## Overview
This PR allows the profiler users to access `Kineto` and `TorchOp` metadata in JSON string format through a new `metadata_json` attribute in `FunctionEvent` objects, which is triggered through a new `expose_kineto_event_metadata` flag in `ExperimentalConfig`.
## Testing
A unit test was added to validate functionality.
## Documentation
Added/updated function doc strings where appropriate.
## Example output
```python
import torch
from torch.profiler import profile
with profile(experimental_config=torch._C._profiler._ExperimentalConfig(expose_kineto_event_metadata=True)) as prof:
res = torch.mm(torch.rand(1024, 1024), torch.rand(1024, 1024))
for event in prof.events():
print(f'name: {event.key}, metadata: {event.metadata_json}')
```
```
name: aten::rand, metadata: "Ev Idx": 0
name: aten::empty, metadata: "Ev Idx": 1
name: aten::uniform_, metadata: "Ev Idx": 2
name: aten::rand, metadata: "Ev Idx": 3
name: aten::empty, metadata: "Ev Idx": 4
name: aten::uniform_, metadata: "Ev Idx": 5
name: aten::mm, metadata: "Ev Idx": 6
name: aten::resolve_conj, metadata: "Ev Idx": 7
name: aten::resolve_conj, metadata: "Ev Idx": 8
name: aten::resolve_conj, metadata: "Ev Idx": 9
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161624
Approved by: https://github.com/sraikund16
## Overview
This PR adds a kwarg to the `table()` method of the profiler allowing users to specify a time unit to be used for all results in the profiling table. The available options are: `s`, `ms` and `us`. If an invalid unit or no unit is provided, then a time unit is selected based on the size of the value (current default behaviour).
## Testing
A unit test has been added to verify this works correctly.
## Documentation
I couldn't find any documentation specific to the `table()` function beyond doc strings which have been updated.
## Example Output
```
import torch
from torch.profiler import profile
with profile() as prof:
res = torch.mm(torch.rand(1024, 1024), torch.rand(1024, 1024))
print(prof.key_averages().table(time_unit="s"))
print(prof.key_averages().table(time_unit="ms"))
print(prof.key_averages().table(time_unit="us"))
print(prof.key_averages().table())
```
```
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
aten::rand 0.04% 0.000s 10.36% 0.014s 0.007s 2
aten::empty 0.04% 0.000s 0.04% 0.000s 0.000s 2
aten::uniform_ 10.27% 0.014s 10.27% 0.014s 0.007s 2
aten::mm 89.64% 0.119s 89.64% 0.119s 0.119s 1
aten::resolve_conj 0.00% 0.000s 0.00% 0.000s 0.000s 3
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 0.133s
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
aten::rand 0.04% 0.055ms 10.36% 13.735ms 6.868ms 2
aten::empty 0.04% 0.054ms 0.04% 0.054ms 0.027ms 2
aten::uniform_ 10.27% 13.626ms 10.27% 13.626ms 6.813ms 2
aten::mm 89.64% 118.892ms 89.64% 118.896ms 118.896ms 1
aten::resolve_conj 0.00% 0.004ms 0.00% 0.004ms 0.001ms 3
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 132.631ms
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
aten::rand 0.04% 55.495us 10.36% 13735.202us 6867.601us 2
aten::empty 0.04% 54.121us 0.04% 54.121us 27.061us 2
aten::uniform_ 10.27% 13625.586us 10.27% 13625.586us 6812.793us 2
aten::mm 89.64% 118892.284us 89.64% 118895.981us 118895.981us 1
aten::resolve_conj 0.00% 3.697us 0.00% 3.697us 1.232us 3
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 132631.183us
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
aten::rand 0.04% 55.495us 10.36% 13.735ms 6.868ms 2
aten::empty 0.04% 54.121us 0.04% 54.121us 27.061us 2
aten::uniform_ 10.27% 13.626ms 10.27% 13.626ms 6.813ms 2
aten::mm 89.64% 118.892ms 89.64% 118.896ms 118.896ms 1
aten::resolve_conj 0.00% 3.697us 0.00% 3.697us 1.232us 3
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 132.631ms
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157913
Approved by: https://github.com/sraikund16
Summary: Fixes https://github.com/pytorch/pytorch/issues/149311
Test Plan:
Just changes string output
```
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg CPU Mem Self CPU Mem # of Calls
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
void at::native::vectorized_elementwise_kernel<4, at... 0.00% 0.000us 0.00% 0.000us 0.000us 60.993us 0.97% 60.993us 1.848us 0 B 0 B 33
...
