The symbol seems to conflict under some compiler versions, giving
an error like "relocation refers to global symbol which is defined in a
discarded section". Simple enough to put it in an anonymous namespace,
so why not.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86092
Approved by: https://github.com/Chillee
### Introduction
<!-- What did you change and why was it needed? -->
Removing unnecessary weight gradient calculation is very important for applications that need high-order derivatives during training. However, this is not supported by the current Autograd engine.
For more detail: The backward function of a `matmul` operator (e.g., `linear` `addmm` `mm`), has two matmuls, one for `input gradient` and another for `weight gradient`. For a typical neural network (nn) with a few linear layers and activation functions, if the user calls `torch.autograd.grad()` to calculate the derivative of the nn output `y` w.r.t the nn input `x`, only the `input gradient` of the `matmul` operator is needed, and the `weight gradient` is discarded. However, the current PyTorch autograd engine will always calculate the `weight gradient` if `weight` requires gradient (the calculation of the high-order derivative is performed during training).
The figure attached shows the autograd graph of the following code snippet:
```py
y = torch.nn.functional.linear(x, weight, bias)
y = y.pow(2)
# first order derivative
y__x, = torch.autograd.grad(y, x, grad_outputs=grad_outputs, create_graph=True)
# first order derivative
y__x__x, = torch.autograd.grad(y__x, x, grad_outputs=grad_outputs, create_graph=True)
```
The path with ❌ is not needed when calculating derivatives.
<img width="50%" alt="image" src="https://user-images.githubusercontent.com/9999318/182018117-719c5a23-bcc6-4a63-8e8d-1bca3ebda2e3.png">
### Issue
<!-- Link to Issue ticket or RFP -->
Related issue: https://github.com/pytorch/pytorch/issues/56500
### Method
When calling `torch.autograd.grad`, `exec_info_` is created for each GraphTask, which allows filtering paths on the graph that are not needed. However, when the GraphTask calls into the node, the node still does not know whether the edges are needed or not. In the case of matmul, `weight.requires_grad is True` so the weight gradient is always calculated.
Following https://github.com/pytorch/pytorch/issues/56500#issuecomment-825694656, this PR passes the graph task's thread_local `exec_info_` into the node, so it could trim unnecessary edges during `torch.autograd.grad` calls.
### Benchmark
Benchmark script: https://gist.github.com/yueyericardo/24158433a2021c51eeef9c3e2722df99
Benchmark result:
6 hidden layers, batch size 10000, on A100
FP32 result
| hessian benchmark | FP32 (before) | FP32 (After) | FP32 (Functorch v0.1.1) |
| ----------------------------- | ------------- | ----------------- | ----------------------- |
| Linear + ReLU (no backward) | 55.658 ms | 29.392 ms (1.90X) | 29.547 ms (1.90X) |
| Linear + ReLU (with backward) | 81.173 ms | 54.917 ms (1.47X) | 68.988 ms (1.18X) |
TF32 result
| hessian benchmark | TF32 (before) | TF32 (after) | TF32 (Functorch v0.1.1) |
| ----------------------------- | ------------- | ----------------- | ----------------------- |
| Linear + ReLU (no backward) | 19.801 ms | 11.259 ms (1.76X) | 10.754 ms (1.84X) |
| Linear + ReLU (with backward) | 29.167 ms | 20.466 ms (1.42X) | 22.784 ms (1.28X) |
For FP32 result, we could get 1.9X speed up for hessian calculation, and 1.47X speed up during training, which is even faster than functorch `vmap(jacfwd(jacrev` implementation. (functorch has performance regression on v0.2.0, https://github.com/pytorch/functorch/issues/989, so we are using v0.1.1 for benchmark)
@zou3519 does functorch also includes similar optimizations during hessian calculation? If not, what do we need to do so the functorch could also benefit from this PR?
