## Summary
- add a CuBLASReductionOption enum so the CUDA context can track reduced-precision and split-K options
- extend the Python bindings, backend helpers, and docs to accept an optional allow_splitk argument for fp16/bf16 matmul controls
- update cuBLAS/cuBLASLt call sites plus dynamo guards and tests to respect the new combinations
## Testing
- python test/test_cuda.py TestCuda.test_cublas_allow_fp16_reduced_precision_reduction_get_set -v *(fails: ModuleNotFoundError: No module named 'psutil')*
------
https://chatgpt.com/codex/tasks/task_e_68e404623178832f8a3e1d34e1e175da
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164766
Approved by: https://github.com/malfet, https://github.com/albanD
Based on the [conversation](https://github.com/pytorch/pytorch/issues/121791), we plan to drop the "highest, high, medium" to represent fp32 internal computation data types . Instead, we will directly use the algorithm to represent it.
### Design Choice: Directly use algorithms name like "TF32", "BF16".
#### Pros
- The names are more informative. 'tf32' is more informative than a simple "high".
- Easier to extend new algorithm like `tf32x3`
#### Cons
- "HIGHEST, HIGH, MEDIUM" indicated the relative precision between different algorithms. However, we can have more documents to discuss them.
### We provide a layered structure for backends/operators.
('f32' is short for 'fp32_precision')
### We provide 3 fp32 compute precision can be set:
- **"ieee"**: Not allowed to use any other internal computation data types .
- **"tf32"**: Allowed to use tf32 as internal computation data types.
- **"bf16"**: Allowed to use bf16 as internal computation data types.
- **"none"**: Precision's are not set. Can be override by its father node.
### Overriding Precision Settings
Child node can be override by its father node if it is set to default.
For current default settings:
```
backend = generic, op = all, precision setting = none
backend = cuda, op = all, precision setting = none
backend = cuda, op = conv, precision setting = tf32
backend = cuda, op = rnn, precision setting = tf32
backend = cuda, op = matmul, precision setting = none
backend = matmul, op = all, precision setting = none
backend = matmul, op = conv, precision setting = none
backend = matmul, op = rnn, precision setting = none
backend = matmul, op = matmul, precision setting = none
```
- If the user set `torch.backends.mkldnn.fp32_precision="bf16"`, his child nodes `torch.backends.mkldnn.matmul.fp32_precision` / `torch.backends.mkldnn.conv.fp32_precision` / `torch.backends.mkldnn.rnn.fp32_precision` will also be override to "bf16".
- If the user set `torch.backends.fp32_precision="bf16"`, `torch.backends.mkldnn.fp32_precision` and his child nodes will also we override to "bf16".
### Backward Compatible
Since new API allow user to have more fine-grained control. There will be some conflict. For example, previous `torch.backends.cudnn.allow_tf32` are not enough to represent the status for `torch.backends.cudnn.rnn.fp32_precision="ieee"` and `torch.backends.cudnn.conv.fp32_precision="tf32"`. Therefore, our goal for backward compatible is
- If the user only uses previous APIs, it will work as previous expectations.
- If the user use **new** API to change the status to an **un-representable** status for old API, and try to access the status by **old** API. We will raise Runtime Error and point the document for user.
### Test Plan
```
python test/test_cuda.py -k test_fp32_precision_with_tf32
python test/test_cuda.py -k test_fp32_precision_with_float32_matmul_precision
python test/test_cuda.py -k test_invalid_status_for_legacy_api
python test/test_mkldnn.py -k test_mlkdnn_get_set
python test/test_mkldnn.py -k test_generic_precision
python test/test_mkldnn.py -k test_invalid
python test/test_mkldnn.py -k test_default_use_parent
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125888
Approved by: https://github.com/jgong5, https://github.com/albanD
Co-authored-by: Jiang, Yanbing <yanbing.jiang@intel.com>
Based on the [conversation](https://github.com/pytorch/pytorch/issues/121791), we plan to drop the "highest, high, medium" to represent fp32 internal computation data types . Instead, we will directly use the algorithm to represent it.
