSON module since the JSON that we need to output is so simple. For now I decided to not touch the `parse_cpu_trace` function since only changing `export_chrome_trace` shows a 4x speedup. Here's the script I used for benchmarking: ``` python import time import torch x = torch.ones(2, 2) start = time.time() with torch.autograd.profiler.profile() as prof: for _ in range(10000): x * x for i in range(50): prof.export_chrome_trace("trace.json") stop = time.time() print(stop-start) ``` master branch (using json dump) -> 8.07515025138855 new branch (without json dump) -> 2.0943689346313477 I checked the trace file generated in the [test](https://github.com/pytorch/pytorch/blob/master/test/test_autograd.py#L2659) and it does work fine. Please let me know what you think. If you still insist on the C++ version I can send a new patch soon enough. CC ezyang rgommers Pull Request resolved: https://github.com/pytorch/pytorch/pull/30724 Differential Revision: D19298955 Pulled By: ezyang fbshipit-source-id: b0d7324ea5f90884ab8a00dd272f3aa3d9bc0427 |
||
|
Richard Zou
|
dbe2f265d0 |
Better error msg for autograd profiler + multi-worker dataloader crash (#31473)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/31473 Mitigates #6313 A common use case for the autograd profiler is to use it to run over an entire model, including dataloading. The following will crash: - run autograd profiler in CUDA mode - Use a multi-worker DataLoader (presumably with the 'fork' spawn method) - because the autograd profiler initializes CUDA and forking after CUDA is initialized is bad. This PR puts in a nice error message when this happens so that users aren't too confused. The new error message looks like: https://gist.github.com/zou3519/903f15c3e86bad4585b7e5ce14cc1b70 Test Plan: - Tested locally. - I didn't add a test case for this because it's hard to write a test case that doesn't completely stop the rest of our test suite from running. Differential Revision: D19178080 Pulled By: zou3519 fbshipit-source-id: c632525ba1f7b168324f1aa55416e5250f56a086 |
||
|
Junjie Bai
|
a26238da57 |
Enable using torch.autograd.profiler.record_function as decorator (#30861)
Summary:
```python
record_function('my_func')
def f(x, y):
return x + y
with profile() as p:
f(1, 2)
print(prof.key_averages().table())
```
```
------------------------------------ --------------- --------------- --------------- --------------- --------------- ---------------
Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls
------------------------------------ --------------- --------------- --------------- --------------- --------------- ---------------
my_func 85.42% 86.796us 87.27% 88.670us 88.670us 1
------------------------------------ --------------- --------------- --------------- --------------- --------------- ---------------
Self CPU time total: 101.606us
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30861
Differential Revision: D18857993
Pulled By: bddppq
fbshipit-source-id: eb6b8e2a8d4f3a7f8e5b4cb3da1ee3320acb1ae7
|
||
|
Tristan Rice
|
59151d3e43 |
autograd/profiler: support merging FunctionEventAvg (#30677)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30677 Currently you can only add FunctionEvents to FunctionEventAvg. This makes it so you can add multiple FunctionEventAvg objects together. This is useful for merging multiple profiles together such as when dealing with distributed training. Test Plan: added unit test buck test //caffe2/test:autograd -- test_profiler Reviewed By: bddppq Differential Revision: D18785578 fbshipit-source-id: 567a441dec885db7b0bd8f6e0ac9a60b18092278 |
||
|
Junjie Bai
|
5a8ad66354 |
Do not show cuda stats in autograd profiler when use_cuda=False (#29666)
Summary:
Example
```python
import torch
x = torch.randn(1)
with torch.autograd.profiler.profile(use_cuda=False) as prof:
x + x
print(prof.key_averages().table(sort_by='cpu_time_total'))
```
Before:
```
------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls
------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
add 100.00% 25.781ms 100.00% 25.781ms 25.781ms NaN 0.000us 0.000us 1
------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ---------------
Self CPU time total: 25.781ms
CUDA time total: 0.000us
```
After:
```
------- --------------- --------------- --------------- --------------- --------------- ---------------
Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls
