tinygrad/docs/adding_new_accelerators.md

Adding a new accelerator to tinygrad

It's pretty easy to add a new accelerator to tinygrad. All you need to do is implement a total of 26 (optionally 27) low level ops. Then tinygrad takes care of the rest, handling derivatives and syntactic sugar.

llops

These are the ops that you must implement for your accelerator of choice. Compiled Accelerators do not need to implement movement_ops, as they are handled by the ShapeTracker.

Buffer                                                       # class of memory on this device
unary_op  (NOOP, EXP2, LOG2, CAST, SIN, SQRT)                # A -> A
reduce_op (SUM, MAX)                                         # A -> B (smaller size, B has 1 in shape)
binary_op (ADD, SUB, MUL, DIV, CMPEQ, MAX)                   # A + A -> A (all the same size)
movement_op (EXPAND, RESHAPE, PERMUTE, PAD, SHRINK, STRIDE)  # A -> B (different size)
load_op   (EMPTY, RAND, CONST, FROM, CONTIGUOUS, CUSTOM)     # -> A   (initialize data on device)
fused_op [[optional]] (MULACC)                               # A * A -> B

mlops

These are the mid level ops that handle the derivatives.

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

These are implemented in mlops.py.

hlops

These are the syntax sugar. They are built on top of the mlops and support most of the things that you could expect from a tensor library.

These are implemented in tensor.py.