Lesser forklet to learn tinygrad.
https://spacecruft.org/deepcrayon/tinygrab
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.github/workflows | ||
docs | ||
examples | ||
test | ||
tinygrad | ||
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LICENSE | ||
push_pypi.sh | ||
README.md | ||
requirements.txt | ||
setup.py |
For something in between a pytorch and a 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.
Installation
pip3 install tinygrad
Example
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
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)
from tinygrad.tensor import Tensor
import tinygrad.optim as optim
from tinygrad.utils import layer_init_uniform
class TinyBobNet:
def __init__(self):
self.l1 = Tensor(layer_init_uniform(784, 128))
self.l2 = Tensor(layer_init_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()
loss.backward()
optim.step()
GPU Support?!
tinygrad supports GPUs through PyOpenCL. Not all ops are supported yet.
from tinygrad.tensor import Tensor
(Tensor.ones(4,4).cuda() + Tensor.ones(4,4).cuda()).cpu()
ImageNet inference
Despite being tiny, tinygrad supports the full EfficientNet. Pass in a picture to discover what it is.
python3 examples/efficientnet.py https://upload.wikimedia.org/wikipedia/commons/4/41/Chicken.jpg
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
python -m pytest
TODO
- Train an EfficientNet on ImageNet
- Make broadcasting work on the backward pass (simple please)
- EfficientNet backward pass
- Tensors on GPU (GPU support, must support Mac)
- Reduce code
- Increase speed
- Add features