------- --------------- --------------- --------------- --------------- --------------- ---------------
add 100.00% 25.037ms 100.00% 25.037ms 25.037ms 1
------- --------------- --------------- --------------- --------------- --------------- ---------------
Self CPU time total: 25.037ms
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29666
Differential Revision: D18458828
Pulled By: bddppq
fbshipit-source-id: d96ef4cec8b1e85b77c211292a3099048882734d
|
||
|
Tristan Rice
|
d8c66c1576 |
autograd/profiler: make python record_function use JIT methods
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28264 Test Plan: buck test caffe2/test:autograd caffe2/test/cpp/jit:jit Reviewed By: bddppq Differential Revision: D17997612 fbshipit-source-id: 8a29ae50c28ce905f63c732fe0aa49edfc9d99e3 |
||
|
David
|
ce16d689b3 |
FunctionEventAvg implements __iadd__ interface (#27498)
Summary: Resolving issue https://github.com/pytorch/pytorch/issues/26433 by making FunctionEventAvg implement the `__iadd__` interface again, like it used to. Pull Request resolved: https://github.com/pytorch/pytorch/pull/27498 Differential Revision: D17801918 Pulled By: ezyang fbshipit-source-id: 0597059c903ac168ed64a05ac1decff3ffd14f06 |
||
|
Prasun Anand
|
be3d27589f |
Added torch.autograd.profiler.record_function() as context manager. (#23428)
Summary: Added torch.autograd.profiler.record_function() as context manager to annotate block of Python code during profiling. Fixes https://github.com/pytorch/pytorch/issues/19422 . Pull Request resolved: https://github.com/pytorch/pytorch/pull/23428 Differential Revision: D16560771 Pulled By: soumith fbshipit-source-id: 3923130f7647a36a84dbbe28cc59d216d395d3f9 |
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Michael Carilli
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2d5ce519f2 |
Fix with emit_nvtx, also allow shape information to appear in nvtx ranges. (#21691)
Summary:
This PR is intended as a fix for https://github.com/pytorch/pytorch/issues/21644.
It allows the `with emit_nvtx` context manager to take an additional `record_shapes` argument. `record_shapes` is False by default, but if True, the nvtx ranges generated for each autograd op will append additional information about the sizes of Tensors received by that op.
The format of shape information is equivalent to what the CPU-side profiler spits out. For example,
```
M = torch.randn(2, 3)
mat1 = torch.randn(2, 3)
mat2 = torch.randn(3, 3)
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx(record_shapes=True):
torch.addmm(M, mat1, mat2)
```
produces the following nvtx range label for addmm:
(cf the "Input Shapes" shown in
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Alexander Sidorov
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726661b152 |
profiler: improve repr for averaged events (#20281)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20281 This is how it looks like now: ``` <FunctionEventAvg key=mm self_cpu_time=11.404s cpu_time=2.895ms cuda_time=0.000us input_shapes=[[26, 4096], [4096, 1024]]> ``` Before I forgot to update the repr for these when updated it for not averaged events Differential Revision: D15262862 fbshipit-source-id: a9e5b32c347b31118f98b4b5bf2bf46c1cc6d0d2 |
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Alexander Sidorov
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864cfbc216 |
PyTorch Profiler Shape aggregation support (#20035)
Summary: This is useful when you would like to understand performance bottlenecks of your model. One can use the shape analysis in order to fit model to a roofline model of their hardware. Please note that this feature can potentially skew profiling results. Also timing for not nested events will become wrong. One should only use timing for the bottom most events when shape analysis is used. Also for the case where people don't need shapes, profiling should not be affected. As in this case we don't collect shapes, which is the default behavior and this diff doesn't change it. One of the next steps could be, for example, choosing best candidates for quantization. In the scope of this diff I am just adding optional shapes collection into the Even class. After that in python there is minor functionality for providing groupping by shapes. In the output tables shapes are being truncated but in groupping full shape string is used as a key. Here is an example output: test_profiler_shapes (test_autograd.TestAutograd) ... ``` ------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls Input Shapes ------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- unsigned short 2.30% 305.031us 2.30% 305.031us 305.031us NaN 0.000us 0.000us 1 [[30, 20]] addmm 69.40% 9.199ms 69.40% 9.199ms 9.199ms NaN 0.000us 0.000us 1 [[30], [128, 20], [20, 30], [], []] unsigned short 0.98% 129.326us 0.98% 129.326us 129.326us NaN 0.000us 0.000us 1 [[40, 30]] addmm 27.32% 3.621ms 27.32% 3.621ms 3.621ms NaN 0.000us 0.000us 1 [[40], [128, 30], [30, 40], [], []] ------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Self CPU time total: 13.255ms CUDA time total: 0.000us ------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls Input Shapes ------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- unsigned short 2.30% 305.031us 2.30% 305.031us 305.031us NaN 0.000us 0.000us 1 [[30, 20]] addmm 69.40% 9.199ms 69.40% 9.199ms 9.199ms NaN 0.000us 0.000us 1 [[30], [128, 20], [20, 30], [], []] unsigned short 0.98% 129.326us 0.98% 129.326us 129.326us NaN 0.000us 0.000us 1 [[40, 30]] addmm 27.32% 3.621ms 27.32% 3.621ms 3.621ms NaN 0.000us 0.000us 1 [[40], [128, 30], [30, 40], [], []] ------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Self CPU time total: 13.255ms CUDA time total: 0.000us ``` Also added this for older aggregation test: ``` test_profiler_aggregation_lstm (test_autograd.TestAutograd) ... ====================================================================================================================================================================================================== TEST ----------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls Input Shapes ----------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- lstm 0.69% 4.606ms 5.30% 35.507ms 35.507ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.67% 4.521ms 5.27% 35.340ms 35.340ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.66% 4.399ms 5.02% 33.638ms 33.638ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.65% 4.354ms 4.92% 32.958ms 32.958ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.65% 4.351ms 4.96% 33.241ms 33.241ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.65% 4.323ms 5.10% 34.163ms 34.163ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.64% 4.304ms 4.92% 32.938ms 32.938ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.64% 4.300ms 5.10% 34.172ms 34.172ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.64% 4.292ms 5.05% 33.828ms 33.828ms NaN 0.000us 0.000us 1 [[5, 3, 10]] lstm 0.64% 4.263ms 4.98% 33.357ms 33.357ms NaN 0.000us 0.000us 1 [[5, 3, 10]] ----------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Self CPU time total: 670.120ms CUDA time total: 0.000us ----------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls Input Shapes ----------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- sigmoid 15.32% 102.647ms 15.32% 102.647ms 171.078us NaN 0.000us 0.000us 600 [[3, 20]] mul 15.20% 101.854ms 15.20% 101.854ms 169.757us NaN 0.000us 0.000us 600 [[3, 20], [3, 20]] lstm 12.74% 85.355ms 100.00% 670.120ms 33.506ms NaN 0.000us 0.000us 20 [[5, 3, 10]] addmm 11.16% 74.808ms 11.16% 74.808ms 249.361us NaN 0.000us 0.000us 300 [[80], [3, 20], [20, 80], [], []] tanh 9.89% 66.247ms 9.89% 66.247ms 165.617us NaN 0.000us 0.000us 400 [[3, 20]] split 6.42% 43.019ms 6.42% 43.019ms 215.095us NaN 0.000us 0.000us 200 [[3, 80]] add 5.67% 38.020ms 5.67% 38.020ms 190.101us NaN 0.000us 0.000us 200 [[3, 80], [3, 80], []] add 4.81% 32.225ms 4.81% 32.225ms 161.124us NaN 0.000us 0.000us 200 [[3, 20], [3, 20], []] addmm 3.79% 25.380ms 3.79% 25.380ms 253.796us NaN 0.000us 0.000us 100 [[80], [3, 10], [10, 80], [], []] unsigned short 3.72% 24.925ms 3.72% 24.925ms 83.083us NaN 0.000us 0.000us 300 [[80, 20]] ----------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- ----------------------------------- Self CPU time total: 670.120ms CUDA time total: 0.000us Total time based on python measurements: 691.366ms CPU time measurement python side overhead: 3.17% ok ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/20035 Differential Revision: D15174987 Pulled By: salexspb fbshipit-source-id: 9600c5d1d1a4c2cba08b320fed9da155d8284ab9 |
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Alexander Sidorov
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7a8bc85f47 |