```
Rollback Plan:
Differential Revision: D76857251
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156250
Approved by: https://github.com/sanrise
Currently, recorded profiler events for aten ops do not store overload names. It would be useful to know which overloads are actually called to analyse performance.
For example, consider the following dispatch trace which occurs if there is a fallthrough kernel registered for aten::add:
```
[call] op=[aten::add.Tensor], key=[AutogradCPU]
[redispatch] op=[aten::add.Tensor], key=[Undefined]
[call] op=[aten::empty.memory_format], key=[BackendSelect]
[redispatch] op=[aten::empty.memory_format], key=[CPU]
[call] op=[aten::add.out], key=[CPU]
```
In this case, aten::add.out is a child of aten::add.Tensor, however the current profiler trace provides no way to differentiate aten op calls.
See the added unit test for a more detailed example.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143114
Approved by: https://github.com/sraikund16
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
Summary: Users have recently asked that the profiler contains self/total CPU and device percentages to FunctionEvents so that teams can process the data procedurely. Some of it could be done mathematically via subroutines but since we already have the information in the _build_table, lets build it there.
Test Plan: Check that we have the same table as before but also check that the parameters we check also have the expected values
Differential Revision: D62210351
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135155
Approved by: https://github.com/shanw-meta, https://github.com/kit1980
Summary: On the autograd side of things, we are currently saving the kwinputs but we aren't doing anything with them on the profiler side. This diff enables the use of the kwinputs for both FunctionEvents and Chrome Traces.
Test Plan: Added unit testing for both chrome traces and FunctionEvents. Used RecordFunctionFast to test kwinputs since test already had kwargs being passed in but not tested.
Differential Revision: D59472345
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130373
Approved by: https://github.com/davidberard98
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves#126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
Summary:
1.Package public headers of kineto if USE_KINETO so that they can be used by PrivateUse1 user.
2.Add PrivateUse1 key to ActivityType.
3. Support PrivateUse1 key in function deviceTypeFromActivity and _supported_activities.
4. Fix some bugs when processing profiler results.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124818
Approved by: https://github.com/aaronenyeshi
This PR unifies the CUDA, XPU and PrivateUse1 in the torch profiler. Now CUDA, XPU and PrivateUse1 can together use string object `use_device` to distinguish each other and share one device path for calculating kineto time durations and memory statistics for post processing.
#suppress-api-compatibility-check
Co-authored-by: Aaron Enye Shi <enye.shi@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123247
Approved by: https://github.com/aaronenyeshi
Update ruff to 0.4.1 .
This version fixes a lot false negatives/false positives, is 20-40% faster, and has various other bug fixes.
Below is a before and after table showing the execution time of ruff lint and ruff format in milliseconds courtesy of https://astral.sh/blog/ruff-v0.4.0
| Repository | Linter (v0.3) | Linter (v0.4) | Formatter (v0.3) | Formatter (v0.4) |
|----------------------------------------------------|---------------|---------------|------------------|------------------|
| [pytorch/pytorch](https://github.com/pytorch/pytorch) | 328.7 | 251.8 | 351.1 | 274.9 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124549
Approved by: https://github.com/ezyang
This PR unifies the CUDA, XPU and PrivateUse1 in the torch profiler. Now CUDA, XPU and PrivateUse1 can together use string object `use_device` to distinguish each other and share one device path for calculating kineto time durations and memory statistics for post processing.
#suppress-api-compatibility-check
Co-authored-by: Aaron Enye Shi <enye.shi@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123247
Approved by: https://github.com/aaronenyeshi, https://github.com/gujinghui
Summary:
1.Package public headers of kineto if USE_KINETO so that they can be used by PrivateUse1 user.
2.Add PrivateUse1 key to ActivityType.