### Design Choice: Directly use algorithms name like "TF32", "BF16".
#### Pros
- The names are more informative. 'tf32' is more informative than a simple "high".
- Easier to extend new algorithm like `tf32x3`
#### Cons
- "HIGHEST, HIGH, MEDIUM" indicated the relative precision between different algorithms. However, we can have more documents to discuss them.
### We provide a layered structure for backends/operators.
('f32' is short for 'fp32_precision')
### We provide 3 fp32 compute precision can be set:
- **"ieee"**: Not allowed to use any other internal computation data types .
- **"tf32"**: Allowed to use tf32 as internal computation data types.
- **"bf16"**: Allowed to use bf16 as internal computation data types.
- **"none"**: Precision's are not set. Can be override by its father node.
### Overriding Precision Settings
Child node can be override by its father node if it is set to default.
For current default settings:
```
backend = generic, op = all, precision setting = none
backend = cuda, op = all, precision setting = none
backend = cuda, op = conv, precision setting = tf32
backend = cuda, op = rnn, precision setting = tf32
backend = cuda, op = matmul, precision setting = none
backend = matmul, op = all, precision setting = none
backend = matmul, op = conv, precision setting = none
backend = matmul, op = rnn, precision setting = none
backend = matmul, op = matmul, precision setting = none
```
- If the user set `torch.backends.mkldnn.fp32_precision="bf16"`, his child nodes `torch.backends.mkldnn.matmul.fp32_precision` / `torch.backends.mkldnn.conv.fp32_precision` / `torch.backends.mkldnn.rnn.fp32_precision` will also be override to "bf16".
- If the user set `torch.backends.fp32_precision="bf16"`, `torch.backends.mkldnn.fp32_precision` and his child nodes will also we override to "bf16".
### Backward Compatible
Since new API allow user to have more fine-grained control. There will be some conflict. For example, previous `torch.backends.cudnn.allow_tf32` are not enough to represent the status for `torch.backends.cudnn.rnn.fp32_precision="ieee"` and `torch.backends.cudnn.conv.fp32_precision="tf32"`. Therefore, our goal for backward compatible is
- If the user only uses previous APIs, it will work as previous expectations.
- If the user use **new** API to change the status to an **un-representable** status for old API, and try to access the status by **old** API. We will raise Runtime Error and point the document for user.
### Test Plan
```
python test/test_cuda.py -k test_fp32_precision_with_tf32
python test/test_cuda.py -k test_fp32_precision_with_float32_matmul_precision
python test/test_cuda.py -k test_invalid_status_for_legacy_api
python test/test_mkldnn.py -k test_mlkdnn_get_set
python test/test_mkldnn.py -k test_generic_precision
python test/test_mkldnn.py -k test_invalid
python test/test_mkldnn.py -k test_default_use_parent
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125888
Approved by: https://github.com/jgong5, https://github.com/albanD
Co-authored-by: Jiang, Yanbing <yanbing.jiang@intel.com>
Replace https://github.com/pytorch/pytorch/pull/138947 for re-import.
Replaces https://github.com/ROCm/pytorch/pull/1592
This PR contains the initial implementation of SDPA with composable_kernel backend. The CK path can be forced by simply calling torch.backends.cuda.preferred_rocm_fa_library("ck"). Similarly, you can force the incumbent aotriton implementation by passing in "aotriton" or "default". As you'd expect, not setting this option will result in aotriton to be used as the backend. In the case of CK, if pytorch deems flash attention usable, then it will use the CK path in all the same places aotriton would have been used. This PR makes no changes to the heuristics which select which attention scheme to use (i.e. flash attention vs memory efficient attention vs math etc etc). It only gets called when flash attention is both enabled (via USE_FLASH_ATTENTION) and is selected at runtime by the existing heuristics.
