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 sub 1000 line core of it is in tinygrad/
Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. Support the simple basic ops, and you get SOTA vision models/efficientnet.py and language models/transformer.py models.
We are working on support for the Apple Neural Engine and the Google TPU in the accel/ folder. Eventually, we will build custom hardware for tinygrad, and it will be blindingly fast. Now, it is slow.
Installation
git clone https://github.com/geohot/tinygrad.git
cd tinygrad
python3 setup.py developContributing
There's a lot of interest in tinygrad lately. Here's some guidelines for contributing:
- Bugfixes are the best and always welcome! Like this one.
- If you don't understand the code you are changing, don't change it!
- All code golf PRs will be closed, but conceptual cleanups are great.
- Features are welcome. Though if you are adding a feature, you need to include tests.
- Improving test coverage is great, with reliable non brittle tests.
Example
from tinygrad.tensor import Tensor
x = Tensor.eye(3, requires_grad=True)
y = Tensor([[2.0,0,-2.0]], requires_grad=True)
z = y.matmul(x).sum()
z.backward()
print(x.grad.numpy()) # dz/dx
print(y.grad.numpy()) # dz/dySame 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/dyNeural 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.nn.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.nn.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?! (broken)
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 sudo nvram boot-args="amfi_get_out_of_my_way=1 ipc_control_port_options=0". Build the library with ane/lib/build.sh
In order to set boot-args and for the AMFI kext to respect that arg, run csrutil enable --without-kext --without-nvram in recovery mode.
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.
hlops (in tensor.py)
hlops are syntactic sugar around mlops. They support most things torch does.
mlops
mlops are mid level ops, there's 15 of them. They understand memory allocation and derivatives
Relu, Log, Exp # unary ops
Sum, Max # reduce ops (with axis argument)
Add, Sub, Mul, Pow # binary ops (no broadcasting, use expand)
Reshape, Permute, Slice, Expand, Flip # movement ops
Conv2D(NCHW) # processing op (Matmul is also Conv2D)
You no longer need to write mlops for a new accelerator
Adding an accelerator (llops)
The autodiff stuff is all in mlops now so you can focus on the raw operations
Buffer # class of memory on this device
unary_op (RELU, EXP, LOG, NEG, SIGN) # A -> A
reduce_op (SUM, MAX) # A -> B (smaller size, B has 1 in shape)
binary_op (ADD, SUB, MUL, DIV, POW, CMPEQ) # A + B -> C (all the same size)
movement_op (RESHAPE, PERMUTE, PAD, SHRINK, EXPAND, FLIP) # A -> B (different size)
processing_op (CONV) # A + B -> C
When tinygrad moves to lazy evaluation, optimizations will happen here.
ImageNet inference
Despite being tiny, tinygrad supports the full EfficientNet. Pass in a picture to discover what it is.
ipython3 examples/efficientnet.py https://media.istockphoto.com/photos/hen-picture-id831791190Or, if you have a webcam and cv2 installed
ipython3 examples/efficientnet.py webcamPROTIP: 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 supports Stable Diffusion!
You might need to download the weight of Stable Diffusion and put it into weights/
Run GPU=1 python3 examples/stable_diffusion.py
"a horse sized cat eating a bagel"
tinygrad supports GANs
See examples/mnist_gan.py
tinygrad supports yolo
See examples/yolov3.py
The promise of small
tinygrad will always be below 1000 lines. If it isn't, we will revert commits until tinygrad becomes smaller.
Drawing Execution Graph
- Nodes are Tensors
- Black edge is a forward pass
- Blue edge is a backward pass
- Red edge is data the backward pass depends on
- Purple edge is intermediates created in the forward
GRAPH=1 python3 test/test_mnist.py TestMNIST.test_sgd_onestep
# requires dot, outputs /tmp/net.svgRunning tests
python3 -m pytest