From 803587b8b42697fcc2f5ad920ea76d9cd716f666 Mon Sep 17 00:00:00 2001 From: George Hotz Date: Fri, 26 May 2023 06:10:41 +0000 Subject: [PATCH] update readme --- README.md | 46 +++++++++++++--------------------------------- 1 file changed, 13 insertions(+), 33 deletions(-) diff --git a/README.md b/README.md index 650025dec4..00a76b4739 100644 --- a/README.md +++ b/README.md @@ -121,24 +121,6 @@ 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. - -```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. - ### hlops (in tensor.py) hlops are syntactic sugar around mlops. They support most things torch does. @@ -148,7 +130,7 @@ hlops are syntactic sugar around mlops. They support most things torch does. mlops are mid level ops. They understand derivatives. They are very simple. ``` -Relu, Log, Exp # unary ops +Relu, Log, Exp, Sin # unary ops Sum, Max # reduce ops (with axis argument) Maximum, Add, Sub, Mul, Pow, Div, Equal # binary ops (no broadcasting, use expand) Expand, Reshape, Permute, Pad, Shrink, Flip # movement ops @@ -162,7 +144,7 @@ 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 (NOOP, EXP, LOG, CAST) # A -> A +unary_op (NOOP, EXP, LOG, CAST, SIN) # A -> A reduce_op (SUM, MAX) # A -> B (smaller size, B has 1 in shape) binary_op (ADD, SUB, MUL, DIV, POW, CMPEQ, MAX) # A + A -> A (all the same size) movement_op (EXPAND, RESHAPE, PERMUTE, PAD, SHRINK, STRIDE) # A -> B (different size) @@ -174,16 +156,16 @@ fused_op [[optional]] (MULACC) # A * A -> B Despite being tiny, tinygrad supports the full EfficientNet. Pass in a picture to discover what it is. ```bash -ipython3 examples/efficientnet.py https://media.istockphoto.com/photos/hen-picture-id831791190 +python3 examples/efficientnet.py https://media.istockphoto.com/photos/hen-picture-id831791190 ``` Or, if you have a webcam and cv2 installed ```bash -ipython3 examples/efficientnet.py webcam +python3 examples/efficientnet.py webcam ``` -PROTIP: Set "DEBUG=1" environment variable if you want to see why it's slow. +PROTIP: Set "DEBUG=2" environment variable if you want to see why it's slow. ### tinygrad supports Stable Diffusion! @@ -199,6 +181,14 @@ Run `python3 examples/stable_diffusion.py` "a horse sized cat eating a bagel"

+### tinygrad supports LLaMA + +After putting the weights in weights/LLaMA, you can have a chat with Stacy. She lives inside tinygrad. + +```bash +python3 examples/llama.py +``` + ### tinygrad supports GANs See `examples/mnist_gan.py` @@ -215,18 +205,8 @@ 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 - ```bash GRAPH=1 python3 test/models/test_mnist.py TestMNIST.test_sgd_onestep # requires dot, outputs /tmp/net.svg