Files located in pytorch/aten/src/ATen/native/transformers/hip/flash_attn/ck/mha* have been pulled from https://github.com/Dao-AILab/flash-attention courtesy of @tridao's hard work who is the co-author
NOTE: In order to use this backend, the user MUST set USE_CK_FLASH_ATTENTION=1 in their environment when they build PyTorch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143695
Approved by: https://github.com/malfet
Co-authored-by: Andy Lugo <Andy.LugoReyes@amd.com>
Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Replaces https://github.com/ROCm/pytorch/pull/1592
This PR contains the initial implementation of SDPA with composable_kernel backend. The CK path can be forced by simply calling `torch.backends.cuda.preferred_rocm_fa_library("ck")`. Similarly, you can force the incumbent aotriton implementation by passing in "aotriton" or "default". As you'd expect, not setting this option will result in aotriton to be used as the backend. In the case of CK, if pytorch deems flash attention usable, then it will use the CK path in all the same places aotriton would have been used. This PR makes no changes to the heuristics which select which attention scheme to use (i.e. flash attention vs memory efficient attention vs math etc etc). It only gets called when flash attention is both enabled (via `USE_FLASH_ATTENTION`) and is selected at runtime by the existing heuristics.
Files located in pytorch/aten/src/ATen/native/transformers/hip/flash_attn/ck/mha* have been pulled from https://github.com/Dao-AILab/flash-attention courtesy of @tridao's hard work who is the co-author
NOTE: In order to use this backend, the user MUST set USE_CK_FLASH_ATTENTION=1 in their environment when they build PyTorch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138947
Approved by: https://github.com/pruthvistony, https://github.com/xw285cornell, https://github.com/leitian
Co-authored-by: Xiaodong Wang <xw285@cornell.edu>
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
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
# 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
Fixes#112632
Before: 171
```
torch/backends/_nnapi/prepare.py:24 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/_nnapi/prepare.py:46 in public method `init`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:60 in public method `forward`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:94 in public function `convert_model_to_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:153 in public function `process_for_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:177 in private nested class `ShapeComputeModule`:
D400: First line should end with a period (not 'n')
torch/backends/_nnapi/serializer.py:19 in public class `NNAPI_OperandCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:35 in public class `NNAPI_OperationCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:133 in public class `NNAPI_FuseCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:140 in public class `OperandValueSourceType`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:150 in public class `TorchScalarTypes`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:154 in public function `approx_equal`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:158 in public function `tensor_size`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:172 in public function `change_element`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:194 in public class `DimOrder`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:225 in public method `use_nchw`:
D102: Missing docstring in public method
torch/backends/_nnapi/serializer.py:233 in public function `broadcast_shapes`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:260 in public function `get_conv_pool_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:284 in public function `fix_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:301 in public function `reverse_map_dim`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:312 in public function `flex_name`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:1337 in private method `_do_add_binary`:
D400: First line should end with a period (not 's')
torch/backends/_nnapi/serializer.py:1337 in private method `_do_add_binary`:
D401: First line should be in imperative mood; try rephrasing (found 'Helper')
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
D202: No blank lines allowed after function docstring (found 1)
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
D400: First line should end with a period (not ':')
torch/backends/cuda/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D209: Multi-line docstring closing quotes should be on a separate line
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D400: First line should end with a period (not 's')
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cuda/__init__.py:37 in public class `cuFFTPlanCacheAttrContextProp`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:40 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:44 in public method `__get__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:47 in public method `__set__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:54 in public class `cuFFTPlanCache`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:54 in public class `cuFFTPlanCache`:
D400: First line should end with a period (not 'e')
torch/backends/cuda/__init__.py:60 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:73 in public method `clear`:
D102: Missing docstring in public method
torch/backends/cuda/__init__.py:78 in public class `cuFFTPlanCacheManager`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:78 in public class `cuFFTPlanCacheManager`:
D400: First line should end with a period (not ',')
torch/backends/cuda/__init__.py:89 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:93 in public method `__getitem__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:106 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:109 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:116 in public class `cuBLASModule`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:117 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:126 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:147 in public function `preferred_linalg_library`:
D202: No blank lines allowed after function docstring (found 1)
torch/backends/cuda/__init__.py:204 in public class `SDPBackend`:
D204: 1 blank line required after class docstring (found 0)
torch/backends/cudnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cudnn/__init__.py:81 in public function `version`:
D400: First line should end with a period (not 'N')
torch/backends/cudnn/__init__.py:81 in public function `version`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cudnn/__init__.py:95 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cudnn/__init__.py:99 in public function `is_acceptable`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:122 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:150 in public function `flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:174 in public class `CudnnModule`:
D101: Missing docstring in public class
