Summary:
Replace for loop with for `irange` loop. Also fix some unused variable warnings in range loop cases
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62928
Reviewed By: driazati
Differential Revision: D30171904
Pulled By: malfet
fbshipit-source-id: 1b437a0f7e3515f4a2e324f3450e93312f1933ae
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Add a new device type 'XPU' ('xpu' for lower case) to PyTorch. Changes are needed for code related to device model and kernel dispatch, e.g. DeviceType, Backend and DispatchKey etc.
https://github.com/pytorch/pytorch/issues/48246
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49786
Reviewed By: mrshenli
Differential Revision: D25893962
Pulled By: ezyang
fbshipit-source-id: 7ff0a316ee34cf0ed6fc7ead08ecdeb7df4b0052
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50228
`fastmod -m 'expect(<((at|c10)::)?\w+Type>\(\)\s*)->'
'expectRef${1}.'`
Presuming it builds, this is a safe change: the result of `expect()`
wasn't being saved anywhere, so we didn't need it, so we can take a
reference instead of a new `shared_ptr`.
ghstack-source-id: 119782961
Test Plan: CI
Reviewed By: SplitInfinity
Differential Revision: D25837374
fbshipit-source-id: 86757b70b1520e3dbaa141001e7976400cdd3b08
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48322
Disable old fuser internally. I would like to find where we are inadvertently setting the old fuser, but in the meantime I would like to land a diff that I know will 100% cause it not to be run, and verify that it fixes the issue.
Test Plan: sandcastle
Reviewed By: ZolotukhinM
Differential Revision: D25126202
fbshipit-source-id: 5a4d0742f5f829e536f50e7ede1256c94dd05232
Summary:
Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript.
This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work.
Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message.
This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907
Reviewed By: ngimel
Differential Revision: D22778301
Pulled By: gmagogsfm
fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad
Summary:
Fixes issue where
- top level fuser's block_ was captured by callback due to [&] capture,
- recursive/nested fusers would compare erroneously to top-level block_ instead of own block_
Closes (https://github.com/pytorch/pytorch/issues/39810)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41560
Reviewed By: Krovatkin
Differential Revision: D22583196
Pulled By: wconstab
fbshipit-source-id: 8f543cd9ea00e116cf3e776ab168cdd9fed69632
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37106
Recomputing the aliasdb on every fusion iteration + in every subblock
is hugely expensive. Instead, update it in-place when doing fusion.
The graph fuser pass operates by pushing nodes into a fusion group. So
we start with
```
x, y = f(a, b, c)
```
and end with:
```
x_out, y_out = prim::fusionGroup(a, b, c)
x_in, y_in = f(a_in, b_in, c_in)
-> x_in, y_in
```
We destroy the `x` and `y` `Value*`s in the process. This operation is
easy to express as an update to the aliasDb--`x_out` just takes on all
the aliasing information `x` used to have. In particular, since we know
`f` and `prim::fusionGroup` are purely functional, we don't have to mess
with any write information.
This PR is the bare minimum to get this working, in the interest of
unscrewing the compilation times ASAP.
Followups I want to do:
- We don't have a way of expressing deletion of values in AliasDb. In
`graph_fuser.cpp` we sometimes construct nodes that we end up throwing
away, and we are littering `MemoryDAG` with references to dangling
pointers. Because of the way the pass works, it's fine, but this is
fragile so I want to fix it.
- We should decouple alias analysis from write tracking, to simplify the
job of keeping the write caches consistent as we mutate the aliasing
information.
- the tensorexpr fuser doesn't do this and thus is incorrect today, we
need to update it to work.
Test Plan: Imported from OSS
Differential Revision: D21219179
Pulled By: suo
fbshipit-source-id: 8ae5397b3a0ad90edec2fbc555647091f1ad5284
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33705
The fact that there were two overloads appears to be a historical
artifact that dates back to when goldsborough originally added these
bindings in the first place. If TensorOptions is made optional,
then you only need one overload, not two, as they are exactly redundant
with each other. When MemoryFormat was added, it was made a little
harder to do this, as the C++ syntax at::empty_like(t, memory_format) would
not work if you collapsed the overload; but now it works because TensorOptions
supports MemoryFormat.
The upshot is, I can get rid of all the overloads and just have one overload.
Amazingly, this change is backwards compatible, as the test attests. While
I was at it, I also deleted the overload name from the functions entirely.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D20073355
Pulled By: bhosmer
fbshipit-source-id: c6a8908213b32ccf6737ea864d135e2cce34f56b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32682
This moves code around so that operator.h/cpp no longer requires a full
definition of Node* nor does it include alias analysis or the pretty printer.