3. Support PrivateUse1 key in function deviceTypeFromActivity and _supported_activities.
4. Fix some bugs when processing profiler results.
Co-authored-by: albanD <desmaison.alban@gmail.com>
Co-authored-by: Aaron Shi <enye.shi@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120556
Approved by: https://github.com/aaronenyeshi
Summary:
Kineto traces use microsecond level granularity because of chrome tracing defaults to that precision. Fix by adding preprocessor flag to TARGETS and BUCK files. Also remove any unnecessary ns to us conversions made in the profiler itself.
This diff contains profiler changes only. Libkineto changes found in D54964435.
Test Plan:
Check JSON and chrome tracing to make sure values are as expected. Tracing with flags enabled should have ns precision. Tracings without flags should be same as master.
Zoomer: https://www.internalfb.com/intern/zoomer/?profiling_run_fbid=796886748550189
Ran key_averages() to make sure FunctionEvent code working as expected:
-- ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls
ProfilerStep* 0.74% 3.976ms 64.40% 346.613ms 69.323ms 0.000us 0.00% 61.710ms 12.342ms 5
Optimizer.zero_grad#SGD.zero_grad 0.76% 4.109ms 0.76% 4.109ms 821.743us 0.000us 0.00% 0.000us 0.000us 5
## forward ## 6.89% 37.057ms 27.19% 146.320ms 29.264ms 0.000us 0.00% 58.708ms 11.742ms 5
aten::conv2d 0.22% 1.176ms 7.74% 41.658ms 157.199us 0.000us 0.00% 27.550ms 103.962us 265
aten::convolution 0.79% 4.273ms 7.52% 40.482ms 152.762us 0.000us 0.00% 27.550ms 103.962us 265
aten::_convolution 0.69% 3.688ms 6.73% 36.209ms 136.637us 0.000us 0.00% 27.550ms 103.962us 265
aten::cudnn_convolution 6.04% 32.520ms 6.04% 32.520ms 122.719us 27.550ms 8.44% 27.550ms 103.962us 265
aten::add_ 2.42% 13.045ms 2.42% 13.045ms 30.694us 12.700ms 3.89% 12.700ms 29.882us 425
aten::batch_norm 0.19% 1.027ms 8.12% 43.717ms 164.971us 0.000us 0.00% 16.744ms 63.185us 265
aten::_batch_norm_impl_index 0.31% 1.646ms 7.93% 42.691ms 161.096us 0.000us 0.00% 16.744ms 63.185us 265
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Differential Revision: D55925068
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123650
Approved by: https://github.com/aaronenyeshi
Summary:
Kineto traces use microsecond level granularity because of chrome tracing defaults to that precision. Fix by adding preprocessor flag to TARGETS and BUCK files. Also remove any unnecessary ns to us conversions made in the profiler itself.
This diff contains profiler changes only. Libkineto changes found in D54964435.
Test Plan:
Check JSON and chrome tracing to make sure values are as expected. Tracing with flags enabled should have ns precision. Tracings without flags should be same as master.
Tracing with flags enabled: https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree/traces/dynocli/devvm2185.cco0.facebook.com/rank-0.Mar_18_14_37_22.4155151.pt.trace.json.gz&bucket=gpu_traces
Tracing without flags enabled: https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree/traces/dynocli/devvm2185.cco0.facebook.com/rank-0.Mar_18_14_39_15.4166047.pt.trace.json.gz&bucket=gpu_traces
Tracing on main: https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree/traces/dynocli/devvm2185.cco0.facebook.com/rank-0.Mar_18_14_42_43.4177559.pt.trace.json.gz&bucket=gpu_traces
Ran key_averages() to make sure FunctionEvent code working as expected:
-- ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls
ProfilerStep* 0.74% 3.976ms 64.40% 346.613ms 69.323ms 0.000us 0.00% 61.710ms 12.342ms 5
Optimizer.zero_grad#SGD.zero_grad 0.76% 4.109ms 0.76% 4.109ms 821.743us 0.000us 0.00% 0.000us 0.000us 5
## forward ## 6.89% 37.057ms 27.19% 146.320ms 29.264ms 0.000us 0.00% 58.708ms 11.742ms 5
aten::conv2d 0.22% 1.176ms 7.74% 41.658ms 157.199us 0.000us 0.00% 27.550ms 103.962us 265
aten::convolution 0.79% 4.273ms 7.52% 40.482ms 152.762us 0.000us 0.00% 27.550ms 103.962us 265
aten::_convolution 0.69% 3.688ms 6.73% 36.209ms 136.637us 0.000us 0.00% 27.550ms 103.962us 265
aten::cudnn_convolution 6.04% 32.520ms 6.04% 32.520ms 122.719us 27.550ms 8.44% 27.550ms 103.962us 265
aten::add_ 2.42% 13.045ms 2.42% 13.045ms 30.694us 12.700ms 3.89% 12.700ms 29.882us 425
aten::batch_norm 0.19% 1.027ms 8.12% 43.717ms 164.971us 0.000us 0.00% 16.744ms 63.185us 265
aten::_batch_norm_impl_index 0.31% 1.646ms 7.93% 42.691ms 161.096us 0.000us 0.00% 16.744ms 63.185us 265
------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Differential Revision: D55087993
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122425
Approved by: https://github.com/aaronenyeshi
This pull request is writing to provide an update on the recent advancements made in the PyTorch profiler with regards to XPU backend support. Following the successful merge of a previous pull request #94502 that established a pathway for the XPU backend within PyTorch, we have now taken steps to enhance the profiler's capabilities for handling and displaying profile data directly related to the XPU backend.
