Summary:
This PR adds `torch.float8e4m3fn` support to cuSPARSELt and `to_sparse_semi_structured`.
This will let users to run fp8 + 2:4 sparse matmuls on Hopper GPUs with
cusparselt >= 0.6.2, via to `scaled_mm` API.
```
A = rand_sparse_semi_structured_mask(256, 128, dtype=torch.float16)
B = torch.rand(dense_input_shape, device=device).to(torch.float16).t()
A_fp8, A_scale = to_float8(A)
B_fp8, B_scale = to_float8(B)
dense_result = torch._scaled_mm(
A_fp8, B_fp8,
scale_a=A_scale, scale_b=B_scale,
out_dtype=out_dtype
)
A_fp8_sparse = to_sparse_semi_structured(A_fp8)
sparse_result = torch._scaled_mm(
A_fp8_sparse, B_fp8,
scale_a=A_scale, scale_b=B_scale,
out_dtype=out_dtype
)
```
Note that to keep this consistent with normal torch behavior, calling
`torch.mm(A_fp8_sparse, B_fp8)` will raise a NotImplementedError.
I also turned on cuSPARSELt by default and added CUSPARSELT_MAX_ID to the
backend to make the tests a bit cleaner
Test Plan:
```
python test/test_sparse_semi_structured -k scaled_mm
python test/test_sparse_semi_structured -k fp8
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136397
Approved by: https://github.com/drisspg
**Motivation:**
In Pytorch, Aten vectorization supports multiple platforms, including x86 and Arm, as well as multiple data types. It provides a generic implementation of Vector (Vec) type that allows the programmer to write code packing various primitives (such as floats) within 256bit & 512bits registers. It can be extended to support other ISAs easily by adding more VecISA sub-classes.
**Reference Link:** https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cpu/vec
**This PR:**
* Our goal with this contribution is to add support for SVE backend for Vec in the Aten vectorization for CPU backend which can be benefitted by any ARM architecture supported CPU's that supports SVE.
* More about SVE ISA for ARM: [https://developer.arm.com/Architectures/Scalable Vector Extensions](https://developer.arm.com/Architectures/Scalable%20Vector%20Extensions)
* We are using the ARM C Language Extensions for SVE (https://developer.arm.com/documentation/102699/0100/Optimizing-with-intrinsics ) to accelerate performance for various operators in the SVE backend for Vec.
* Currently we are adding support only for SVE ISA with the vector length of 256 bits (SVE 256). In future, we plan to extend this SVE support for other vector lengths as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119571
Approved by: https://github.com/malfet, https://github.com/snadampal
Co-authored-by: Divya Kotadiya <divya.kotadiya@fujitsu.com>
Summary:
This PR adds in cuSPARSELt as a backend to PyTorch.
It is now possible to see if cuSPARSELt is available and the version if
it is with
```
torch.backends.cusparselt.is_available()
torch.backends.cusparselt.version()
```
Test Plan:
```
python test/test_sparse_semi_structured.py -k test_cusparselt_backend
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128534
Approved by: https://github.com/cpuhrsch, https://github.com/eqy, https://github.com/syed-ahmed
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
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.
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
The current call passes in `['/actual/path']` to os.walk which is a string pointing to no path and thus silently leads to and empty traversal.
There is an unused function just above that handles that, so I guess this is what was supposed to be called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126103
Approved by: https://github.com/suo
This PR fix `torch.backends.xeon.run_cpu` behavior when it is launched from `torchrun` with `--nproc-per-node` parameter.
As a CPU launcher, `run_cpu` would bind cores to each instance it launches using `numactl`, and assign cores to each instance evenly.
However, if we use `torchrun` to start `run_cpu` and use `--nproc-per-node` to create multiple `run_cpu` processes. In this case, each `run_cpu` process would assume it can use all the CPU cores, which causes each `run_cpu` process compete for CPU cores. This results in poor performance.
