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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.

tinygrad is also a city in Russia.

Installation

pip3 install git+https://github.com/geohot/tinygrad.git --upgrade

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

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.

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

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 (with broadcasting)
Relu, Log, Exp                  # unary ops
Sum, Max                        # reduce ops (with axis argument)
Dot, Conv2D                     # matrix multiplication and conv
Reshape, Transpose              # moving things around ops
Unpad2D, Pad2D                  # stupid slices

ImageNet inference

Despite being tiny, tinygrad supports the full EfficientNet. Pass in a picture to discover what it is.

ipython3 examples/efficientnet.py https://upload.wikimedia.org/wikipedia/commons/4/41/Chicken.jpg

Or, if you have a webcam and cv2 installed

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

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

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