# Motivation
The current pull request builds upon this foundation by refining the profiler's data processing scripts, particularly `profiler_util.py`, to accommodate XPU backend-specific profile data. The aim is to align the handling and presentation of this data with that of the CUDA backend, offering users a consistent experience across different device profiles. This includes generating outputs such as JSON files compatible with Chrome trace tooling, among other formats.
# Principles
1. Minimal Impact: The modifications introduced should support XPU backend data with minimal disruption to the existing profiling scripts.
2. Consistency: Changes should maintain stylistic and functional consistency with existing `CUDA` and `privateuse1` pathways, ensuring no adverse effects on other logic paths.
3. Exclusivity: Ensure that the new XPU pathway does not interfere with or impede other pathways.
# Solutions
### a. Pathway Identification:
Introduction of a `use_xpu` flag within `torch.autograd.profiler.profile` interfaces to distinguish XPU-specific profiling.
### b. `use_device` Logic Revision:
With the introduction of the XPU pathway, `use_device` no longer implies a binary relationship with `use_cuda`. Consequently, we have revised related logic to remove implicit assertions and establish independent device distinction.
### c. Kernel List Segregation:
To accommodate the non-binary nature of device pathways, we have enabled kernel lists to identify specific device affiliations through separate list objects.
### d. Formatted Output:
To ensure output consistency, we have employed code duplication and keyword substitution techniques to facilitate the formatting of XPU-related profile data.
# Additional Enhancements
### a. Enumerations in `.pyi` Files:
Added recognition items for `DeviceType` and `ProfilerActivity` specific to XPU.
### b. Correct DeviceType Returns:
Revised `deviceTypeFromActivity` logic to accurately differentiate between device backends, even when they share common flags such as `libkineto::ActivityType::GPU_MEMCPY`.
### c. Bug Fixes in `cuda_corr_map`:
Addressed a corner case where erroneous parent-child event relationships were formed due to shared function event identifiers. The solution involves refining `cuda_corr_map` processing to prevent a function event from being misidentified as both the linker and linkee.
# Further Abstraction
Looking forward, we acknowledge the potential for further abstraction in the codebase. The current changes necessitated by XPU support have highlighted opportunities for reducing redundancy by consolidating naming conventions and utilizing a singular `device` naming system that relies on `DeviceType` attributes or string flags for differentiation. This would involve significant refactoring to replace device-specific flags and variables. This topic needs further discussions about whether we could and when we should deprecate all those flags and variables named with `cuda`.
# Next Pull Request
The next pull request will be contingent on Kineto's adoption of Intel's forthcoming PTI-sdk library, which will enable direct usage of XPU-related tracers. Subsequent modifications to `libkineto_init()` will aim to endow PyTorch running on XPU backends with comprehensive profiling capabilities on XPU devices.
We appreciate your attention to these enhancements and welcome any feedback or questions you may have regarding these developments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120185
Approved by: https://github.com/aaronenyeshi, https://github.com/gujinghui
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
It appears that `mypy` is now checking a few more previously-unchecked files; these files
are being found via import-following. Not sure exactly why they weren't being checked before.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114160
Approved by: https://github.com/eellison
ghstack dependencies: #114162