This PR recognize environment variable `LOCAL_WORLD_SIZE` and `LOCAL_RANK` set by `torchrun`, then use this information to further shard the cores bind to each instance. With this PR, when launched by `torchrun --nproc-per-node ...`, different CPU cores will be bind to different workers, which maximize CPU utilization and application performance.
The specific use case this PR enabled is using TorchServe with DeepSpeed tensor parallel. In this case, TorchServe would run `torchrun --nproc-per-node <tp_size>` to start tensor parallel workers it needed. When run TorchServe on multisocket CPU server with DeepSpeed tensor parallel, we need this PR to achieve best performance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123711
Approved by: https://github.com/jingxu10, https://github.com/ezyang
Following the example of PyTorch supporting a preferred Linalg library (cusolver or magma), this PR introduces a preferred blas library selector of either cublas or cublaslt for CUDA and hipblas or hipblaslt for ROCm via normal hipification of sources.
The default blas implementation remains cublas or hipblas. cublaslt or hipblaslt can be enabled using environment variable TORCH_BLAS_PREFER_CUBLASLT=1 (or TORCH_BLAS_PREFER_HIPBLASLT=1 as an alias) or by calling `torch.backends.cuda.preferred_blas_library(backend="cublaslt")` or as an alias `backend="hipblaslt"`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122106
Approved by: https://github.com/lezcano
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
Automatic fixes that replaces certain list comprehensions with generator ones where appropriate so that they are immediately consumed. This is preview functionality in ruff for rule C419 and it was automatically applied.
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123960
Approved by: https://github.com/malfet
Summary:
Pulling out logging parameters into a logging specs that can be overridden (follow-up changes on possible mechanism)
Why?
Right now the logging approach is quite rigid:
- Requires for log directory to exist and not be empty
- Will create tempdir otherwise,
- Creates subdir for a run
- creates subdir for each attempt
- creates files named as stdout.log, stderr.log, error.json
In some instances some of the users would like to customize the behavior including file names based on context. And we do have right now a mechanism to template multiplexed teed output prefix.
With current changes, users can create custom log spec that can use env variables to change the behavior.
Notes:
Made `LaunchConf.logs_specs` as an optional field that will be bound to `DefaultLogsSpecs` instance. There are large number of clients (code) that use the API directly without using torchrun API. For those cases, we have to explicitly pass LogSpecs implementation if we would like to override the implementation. For the regular torchrun users, we can use pluggable approach proposed in the follow up change.
Test Plan: CI + unit tests
Differential Revision: D54176265
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120691
Approved by: https://github.com/ezyang
By just calling `std_mps` and `mean` in sequence
Move `var_mean` decomp to `ReduceOps.mm`, as it should be faster to skip dispatching to a Python, which one can validate by running the following script:
```python
from timeit import default_timer
import torch
from torch.utils.benchmark import Measurement, Timer
def bench_var_mean(
m, n, k,
dtype = torch.float32,
device:str = "cpu",
) -> Measurement:
setup = f"""
x = torch.rand({m}, {n}, {k}, dtype={dtype}, device="{device}")
"""
t = Timer(
stmt="torch.var_mean(x, dim=1)", setup=setup, language="python", timer=default_timer
)
return t.blocked_autorange()
for x in [100, 1000]:
rc = bench_var_mean(1000, x, 100, device="mps")
print(f"{x:5} : {rc.mean*1e6:.2f} usec")
```
which before the change reports 681 and 1268 usec and after 668 and 684 (which probably means that GPU is not saturated, but overhead from switching between native and interpretable runtimes are shorter.
Fixes https://github.com/pytorch/pytorch/issues/119663
TODOs:
- Refactor the codebase and implement proper composite function (that must be faster)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119777
Approved by: https://github.com/albanD
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
Summary:
Test Plan:
```
lintrunner --take MYPYINDUCTOR --all-files
ok No lint issues.
lintrunner -a
ok No lint issues.