torch/backends/cudnn/__init__.py:175 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkl/__init__.py:5 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/mkl/__init__.py:14 in public class `verbose`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/mkl/__init__.py:14 in public class `verbose`:
D400: First line should end with a period (not 'y')
torch/backends/mkl/__init__.py:41 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:44 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkl/__init__.py:53 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkldnn/__init__.py:9 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/mkldnn/__init__.py:19 in public class `verbose`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/mkldnn/__init__.py:19 in public class `verbose`:
D400: First line should end with a period (not 'y')
torch/backends/mkldnn/__init__.py:47 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkldnn/__init__.py:50 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:59 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:64 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:71 in public function `flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:81 in public class `MkldnnModule`:
D101: Missing docstring in public class
torch/backends/mkldnn/__init__.py:82 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/openmp/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/openmp/__init__.py:5 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/intrinsic/qat/modules/conv_fused.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/intrinsic/qat/modules/linear_fused.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/intrinsic/qat/modules/linear_relu.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/dynamic/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/dynamic/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/embedding_ops.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantizable/modules/activation.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantizable/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/sparse.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/utils.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/functional.py:1 at module level:
D400: First line should end with a period (not 'l')
torch/nn/quantized/modules/__init__.py:1 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/activation.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/batchnorm.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/dropout.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/embedding_ops.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/functional_modules.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/normalization.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/utils.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/utils/_expanded_weights/conv_utils.py:13 in public function `conv_picker`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:23 in public function `conv_args_and_kwargs`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:31 in public function `conv_normalizer`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:35 in public function `conv_input_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:43 in public function `int_padding_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:59 in public function `conv_padding_for_same`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:66 in public function `conv_backward`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:131 in public function `conv_unfold_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:166 in public function `conv_group_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
D401: First line should be in imperative mood (perhaps 'Extract', not 'Extracts')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:6 in public function `is_batch_first`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
D300: Use """triple double quotes""" (found '''-quotes)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
D400: First line should end with a period (not 'e')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
D300: Use """triple double quotes""" (found '''-quotes)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
D400: First line should end with a period (not ')')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:84 in public function `maybe_scale_by_batch_size`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:90 in public function `set_grad_sample_if_exists`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:108 in public function `unpack_expanded_weight_or_tensor`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
D400: First line should end with a period (not 't')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
D401: First line should be in imperative mood (perhaps 'Calculate', not 'Calculates')
torch/nn/utils/convert_parameters.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D400: First line should end with a period (not 'd')
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D401: First line should be in imperative mood; try rephrasing (found 'This')
torch/nn/utils/rnn.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/rnn.py:28 in public class `PackedSequence`:
D204: 1 blank line required after class docstring (found 0)
torch/nn/utils/rnn.py:63 in public method `__new__`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:73 in public method `pin_memory`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:80 in public method `cuda`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:87 in public method `cpu`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:94 in public method `double`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:97 in public method `float`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:100 in public method `half`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:103 in public method `long`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:106 in public method `int`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:109 in public method `short`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:112 in public method `char`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:115 in public method `byte`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:119 in public method `to`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:119 in public method `to`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
torch/nn/utils/rnn.py:146 in public method `is_cuda`:
D400: First line should end with a period (not 'u')
torch/nn/utils/rnn.py:150 in public method `is_pinned`:
D400: First line should end with a period (not 'y')
torch/nn/utils/rnn.py:150 in public method `is_pinned`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/utils/rnn.py:198 in public function `invert_permutation`:
D103: Missing docstring in public function
torch/nn/utils/rnn.py:274 in public function `pad_packed_sequence`:
D401: First line should be in imperative mood (perhaps 'Pad', not 'Pads')
torch/nn/utils/rnn.py:347 in public function `pad_sequence`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:347 in public function `pad_sequence`:
D400: First line should end with a period (not '`')