This should make it possible to include in the mobile build.
Functionality for checking if operators match Node and to look up
and operator for a Node have moved to the Node object.
Test Plan: Imported from OSS
Differential Revision: D19615386
Pulled By: zdevito
fbshipit-source-id: e38bdf29971183597ef940d061c06ba56e71d9c5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27561
Adds memory_format keyword argument (positional for cpp).
'Preserve' behavior now follows next rules:
1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor.
2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format.
3) Output tensor is going to be contiguous in all other cases.
---
Dense tensor is the tensor that store values in a contiguous block of memory.
Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory.
Test Plan: Imported from OSS
Differential Revision: D17980316
Pulled By: VitalyFedyunin
fbshipit-source-id: 2a1d47571268673de0c6f5ae1b6d4f9110962ab0
Summary:
This PR excises the last of SymbolicVariable. There should be no change in functionality. One new test for addmm fusion was added. A case where the peephole optimizer might convert a scalar argument remains untested.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25077
Test Plan: Refactors existing code so mostly covered by current tests. One test for addmm fusion was added.
Differential Revision: D17145334
Pulled By: mruberry
fbshipit-source-id: 6b68faf764f9ee8398b55c43110228ed9faf81eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24284
This PR finishes the unification of all Tensor types into a single object.
ProfiledTensorType is renamed to TensorType and the old TensorType is
deleted.
Notes:
* Fixes bug in merge for VaryingShape by changing its representation to an
optional list of optional ints.
* Removes ProfiledTensorType::create(type) invocations that can now
simply be expect calls on tensor type.
Test Plan: Imported from OSS
Differential Revision: D16794034
Pulled By: zdevito
fbshipit-source-id: 10362398d0bb166d0d385d74801e95d9b87d9dfc
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/22833
grad_sum_to_size does not commute with AutogradAdd after all because it turns the broadcasting AutogradAdd into a broadcasting add.
Chillee did actually do most of the tracking down to the fusion of grad_sum_to_size and pinging me when he had found the cause. Thank you!
About the choice of removing the fusion completely instead of being more precise:
- We do have grad_sum_to_size elimination which works for cases where broadcasting does not actually happen in the forward, so the cases where the fusing of grad_sum_to_size is actually beneficial is much smaller than when initially proposed.
- There will be less fusion, in terms of the tests, IOU stops being fully fused. I vaguely think that it is a case we could handle with refined logic.
- Keeping it would add complexity in checking when to merge fusion groups to the complexities that this PR removes.
- The future of fusion probably lies more in more complete solutions including reductions (TVM or KeOps or our own or ...).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23372
Differential Revision: D16489930
Pulled By: soumith
fbshipit-source-id: bc0431b0d3eda264c401b634675872c4ce46f0f4
Summary:
This PR is a eliminates unneeded grad_sum_to_size and in particular speeds up the LSTM backward by allowing better fusion.
It consists of two parts:
- In AutoDiff, record broadcasting sizes only if the broadcast output size is different from the input size, otherwise record None.
- The specialization of Optional arguments (#18407) allows us to then eliminate ` _grad_sum_to_size(t, None)` in the peephole optimization step.
Thus, in the LSTM case, no SumToSize remain in the crucial fusion group. The trick here is that we can specialize on the runtime information from the forward.
I'm testing that different broadcasting situations lead to different graphs.
I didn't move all symbolic_script _grad_sum_to_size to the new logic, but it might be better to do this incrementally, anyway.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18697
Differential Revision: D15482076
Pulled By: wanchaol
fbshipit-source-id: 7f89367e35b8729910077c95c02bccefc8678afb
Summary:
move the insert_guard all the way up to the beginning of the decomposation, this will fix the case that we lose insert_point context after decomposeCommonNormalization and we still need to modify the graph.
fixes#19502
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19646
Differential Revision: D15058040
Pulled By: wanchaol
fbshipit-source-id: ebdbf8623ebfe4556c461e1b650e94b905791adb
Summary:
I believe the existing check in FuseGraph was only `false` if PyTorch was built with NO_CUDA=1. Otherwise, we would create fusion groups even if we're on a CPU-only machine running CPU code. This is confusing. Instead I've made it so that the decision to fuse or not is dependent on if the producer Value is a known CPU tensor. If it is, we skip fusion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19342
Differential Revision: D15038351
Pulled By: jamesr66a
fbshipit-source-id: fce9d83929309a7bf14346833f84b996f3e7f6db