Successfully applied all patches.
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116311
Approved by: https://github.com/int3
# Summary
Simplification of Backend Selection
This PR deprecates the `torch.backends/cuda/sdp_kernel` context manager and replaces it with a new context manager `torch.nn.attention.sdpa_kernel`. This context manager also changes the api for this context manager.
For `sdp_kernel` one would specify the backend choice by taking the negation of what kernel they would like to run. The purpose of this backend manager was to only to be a debugging tool, "turn off the math backend" and see if you can run one of the fused implementations.
Problems:
- This pattern makes sense if majority of users don't care to know anything about the backends that can be run. However, if users are seeking to use this context manager then they are explicitly trying to run a specific backend.
- This is not scalable. We are working on adding the cudnn backend and this API makes it so so that more implementations will need to be turned off if user wants to explicitly run a given backend.
- Discoverability of the current context manager. It is somewhat un-intutive that this backend manager is in backends/cuda/init when this now also controls the CPU fused kernel behavior. I think centralizing to attention namespace will be helpful.
Other concerns:
- Typically backends (kernels) for operators are entirely hidden from users and implementation details of the framework. We have exposed this to users already, albeit not by default and with beta warnings. Does making backends choices even more explicit lead to problems when we potentially want to remove existing backends, (perhaps inputs shapes will get covered by newer backends).
A nice side effect is now that we aren't using the `BACKEND_MAP` in test_transformers many, many dynamo failures are passing for CPU tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114689
Approved by: https://github.com/cpuhrsch
When exporting a model with a convolution kernel on cpu, if mkldnn is disabled and nnpack is enabled, export will go down the nnpack optimized convolution kernel for certain shapes ((code pointer)[cd449e260c/aten/src/ATen/native/Convolution.cpp (L542-L552)]). This means that we will automatically create a guard on that certain shape. If users want to export without any restrictions, one option is to disable nnpack. However, no config function exists for this, so this PR is adding a config function, similar to the `set_mkldnn_enabled` function.
Original context is in https://fb.workplace.com/groups/1075192433118967/posts/1349589822345892/?comment_id=1349597102345164&reply_comment_id=1349677642337110.
To test the flag, the following script runs successfully:
```
import os
import torch
from torchvision.models import ResNet18_Weights, resnet18
torch.set_float32_matmul_precision("high")
model = resnet18(weights=ResNet18_Weights.DEFAULT)
model.eval()
with torch.no_grad():
# device = "cuda" if torch.cuda.is_available() else "cpu"
torch.backends.mkldnn.set_flags(False)
torch.backends.nnpack.set_flags(False) # <--- Added config
device = "cpu"
model = model.to(device=device)
example_inputs = (torch.randn(2, 3, 224, 224, device=device),)
batch_dim = torch.export.Dim("batch", min=2, max=32)
so_path = torch._export.aot_compile(
model,
example_inputs,
# Specify the first dimension of the input x as dynamic
dynamic_shapes={"x": {0: batch_dim}},
# Specify the generated shared library path
options={
"aot_inductor.output_path": os.path.join(os.getcwd(), "resnet18_pt2.so"),
"max_autotune": True,
},
)
```
I'm not sure who to add as reviewer, so please feel free to add whoever is relevant!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116152
Approved by: https://github.com/malfet
This replaces a bunch of unnecessary lambdas with the operator package. This is semantically equivalent, but the operator package is faster, and arguably more readable. When the FURB rules are taken out of preview, I will enable it as a ruff check.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116027
Approved by: https://github.com/malfet
* Enable PERF402. Makes code more efficient and succinct by removing useless list copies that could be accomplished either via a list constructor or extend call. All test cases have noqa added since performance is not as sensitive in that folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115505
Approved by: https://github.com/malfet
Prerequisite for adding more complex type support and FFT operation
Check using `conjugateWithTensor:name:` selector defined as follows
```objc
/// Returns the complex conjugate of the input tensor elements.