torch/nn/utils/rnn.py:408 in public function `unpad_sequence`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:408 in public function `unpad_sequence`:
D400: First line should end with a period (not 's')
torch/nn/utils/rnn.py:454 in public function `pack_sequence`:
D400: First line should end with a period (not 's')
torch/nn/utils/rnn.py:490 in public function `unpack_sequence`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:490 in public function `unpack_sequence`:
D400: First line should end with a period (not 's')
171
```
After: 81
```
torch/backends/_nnapi/prepare.py:24 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/_nnapi/prepare.py:46 in public method `init`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:60 in public method `forward`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:94 in public function `convert_model_to_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:153 in public function `process_for_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:19 in public class `NNAPI_OperandCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:35 in public class `NNAPI_OperationCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:133 in public class `NNAPI_FuseCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:140 in public class `OperandValueSourceType`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:150 in public class `TorchScalarTypes`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:154 in public function `approx_equal`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:158 in public function `tensor_size`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:172 in public function `change_element`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:194 in public class `DimOrder`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:225 in public method `use_nchw`:
D102: Missing docstring in public method
torch/backends/_nnapi/serializer.py:233 in public function `broadcast_shapes`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:260 in public function `get_conv_pool_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:284 in public function `fix_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:301 in public function `reverse_map_dim`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:312 in public function `flex_name`:
D103: Missing docstring in public function
torch/backends/cuda/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cuda/__init__.py:39 in public class `cuFFTPlanCacheAttrContextProp`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:42 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:46 in public method `__get__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:49 in public method `__set__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:63 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:76 in public method `clear`:
D102: Missing docstring in public method
torch/backends/cuda/__init__.py:91 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:95 in public method `__getitem__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:108 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:111 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:118 in public class `cuBLASModule`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:119 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:128 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cudnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cudnn/__init__.py:99 in public function `is_acceptable`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:122 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:150 in public function `flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:174 in public class `CudnnModule`:
D101: Missing docstring in public class
torch/backends/cudnn/__init__.py:175 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkl/__init__.py:42 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:45 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkl/__init__.py:54 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkldnn/__init__.py:48 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkldnn/__init__.py:51 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:60 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:65 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:72 in public function `flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:82 in public class `MkldnnModule`:
D101: Missing docstring in public class
torch/backends/mkldnn/__init__.py:83 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/openmp/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/nn/utils/_expanded_weights/conv_utils.py:13 in public function `conv_picker`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:23 in public function `conv_args_and_kwargs`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:31 in public function `conv_normalizer`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:35 in public function `conv_input_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:43 in public function `int_padding_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:59 in public function `conv_padding_for_same`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:66 in public function `conv_backward`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:131 in public function `conv_unfold_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:166 in public function `conv_group_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:6 in public function `is_batch_first`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:87 in public function `maybe_scale_by_batch_size`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:93 in public function `set_grad_sample_if_exists`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:111 in public function `unpack_expanded_weight_or_tensor`:
D103: Missing docstring in public function
torch/nn/utils/convert_parameters.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/rnn.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/rnn.py:64 in public method `__new__`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:74 in public method `pin_memory`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:81 in public method `cuda`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:88 in public method `cpu`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:95 in public method `double`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:98 in public method `float`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:101 in public method `half`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:104 in public method `long`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:107 in public method `int`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:110 in public method `short`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:113 in public method `char`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:116 in public method `byte`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:198 in public function `invert_permutation`:
D103: Missing docstring in public function
81
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112695
Approved by: https://github.com/mikaylagawarecki