Profiler: add Self CPU Time Total, CPU time total and other general improvements (#19378)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19378 Function profile events are typically nested. In this diff I add parent child relationship to the intervals. This way we can attribute self time easily. As a result, user printing a table from a profiler trace gets self cpu time. This diff doesn't try to address CUDA self time as CUDA kernels are already getting special care in the profiler. There are also some other minor improvements. Like reporting total CPU time spent, reversed sorting, aggregated data after the table, etc. There is a new unit test added which tests more functionality than previous profiler test Reviewed By: zheng-xq Differential Revision: D14988612 fbshipit-source-id: 2ee6f64f0a4d0b659c6b23c0510bf13aa46f07dc |
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Edward Yang
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173f224570 |
Turn on F401: Unused import warning. (#18598)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3 |
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Edward Yang
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d1497debf2 |
Fix B903 lint: save memory for data classes with slots/namedtuple (#18184)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18184 ghimport-source-id: 2ce860b07c58d06dc10cd7e5b97d4ef7c709a50d Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18184 Fix B903 lint: save memory for data classes with slots/namedtuple** * #18181 Fix B902 lint error: invalid first argument. * #18178 Fix B006 lint errors: using mutable structure in default argument. * #18177 Fix lstrip bug revealed by B005 lint Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14530872 fbshipit-source-id: e26cecab3a8545e7638454c28e654e7b82a3c08a |
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Adam Paszke
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8c3a94eaf2 |
Improve autograd profiler performance (#11773)
Summary: To illustrate the benefits of this commit, I'll use the time/iter I got from one of the JIT benchmarks on my machine. | Run | Time | |----------------------------------------------|-------------------------| | No profiler | 45ms | | With profiler | 56ms | | Use `clock_gettime` instead of `std::chrono` | 48ms | | Touch all pages on block allocation | 48ms (less jitter) | | Use `const char*` instead of `std::string` | 47ms (even less jitter) | Pull Request resolved: https://github.com/pytorch/pytorch/pull/11773 Differential Revision: D9886858 Pulled By: apaszke fbshipit-source-id: 58f926f09e95df0b11ec687763a72b06b66991d0 |
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Michael Carilli
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0c2648830f |
Augment emit_nvtx to help connect backward-pass Function apply calls with their corresponding forward pass ops (#10881)
Summary: Often, we find ourselves looking at some long-running kernel or emit_nvtx range on an nvvp profile and trying to connect it to the offending line in a training script. If the op is in the forward pass that's easy: ops are enqueued explicitly from the Python side, so tracking it down with manual nvtx ranges supplemented by the built-in emit_nvtx ranges is straightforward. If the op is in the backward pass, it's much more difficult. From the Python side, all you can do is wrap loss.backward() in an nvtx range, and if you also use emit_nvtx, the automatic ranges provide only local information. Right now, the only consistent way to connect backward-pass kernels to their associated forward-pass lines of Python is to understand your script line by line, and know exactly where in the backward pass you are. This PR augments the existing nvtx machinery to bridge the gap between forward and backward, allowing connection of backward-pass Function apply calls to the forward-pass operations that required/created those Functions. The method is simple and surgical. During the forward pass, when running with emit_nvtx, the nvtx range for each function in VariableType is tagged with the current sequence number. During the backward pass, the nvtx range associated with each Function's operator() is tagged with that Function's stashed sequence number, which can be compared to "current sequence numbers" from the forward pass to locate the associated op. Double-backward is not a problem. If a backward pass with create_graph = True is underway, the relationship between backward and double-backward is conceptually the same as the relationship between forward and backward: The functions in VariableType