///
/// - Parameters:
/// - tensor: The input tensor.
/// - name: An optional string which serves as an identifier for the operation..
/// - Returns: A valid `MPSGraphTensor` object containing the elementwise result of the applied operation.
-(MPSGraphTensor *) conjugateWithTensor:(MPSGraphTensor *) tensor
name:(NSString * _Nullable) name
MPS_AVAILABLE_STARTING(macos(14.0), ios(17.0), tvos(17.0))
MPS_SWIFT_NAME( conjugate(tensor:name:) );
```
- Rename `isOnMacOS13orNewer(unsigned minor)` hook to `isOnMacOSorNewer(major, minor)`
- Replace `torch._C.__mps_is_on_macos_13_or_newer` with `torch._C._mps_is_on_macos_or_newer`
- Add `torch.backends.mps.is_macos_or_newer` public API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115512
Approved by: https://github.com/albanD
Fixes#112633
Fixed errors relating to pydocstyle in the following files. The remaining errors are not covered in this issue. `torch/utils/dlpack.py` was not modified as the errors are relating to the function signature in the first line in the docstring which must be maintained as is for proper Sphinx interpretation.
```python
def from_dlpack(ext_tensor: Any) -> 'torch.Tensor':
"""from_dlpack(ext_tensor) -> Tensor
.....
"""
```
pydocstyle torch/utils/_contextlib.py --count
before: 4
after: 0
pydocstyle torch/backends/mps/__init__.py --count
before: 8
after: 1
**remaining errors**
```
torch/backends/mps/__init__.py:1 at module level:
D104: Missing docstring in public package
```
pydocstyle torch/backends/xeon/run_cpu.py --count
before: 13
after: 1
**remaining errors**
```
torch/backends/xeon/run_cpu.py:864 in public function `main`:
D103: Missing docstring in public function
```
pydocstyle torch/backends/cpu/__init__.py --count
before: 2
after: 1
**remaining errors**
```
torch/backends/cpu/__init__.py:1 at module level:
D104: Missing docstring in public package
```
pydocstyle torch/utils/cpp_backtrace.py --count
before: 4
after: 1
**remaining errors**
```
torch/utils/cpp_backtrace.py:1 at module level:
D100: Missing docstring in public module
```
pydocstyle torch/utils/bundled_inputs.py --count
before: 8
after: 1
**remaining errors**
```
torch/utils/bundled_inputs.py:1 at module level:
D100: Missing docstring in public module
```
pydocstyle torch/utils/file_baton.py --count
before: 8
after: 1
**remaining errors**
```
torch/utils/file_baton.py:1 at module level:
D100: Missing docstring in public module
```
pydocstyle torch/utils/mobile_optimizer.py --count
before: 6
after: 1
**remaining errors**
```
torch/utils/mobile_optimizer.py:8 in public class `LintCode`:
D101: Missing docstring in public class
```
pydocstyle torch/backends/opt_einsum/__init__.py --count
before: 7
after: 5
**remaining errors**
```
torch/backends/opt_einsum/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/opt_einsum/__init__.py:67 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/opt_einsum/__init__.py:77 in public function `flags`:
D103: Missing docstring in public function
torch/backends/opt_einsum/__init__.py:93 in public class `OptEinsumModule`:
D101: Missing docstring in public class
torch/backends/opt_einsum/__init__.py:94 in public method `__init__`:
D107: Missing docstring in __init__
```
pydocstyle torch/utils/_device.py --count
before: 9
after: 6
**remaining errors**
```
torch/utils/_device.py:58 in public class `DeviceContext`:
D101: Missing docstring in public class
torch/utils/_device.py:59 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/_device.py:62 in public method `__enter__`:
D105: Missing docstring in magic method
torch/utils/_device.py:68 in public method `__exit__`:
D105: Missing docstring in magic method
torch/utils/_device.py:73 in public method `__torch_function__`:
D105: Missing docstring in magic method
torch/utils/_device.py:80 in public function `device_decorator`:
D103: Missing docstring in public function
```
pydocstyle torch/utils/_freeze.py --count
before: 15
after: 7
**remaining errors**
```
torch/utils/_freeze.py:77 in public function `indent_msg`:
D103: Missing docstring in public function
torch/utils/_freeze.py:89 in public class `FrozenModule`:
D101: Missing docstring in public class
torch/utils/_freeze.py:100 in public class `Freezer`:
D101: Missing docstring in public class
torch/utils/_freeze.py:101 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/_freeze.py:106 in public method `msg`:
