forked from tinygrad/tinygrad
49 lines
3.4 KiB
Python
49 lines
3.4 KiB
Python
import numpy as np
|
|
from typing import Callable, Dict, Tuple
|
|
from tinygrad.helpers import dtypes, DType
|
|
from tinygrad.ops import BufferOps, UnaryOps, BinaryOps, MovementOps, ReduceOps, TernaryOps, Op
|
|
from tinygrad.device import Interpreted, Allocator
|
|
|
|
def shape_to_axis(old_shape:Tuple[int, ...], new_shape:Tuple[int, ...]) -> Tuple[int, ...]:
|
|
assert len(old_shape) == len(new_shape), "reduce shapes must have same dimensions"
|
|
return tuple(i for i,(a,b) in enumerate(zip(old_shape, new_shape)) if a != b)
|
|
|
|
# TODO: this should be global infrastructure
|
|
def output_type(x, y): return x.dtype if dtypes.from_np(x.dtype).priority > dtypes.from_np(y.dtype).priority else y.dtype
|
|
def match_types(x, y):
|
|
up = output_type(x, y)
|
|
return x.astype(up, copy=False), y.astype(up, copy=False)
|
|
|
|
def einsum_mulacc(einsum, get_strides, expand):
|
|
def einscripts(x): return ''.join(["abcdefghijklmnopqrstuvwxyz"[i] for i in x])
|
|
def axes_slice(strides): return [i for i,s in enumerate(strides) if s != 0], tuple([slice(None) if s != 0 else 0 for i,s in enumerate(strides)])
|
|
def mulacc(a, b, new_shape):
|
|
(a_axes, a_slices), (b_axes, b_slices) = axes_slice(get_strides(a)), axes_slice(get_strides(b))
|
|
out = [i for i in range(len(new_shape)) if a.shape[i] == new_shape[i] and (i in a_axes or i in b_axes)]
|
|
ret = einsum(f"{einscripts(a_axes)}, {einscripts(b_axes)} -> {einscripts(out)}", a[a_slices], b[b_slices])
|
|
return expand(ret.reshape([(1 if i not in a_axes and i not in b_axes else s) for i,s in enumerate(new_shape)]), new_shape)
|
|
return mulacc
|
|
|
|
numpy_fxn_for_op: Dict[Op, Callable] = {
|
|
BufferOps.CONST: lambda val, dtype: np.array(val, dtype=dtype.np),
|
|
UnaryOps.EXP2: np.exp2, UnaryOps.LOG2: np.log2, UnaryOps.SIN: np.sin,
|
|
UnaryOps.CAST: lambda x,y: x.view(y[0].np) if y[1] else x.astype(y[0].np, copy=False), UnaryOps.NEG: lambda x: np.logical_not(x) if x.dtype == np.bool_ else np.negative(x),
|
|
BinaryOps.MAX: np.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).astype(output_type(x,y)), BinaryOps.ADD: lambda x, y: np.add(*match_types(x, y)),
|
|
BinaryOps.SUB: lambda x, y: np.subtract(*match_types(x, y)), BinaryOps.MUL: lambda x, y: np.multiply(*match_types(x, y)),
|
|
BinaryOps.DIV: lambda x, y: np.divide(*match_types(x, y)).astype(output_type(x, y), copy=False), UnaryOps.SQRT: np.sqrt,
|
|
ReduceOps.SUM: lambda x, new_shape: x.sum(shape_to_axis(x.shape, new_shape), dtype=x.dtype, keepdims=True) if x.shape != new_shape else x,
|
|
ReduceOps.MAX: lambda x, new_shape: x.max(shape_to_axis(x.shape, new_shape), keepdims=True) if x.shape != new_shape else x,
|
|
MovementOps.AS_STRIDED: lambda x, arg: np.ndarray(arg[0], buffer=np.require(x, requirements='C'), dtype=x.dtype, offset=arg[2]*x.dtype.itemsize, strides=tuple(y*x.dtype.itemsize for y in arg[1])),
|
|
MovementOps.PAD: np.pad, MovementOps.EXPAND: np.broadcast_to,
|
|
TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: np.einsum(s, *match_types(a.copy(), b.copy()), optimize=True), lambda x: x.strides, np.broadcast_to),
|
|
TernaryOps.WHERE: np.where,
|
|
}
|
|
|
|
class NumpyAllocator(Allocator):
|
|
def _alloc(self, size:int, dtype:DType): return np.empty(size, dtype.np)
|
|
def as_buffer(self, src:np.ndarray) -> memoryview: return np.require(src, requirements='C').data
|
|
def copyin(self, dest:np.ndarray, src:memoryview): np.copyto(dest, np.frombuffer(src, dest.dtype).reshape(dest.shape))
|
|
def copyout(self, dest:memoryview, src:np.ndarray): np.copyto(np.frombuffer(dest, src.dtype).reshape(src.shape), src)
|
|
|
|
CPUDevice = Interpreted(NumpyAllocator(), numpy_fxn_for_op)
|