mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
164 lines
4.1 KiB
Markdown
164 lines
4.1 KiB
Markdown
<p align="center">
|
|
<img src="https://raw.githubusercontent.com/geohot/tinygrad/master/docs/logo.png">
|
|
</p>
|
|
|
|
--------------------------------------------------------------------
|
|
|
|
|
|
|
|
For something in between a [pytorch](https://github.com/pytorch/pytorch) and a [karpathy/micrograd](https://github.com/karpathy/micrograd)
|
|
|
|
This may not be the best deep learning framework, but it is a deep learning framework.
|
|
|
|
The Tensor class is a wrapper around a numpy array, except it does Tensor things.
|
|
|
|
tinygrad is also a city in Russia.
|
|
|
|
### Installation
|
|
|
|
```bash
|
|
pip3 install git+https://github.com/geohot/tinygrad.git --upgrade
|
|
```
|
|
|
|
### Example
|
|
|
|
```python
|
|
from tinygrad.tensor import Tensor
|
|
|
|
x = Tensor.eye(3)
|
|
y = Tensor([[2.0,0,-2.0]])
|
|
z = y.matmul(x).sum()
|
|
z.backward()
|
|
|
|
print(x.grad) # dz/dx
|
|
print(y.grad) # dz/dy
|
|
```
|
|
|
|
### Same example in torch
|
|
|
|
```python
|
|
import torch
|
|
|
|
x = torch.eye(3, requires_grad=True)
|
|
y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)
|
|
z = y.matmul(x).sum()
|
|
z.backward()
|
|
|
|
print(x.grad) # dz/dx
|
|
print(y.grad) # dz/dy
|
|
```
|
|
|
|
## Neural networks?
|
|
|
|
It turns out, a decent autograd tensor library is 90% of what you need for neural networks. Add an optimizer (SGD, RMSprop, and Adam implemented) from tinygrad.optim, write some boilerplate minibatching code, and you have all you need.
|
|
|
|
### Neural network example (from test/test_mnist.py)
|
|
|
|
```python
|
|
from tinygrad.tensor import Tensor
|
|
import tinygrad.optim as optim
|
|
|
|
class TinyBobNet:
|
|
def __init__(self):
|
|
self.l1 = Tensor.uniform(784, 128)
|
|
self.l2 = Tensor.uniform(128, 10)
|
|
|
|
def forward(self, x):
|
|
return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
|
|
|
|
model = TinyBobNet()
|
|
optim = optim.SGD([model.l1, model.l2], lr=0.001)
|
|
|
|
# ... and complete like pytorch, with (x,y) data
|
|
|
|
out = model.forward(x)
|
|
loss = out.mul(y).mean()
|
|
optim.zero_grad()
|
|
loss.backward()
|
|
optim.step()
|
|
```
|
|
|
|
## GPU and Accelerator Support
|
|
|
|
tinygrad supports GPUs through PyOpenCL.
|
|
|
|
```python
|
|
from tinygrad.tensor import Tensor
|
|
(Tensor.ones(4,4).gpu() + Tensor.ones(4,4).gpu()).cpu()
|
|
```
|
|
|
|
### ANE Support?!
|
|
|
|
If all you want to do is ReLU, you are in luck! You can do very fast ReLU (at least 30 MEGAReLUs/sec confirmed)
|
|
|
|
Requires your Python to be signed with `ane/lib/sign_python.sh` to add the `com.apple.ane.iokit-user-access` entitlement, which also requires `amfi_get_out_of_my_way=0x1` in your `boot-args`. Build the library with `ane/lib/build.sh`
|
|
|
|
```python
|
|
from tinygrad.tensor import Tensor
|
|
|
|
a = Tensor([-2,-1,0,1,2]).ane()
|
|
b = a.relu()
|
|
print(b.cpu())
|
|
```
|
|
|
|
Warning: do not rely on the ANE port. It segfaults sometimes. So if you were doing something important with tinygrad and wanted to use the ANE, you might have a bad time.
|
|
|
|
### Adding an accelerator
|
|
|
|
You need to support 15 basic ops:
|
|
|
|
```
|
|
Add, Sub, Mul, Pow # binary ops
|
|
Relu, Log, Exp # unary ops
|
|
Sum, Max # reduce ops
|
|
Dot # matrix multiplication
|
|
Conv2D, MaxPool2D # 2D ops
|
|
Pad2D, Reshape, Transpose # moving things around ops
|
|
```
|
|
|
|
## ImageNet inference
|
|
|
|
Despite being tiny, tinygrad supports the full EfficientNet. Pass in a picture to discover what it is.
|
|
|
|
```bash
|
|
ipython3 examples/efficientnet.py https://upload.wikimedia.org/wikipedia/commons/4/41/Chicken.jpg
|
|
```
|
|
|
|
Or, if you have a webcam and cv2 installed
|
|
|
|
```bash
|
|
ipython3 examples/efficientnet.py webcam
|
|
```
|
|
|
|
PROTIP: Set "GPU=1" environment variable if you want this to go faster.
|
|
|
|
PROPROTIP: Set "DEBUG=1" environment variable if you want to see why it's slow.
|
|
|
|
### tinygrad also supports GANs
|
|
|
|
See `examples/mnist_gan.py`
|
|
|
|
<p align="center">
|
|
<img src="https://raw.githubusercontent.com/geohot/tinygrad/master/docs/mnist_by_tinygrad.jpg">
|
|
</p>
|
|
|
|
## The promise of small
|
|
|
|
tinygrad will always be below 1000 lines. If it isn't, we will revert commits until tinygrad becomes smaller.
|
|
|
|
### Running tests
|
|
|
|
```bash
|
|
python3 -m pytest
|
|
```
|
|
|
|
### TODO
|
|
|
|
* Train an EfficientNet on ImageNet
|
|
* Add a language model. BERT?
|
|
* Add a detection model. EfficientDet?
|
|
* Reduce code
|
|
* Increase speed
|
|
* Add features
|
|
|