D102: Missing docstring in public method
torch/utils/_freeze.py:185 in public method `get_module_qualname`:
D102: Missing docstring in public method
torch/utils/_freeze.py:206 in public method `compile_string`:
D102: Missing docstring in public method
```
pydocstyle torch/utils/throughput_benchmark.py --count
before: 25
after: 8
**remaining errors**
```
torch/utils/throughput_benchmark.py:1 at module level:
D100: Missing docstring in public module
torch/utils/throughput_benchmark.py:27 in public class `ExecutionStats`:
D101: Missing docstring in public class
torch/utils/throughput_benchmark.py:28 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/throughput_benchmark.py:33 in public method `latency_avg_ms`:
D102: Missing docstring in public method
torch/utils/throughput_benchmark.py:37 in public method `num_iters`:
D102: Missing docstring in public method
torch/utils/throughput_benchmark.py:46 in public method `total_time_seconds`:
D102: Missing docstring in public method
torch/utils/throughput_benchmark.py:50 in public method `__str__`:
D105: Missing docstring in magic method
torch/utils/throughput_benchmark.py:94 in public method `__init__`:
D107: Missing docstring in __init__
```
pydocstyle torch/utils/hooks.py --count
before: 14
after: 11
**remaining errors**
```
torch/utils/hooks.py:1 at module level:
D100: Missing docstring in public module
torch/utils/hooks.py:23 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/hooks.py:34 in public method `remove`:
D102: Missing docstring in public method
torch/utils/hooks.py:44 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:50 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:64 in public method `__enter__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:67 in public method `__exit__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:82 in public function `warn_if_has_hooks`:
D103: Missing docstring in public function
torch/utils/hooks.py:103 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/hooks.py:188 in public method `setup_input_hook`:
D102: Missing docstring in public method
torch/utils/hooks.py:197 in public method `setup_output_hook`:
D102: Missing docstring in public method
```
pydocstyle torch/utils/_traceback.py --count
before: 19
after: 14
**remaining errors**
```
torch/utils/_traceback.py:47 in public function `report_compile_source_on_error`:
D103: Missing docstring in public function
torch/utils/_traceback.py:160 in public class `CapturedTraceback`:
D101: Missing docstring in public class
torch/utils/_traceback.py:163 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/_traceback.py:167 in public method `cleanup`:
D102: Missing docstring in public method
torch/utils/_traceback.py:170 in public method `summary`:
D102: Missing docstring in public method
torch/utils/_traceback.py:182 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/_traceback.py:190 in public method `extract`:
D205: 1 blank line required between summary line and description (found 0)
torch/utils/_traceback.py:190 in public method `extract`:
D400: First line should end with a period (not 't')
torch/utils/_traceback.py:213 in public method `format`:
D205: 1 blank line required between summary line and description (found 0)
torch/utils/_traceback.py:213 in public method `format`:
D400: First line should end with a period (not 'f')
torch/utils/_traceback.py:213 in public method `format`:
D401: First line should be in imperative mood (perhaps 'Format', not 'Formats')
torch/utils/_traceback.py:224 in public method `format_all`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/utils/_traceback.py:247 in private function `_extract_symbolized_tb`:
D205: 1 blank line required between summary line and description (found 0)
torch/utils/_traceback.py:247 in private function `_extract_symbolized_tb`:
D400: First line should end with a period (not 'f')
```
pydocstyle torch/utils/mkldnn.py --count
before: 28
after: 26
**remaining errors**
```
torch/utils/mkldnn.py:1 at module level:
D100: Missing docstring in public module
torch/utils/mkldnn.py:4 in public class `MkldnnLinear`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:5 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:19 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:23 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:29 in public method `forward`:
D102: Missing docstring in public method