### Testing
<!-- How did you test your change? -->
- [x] we need to figure out a way for unittest
### Thanks
Thanks for the great blog: [How Computational Graphs are Executed in PyTorch | PyTorch](https://pytorch.org/blog/how-computational-graphs-are-executed-in-pytorch/)
cc @zasdfgbnm @albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82544
Approved by: https://github.com/soulitzer
And use it throughout the CMakeLists and rectify `IF(APPLE)`/`IF(GNU_CXX_VERSION VERSION_GREATER A.B)` and so on
Also, add `target_compile_options_if_supported` and use it in `Dependencies.cmake` as well as in test's `CMakeListst.txt`
Delete `-Wno-unknown-warning-option` to test that conditions indeed working as expected
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82883
Approved by: https://github.com/seemethere
And use it throughout the CMakeLists and rectify `IF(APPLE)`/`IF(GNU_CXX_VERSION VERSION_GREATER A.B)` and so on
Also, add `target_compile_options_if_supported` and use it in `Dependencies.cmake` as well as in test's `CMakeListst.txt`
Delete `-Wno-unknown-warning-option` to test that conditions indeed working as expected
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82883
Approved by: https://github.com/seemethere
See this doc: https://docs.google.com/document/d/1KiRdnoj6B4cI3yl017hTbCqcOGO1gWIpUf20sldipHM/edit#
Two issues (1) regarding hooks in general and (2) regarding retains grad hooks are fixed, Python hooks, which rely on a different mechanism are not discussed here:
- Hooks in cpp in general
- (fixed) new hooks to registered to a newer version of the tensor no longer get applied to grad_fn
associated with older version of the tensor when the first hook was ever registered
- (unchanged) hooks registered to the older version of the tensor remain active on
- Retains grad hooks
- (fixed) now get moved to the latest grad_fn. NB: To the user, retains_grad is not considered hooks
or expected to behave like hooks (which we consider properties of the grad_fn) vs retains_gradness
which is a property of the tensor.
- (not in this PR) Python hooks
- (will fix) same issue as hooks in cpp where new hooks are being applied to grad_fn associated
with the older version of the tensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79996
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75244
Original commit changeset: d653a5af662a
Original Phabricator Diff: D35060736 (d9d34922a0)
Test Plan: Model loading test, verified that D35060736 (d9d34922a0) will cause the torch::save => torch::load failure.
Reviewed By: yinghai, jianyuh
Differential Revision: D35387009
fbshipit-source-id: 9d176992d402d57779e2af3d905b3c1538335298
(cherry picked from commit 6c8cc0d3b8a88b15e35702d70e18bbae8aa4628a)
Summary:
There are various possible approaches, but the approach chosen minimizes disruption to source control blame.
Addresses:
```
error: Function _ZN23FunctionalTest_Pad_Test8TestBodyEv is too big to optimize [-Werror,-Wignored-optimization-argument]
```
Test Plan: buck2 build mode/opt caffe2/test/cpp/api:functional
Reviewed By: jamesr66a
Differential Revision: D34027291
fbshipit-source-id: 9dfd771ad56d3d4bc0d41b38b04654c8dae7c006
(cherry picked from commit d43b5a7ed6224192fff8ad1215a572b333ec5f97)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69394
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: D32837991
fbshipit-source-id: fc7c4f76d2f32a17a0faf329294b3fe7cb81df32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66743
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: D31705359
fbshipit-source-id: c9ea2fbc0f9cd29e97a52dcb203addc5f2abb09b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67195
Now that `is_nonzero` is part of `at::native` refer https://github.com/pytorch/pytorch/pull/66663, replacing `TensorCompare::is_nonzero` to `at::native::is_nonzero`
ghstack-source-id: 141514416
Test Plan: CI
Reviewed By: larryliu0820
Differential Revision: D31704041
fbshipit-source-id: 36813e5411d0aa2eb2d0442e2a195bbed417b33d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66744
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: ngimel
Differential Revision: D31705358
fbshipit-source-id: d6ea350cbaa8f452fc78f238160e5374be637a48
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
