Summary:
This PR move glu to Aten(CPU).
Test script:
```
import torch
import torch.nn.functional as F
import time
torch.manual_seed(0)
def _time():
if torch.cuda.is_available():
torch.cuda.synchronize()
return time.time()
device = "cpu"
#warm up
for n in [10, 100, 1000, 10000]:
input = torch.randn(128, n, requires_grad=True, device=device)
grad_output = torch.ones(128, n // 2, device=device)
for i in range(1000):
output = F.glu(input)
output.backward(grad_output)
for n in [10, 100, 1000, 10000]:
fwd_t = 0
bwd_t = 0
input = torch.randn(128, n, requires_grad=True, device=device)
grad_output = torch.ones(128, n // 2, device=device)
for i in range(10000):
t1 = _time()
output = F.glu(input)
t2 = _time()
output.backward(grad_output)
t3 = _time()
fwd_t = fwd_t + (t2 -t1)
bwd_t = bwd_t + (t3 - t2)
fwd_avg = fwd_t / 10000 * 1000
bwd_avg = bwd_t / 10000 * 1000
print("input size(128, %d) forward time is %.2f (ms); backwad avg time is %.2f (ms)."
% (n, fwd_avg, bwd_avg))
```
Test device: **skx-8180.**
Before:
```
input size(128, 10) forward time is 0.04 (ms); backwad avg time is 0.08 (ms).
input size(128, 100) forward time is 0.06 (ms); backwad avg time is 0.14 (ms).
input size(128, 1000) forward time is 0.11 (ms); backwad avg time is 0.31 (ms).
input size(128, 10000) forward time is 1.52 (ms); backwad avg time is 2.04 (ms).
```
After:
```
input size(128, 10) forward time is 0.02 (ms); backwad avg time is 0.05 (ms).
input size(128, 100) forward time is 0.04 (ms); backwad avg time is 0.09 (ms).
input size(128, 1000) forward time is 0.07 (ms); backwad avg time is 0.17 (ms).
input size(128, 10000) forward time is 0.13 (ms); backwad avg time is 1.03 (ms).
```
Fix https://github.com/pytorch/pytorch/issues/24707, https://github.com/pytorch/pytorch/issues/24708.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33179
Differential Revision: D19839835
Pulled By: VitalyFedyunin
fbshipit-source-id: e4d3438556a1068da2c4a7e573d6bbf8d2a6e2b9
Summary:
This PR aims to re-organize C++ API `torch::nn` folder structure in the following way:
- Every module in `torch/csrc/api/include/torch/nn/modules/` (except `any.h`, `named_any.h`, `modulelist.h`, `sequential.h`, `embedding.h`) has a strictly equivalent Python file in `torch/nn/modules/`. For example:
`torch/csrc/api/include/torch/nn/modules/pooling.h` -> `torch/nn/modules/pooling.py`
`torch/csrc/api/include/torch/nn/modules/conv.h` -> `torch/nn/modules/conv.py`
`torch/csrc/api/include/torch/nn/modules/batchnorm.h` -> `torch/nn/modules/batchnorm.py`
`torch/csrc/api/include/torch/nn/modules/sparse.h` -> `torch/nn/modules/sparse.py`
- Containers such as `any.h`, `named_any.h`, `modulelist.h`, `sequential.h` are moved into `torch/csrc/api/include/torch/nn/modules/container/`, because their implementations are too long to be combined into one file (like `torch/nn/modules/container.py` in Python API)
- `embedding.h` is not renamed to `sparse.h` yet, because we have another work stream that works on API parity for Embedding and EmbeddingBag, and renaming the file would cause conflict. After the embedding API parity work is done, we will rename `embedding.h` to `sparse.h` to match the Python file name, and move the embedding options out to options/ folder.
- `torch/csrc/api/include/torch/nn/functional/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/functional/pooling.h` contains the functions for pooling, which are then used by the pooling modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`.
- `torch/csrc/api/include/torch/nn/options/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/options/pooling.h` contains MaxPoolOptions, which is used by both MaxPool modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`, and max_pool functions in `torch/csrc/api/include/torch/nn/functional/pooling.h`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26262
Differential Revision: D17422426
Pulled By: yf225
fbshipit-source-id: c413d2a374ba716dac81db31516619bbd879db7f
Summary:
Previously, we were not able to assign names to `nn::Sequential`'s submodules. This PR adds this feature to match the Python API. Example use:
```cpp
Sequential sequential(named_submodule({
{"linear", Linear(10, 3)},
{"conv2d", Conv2d(1, 2, 3)},
{"dropout", Dropout(0.5)},
{"batchnorm", BatchNorm(5)},
{"embedding", Embedding(4, 10)},
{"lstm", LSTM(4, 5)}
}));
```
It also enables loading parameters of Python `nn.Sequential` module with custom submodules names into C++ frontend, unblocking https://github.com/pytorch/vision/pull/728#issuecomment-466661344.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17552
Differential Revision: D14246834
Pulled By: yf225
fbshipit-source-id: 3030b5c5d68f6dd5d3e37ac4b4f98dc6d6d9ba72