torch/utils/mkldnn.py:75 in public class `MkldnnConv1d`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:76 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:82 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:88 in public class `MkldnnConv2d`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:89 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:100 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:110 in public class `MkldnnConv3d`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:111 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:122 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:133 in public class `MkldnnBatchNorm`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:136 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:155 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:163 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:171 in public method `forward`:
D102: Missing docstring in public method
torch/utils/mkldnn.py:184 in public class `MkldnnPrelu`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:185 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:190 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:194 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:199 in public method `forward`:
D102: Missing docstring in public method
torch/utils/mkldnn.py:205 in public function `to_mkldnn`:
D103: Missing docstring in public function
```
pydocstyle torch/utils/weak.py --count
before: 32
after: 30
**remaining errors**
```
torch/utils/weak.py:1 at module level:
D100: Missing docstring in public module
torch/utils/weak.py:42 in public class `WeakIdRef`:
D101: Missing docstring in public class
torch/utils/weak.py:45 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/weak.py:54 in public method `__call__`:
D102: Missing docstring in public method
torch/utils/weak.py:61 in public method `__hash__`:
D105: Missing docstring in magic method
torch/utils/weak.py:64 in public method `__eq__`:
D105: Missing docstring in magic method
torch/utils/weak.py:84 in public class `WeakIdKeyDictionary`:
D101: Missing docstring in public class
torch/utils/weak.py:87 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/weak.py:131 in public method `__delitem__`:
D105: Missing docstring in magic method
torch/utils/weak.py:135 in public method `__getitem__`:
D105: Missing docstring in magic method
torch/utils/weak.py:138 in public method `__len__`:
D105: Missing docstring in magic method
torch/utils/weak.py:145 in public method `__repr__`:
D105: Missing docstring in magic method
torch/utils/weak.py:148 in public method `__setitem__`:
D105: Missing docstring in magic method
torch/utils/weak.py:151 in public method `copy`:
D102: Missing docstring in public method
torch/utils/weak.py:162 in public method `__deepcopy__`:
D105: Missing docstring in magic method
torch/utils/weak.py:172 in public method `get`:
D102: Missing docstring in public method
torch/utils/weak.py:175 in public method `__contains__`:
D105: Missing docstring in magic method
torch/utils/weak.py:182 in public method `items`:
D102: Missing docstring in public method
torch/utils/weak.py:189 in public method `keys`:
D102: Missing docstring in public method
torch/utils/weak.py:198 in public method `values`:
D102: Missing docstring in public method
torch/utils/weak.py:216 in public method `popitem`:
D102: Missing docstring in public method
torch/utils/weak.py:224 in public method `pop`:
D102: Missing docstring in public method
torch/utils/weak.py:228 in public method `setdefault`:
D102: Missing docstring in public method
torch/utils/weak.py:231 in public method `update`:
D102: Missing docstring in public method
torch/utils/weak.py:241 in public method `__ior__`:
D105: Missing docstring in magic method
torch/utils/weak.py:245 in public method `__or__`:
D105: Missing docstring in magic method
torch/utils/weak.py:252 in public method `__ror__`:
D105: Missing docstring in magic method
torch/utils/weak.py:262 in public method `__eq__`:
D105: Missing docstring in magic method
torch/utils/weak.py:276 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/weak.py:280 in public method `__call__`:
D102: Missing docstring in public method
```
@mikaylagawarecki @jbschlosser @svekars
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113311
Approved by: https://github.com/ezyang