still spit out current-sequence-number-tagged ranges, the Function objects they create still stash those sequence numbers, and in the eventual double-backward execution, their operator() ranges are still tagged with the stashed numbers, which can be compared to "current sequence numbers" from the backward pass. Minor caveats: - The sequence number is thread-local, and many VariableType functions (specifically, those without a derivative explicitly defined in derivatives.yaml) don't create an associated function object (instead delegating that to sub-functions further down the call chain, perhaps called from within at::native functions that route back through VariableType by calling at::function_name). So the correspondence of stashed sequence numbers in Function operator() ranges with numbers in forward-pass ranges is not guaranteed to be 1 to 1. However, it's still a vast improvement over the current situation, and I don't think this issue should be a blocker. - Feel free to litigate my use of stringstream in profiler.cpp. I did it because it was easy and clean. If that's too big a hammer, let's figure out something more lightweight. Pull Request resolved: https://github.com/pytorch/pytorch/pull/10881 Differential Revision: D9833371 Pulled By: apaszke fbshipit-source-id: 1844f2e697117880ef5e31394e36e801d1de6088 |
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Richard Zou
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4e446b85fb |
Make profiler.build_table() O(n) rather than O(n^2) (#10969)
Summary: Fixes #10851 Speeds up profiling results dramatically. For the following script: ``` import torch import time ITER = 2000 x = torch.randn(1, 1, requires_grad=True) with torch.autograd.profiler.profile() as prof: y = x for i in range(ITER): y = 3 * y - 2 * y y.backward() start = time.time() print("Done running. Preparing prof") x = str(prof) print("Done preparing prof results") end = time.time() print("Elapsed: {}".format(end - start)) ``` I get 7s before / 0.13s after these changes. cc apaszke Pull Request resolved: https://github.com/pytorch/pytorch/pull/10969 Differential Revision: D9556129 Pulled By: zou3519 fbshipit-source-id: 26b421686f8a42cdaace6382567d403e6385dc12 |
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Ryan Brigden
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8f421159fd |
Fix profiler crash when no events register (#8034)
* Fix profiler crash when no events register When trying to profile, attempting to print the event table throws a vague error because the event list is empty: .... max_name_length = max(len(evt.key) for evt in events) ValueError: max() arg is an empty sequence This change fixes the error by returning an empty string. * Update profiler.py |
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Maxim Berman
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03767b66db |
Add FileNotFoundError to torch._six (#7524)
Add FileNotFoundError for compatibility with Python 2 and use in dataloader. Fixes pytorch/pytorch#6932 |
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Soumith Chintala
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0016dad841
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[pytorch] minor fixes around binary builds (#6291)
* remove patch * check that cuda dev environment is also present before running cpp_extension cuda tests * add OSError to list of exceptions when c++filt is not found |
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Richard Zou
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1449c9f754 |
Update autograd docs (#5907)
* Update autograd docs * Deprecate 'grad_variables' in backward(). Advise to replace with 'grad_tensors'. * Resolve saved_variables/saved_tensors * Tensor section * Address comments * Address comments * Address comments |
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li-roy
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d776c52ff7 | Fix nvprof parsing (#5840) | ||
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Teng Li
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e979b7c940 | Removed redundant import re (#4826) | ||
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peterjc123
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23dc8acbc8 |
Fix missing import and enable test for profiler on Windows (#4522)
* Fix missing import and enable test for profiler on Windows * Skip process when excutable is not found |
