Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: malfet
Differential Revision: D31705366
fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66234
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
bypass_size_limit
allow-large-files
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D30652629
fbshipit-source-id: 0ae6c4bbbb554bad42e372792a6430e1acf15e3e
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:
when enabling this flag, there were a lot of warnings, this pr focuses on the warnings where this comparison could be affecting array indices, which could be ones most prone to fail
the good news is that I didn't find anything obviously concerning
one degenerate case could be when the matrices we work with are too skinny could run into issues (dim1=1, dim2 needs to hold a big number)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18187
Differential Revision: D14527182
Pulled By: hyuen
fbshipit-source-id: b9f46b6f68ab912c55368961758a7a5af1805555
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18123
the motivation of this fix is to resolve things like:
for(auto i = 0; i < N; i++) where N is bigger than int32
These instances of comparison were found by enabling -Wsign-compare
There are way too many things to fix, so issuing this as a series of fixes
The plan is to fix all these issues and then enable this flag into Caffe2 to catch future instances
Reviewed By: ZolotukhinM
Differential Revision: D14497094
fbshipit-source-id: bca3927a2188bd33a508fa503ba221c220cdaefe
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16273
Previously we have SetOutputSize which accept a partially initialized Output Tensor and set it to the correct size,
the diff change this to GetOutputSize that returns the correct size instead.
e.g.
```
auto* Y = Output(0);
ConvPoolOp<Context>::SetOutputSize(X, Y, channels);
...
Y->mutable_data<T>...
```
-->
```
auto sizes = ConvPoolOp<Context>::GetOutputSize(X, channels);
auto* Y = Output(0, sizes, at::dtype<T>());
```
Reviewed By: dzhulgakov
Differential Revision: D13736281
fbshipit-source-id: 64abce3dbaed0b375098463333dfd0ea5a3b1945
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15947
Codemod generated with clangr shard mode, 25 files per diff,
To eliminiate partially initialized Tensor, we split the initialization of local Tensor variables into two steps, first declare un uninitialized Tensor, and
call `ReinitializeTensor` to initialize it.
motivation: https://github.com/pytorch/pytorch/pull/12407
Reviewed By: smessmer
Differential Revision: D13586732
fbshipit-source-id: 5295ab27ca0155f96a4fccf9c0ba8a609101ba24
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15625
3D group conv (both NCHW and NHWC layout) was not correct.
Added group=2 in test_1d_convolution and test_3d_convolution in conv_test
Reviewed By: protonu
Differential Revision: D13562099
fbshipit-source-id: 586e8a7574a2764f2a3b559db6c2415b3ab90453
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12733
Conv in NHWC layout only works for 2D images. This has been a pain point when implementing quantized 3D convolution because we need NHWC layout for best performance (note that NHWC layout in general gives better performance in CPU not just for quantized operators). For example, our quantized ops have a functionality to measure quantized error operator by operator but this needs running a shadow fp32 operator, but this is not easy when there's no 3D conv in NHWC layout is available (currently we're doing layout conversion on the fly for the shadow fp32 operator which is error prone). Some of Caffe2 frameworks like brew generates error when we try to create a 3D conv op in NHWC layout. This was also a blocker for using aibench because aibench is using brew.
i-am-not-moving-c2-to-c10
Reviewed By: houseroad
Differential Revision: D10333829
fbshipit-source-id: 2d203ee1db833cd3f9d39353219e3894b46c4389
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12714
This is a short change to enable c10 namespace in caffe2. We did not enable
it before due to gflags global variable confusion, but it should have been
mostly cleaned now. Right now, the plan on record is that namespace caffe2 and
namespace aten will fully be supersets of namespace c10.
Most of the diff is codemod, and only two places of non-codemod is in caffe2/core/common.h, where
```
using namespace c10;
```
is added, and in Flags.h, where instead of creating aliasing variables in c10 namespace, we directly put it in the global namespace to match gflags (and same behavior if gflags is not being built with).
Reviewed By: dzhulgakov
Differential Revision: D10390486
fbshipit-source-id: 5e2df730e28e29a052f513bddc558d9f78a23b9b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10439
Update Im2Col related to make preparation for group conv in NHWC order.
Reviewed By: houseroad
Differential Revision: D9285344
fbshipit-source-id: 1377b0243acb880d2ad9cf73084529a787dcb97d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10244
Use CAFFE_ENFORCE_EQ(x, y) instead of CAFFE_ENFORCE(x == y) in conv_op_impl.h for error messages with more information.
Reviewed By: viswanathgs
Differential Revision: D9177091
fbshipit-source-id: cf8d10afec1ce6793d3ae0b62f05648722a4130b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9939
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: ezyang, houseroad
Differential Revision: D9024330
fbshipit-source-id: e0b8295d2dc6ebe2963383ded5af799ad17164ba
Summary:
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: xw285cornell
Differential Revision: D8121878
fbshipit-source-id: 4a5e9a677ba4ac82095df959851a054c81eccf81
Summary:
Shared im2col buffer needs a mutex only to protect it from ops within a
workspace (since the shared buffer is created per workspace). The current
implementation has a global mutex which affects perf when running multiple nets
in parallel.
I don't feel great about adding a mutex for this in workspace, let me know if
anyone has better suggestions.
Reviewed By: akyrola
Differential Revision: D5341476
fbshipit-source-id: 1c9a92ef488ffb0c0013a7656bcb3d530bc7208b
Summary:
MSVC 2015 has known bugs about template functions so these changes aim to fix them - no functional differences introduced.
Closes https://github.com/caffe2/caffe2/pull/179
Reviewed By: ajtulloch
Differential Revision: D4635241
Pulled By: Yangqing
fbshipit-source-id: a282a96e1e626e9440c1e3f3cb15b5b1fa710887
Summary:
It used to be that only the cudnn engine supports it, and now it should be
fully supported by any conv engine.
To ignore bias, simply use a convolution op that has two inputs instead of
3. The gradient operator will automatically figure out that it does not
compute the bias gradient.
Reviewed By: prigoyal
Differential Revision: D4354183
fbshipit-source-id: cf71b6289a254d15a6a663a85df63fbbaec3702b
Summary:
This is an ongoing work - currently the forward pass is implemented, but backward
is yet to be done. We might want a CPU counterpart as well.
I will wait for D4341288 to land and then make bias optional.
Reviewed By: prigoyal
Differential Revision: D4342210
fbshipit-source-id: 51bb0e98d917970bdc040d076b535beb8e994d9a
Summary:
Yangqing This seems to work for me, not sure if it's implemented in the right way for you to accept :)
Allows user to specify "no_bias" as an option for convolution layers (only cuDNN at this point), so that the bias associated with that operator is not allocated or computed. This is useful in particular for conv + BatchNorm combinations (such as ResNets), as the bias term can be handled by both conv and Batch Norm, wasting memory and computation.
Closes https://github.com/caffe2/caffe2/pull/50
Reviewed By: Yangqing
Differential Revision: D4341288
Pulled By: bwasti
fbshipit-source-id: e6138d0024c83ed876dff2f83ffbebe7de502fd8
(1) cudnn for conv
(2) cublas: after going through the work I feel it's beter to use HOST pointer mode, so changed it.
(3) storage order: despite that googlenet and multibox uses NHWC, it seems better to be still using
NCHW as default to be consistent with caffe and cudnn; moved to NCHW as default.
