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from .base import *
import einx.tracer as tracer
from einx.tracer.tensor import op
import einx, types
from functools import partial
import functools
def create():
tTensor = tracer.import_("Tensor", from_="tinygrad")
tdtypes = tracer.import_("dtypes", from_="tinygrad")
from tinygrad import Tensor, dtypes
def scalar_to_tensor(x):
if isinstance(x, (einx.tracer.Scalar, float, int)):
return einx.tracer.apply(
tTensor,
args=[x],
output=einx.tracer.Tensor([]),
)
else:
return x
def elementwise(func, convert_all_to_tensor=False):
@einx.trace
@functools.wraps(func)
def outer(*args):
if convert_all_to_tensor:
args = [scalar_to_tensor(a) for a in args]
else:
args = [a for a in args]
args[0] = scalar_to_tensor(args[0])
return op.elementwise(func)(*args)
return outer
def reduce(func):
@einx.trace
@functools.wraps(func)
def reduce(tensor, axis=None, **kwargs):
keepdims = kwargs.get("keepdims", False)
if axis is None:
shape = ()
else:
axes = [axis] if isinstance(axis, int) else axis
shape = list(tensor.shape)
if keepdims:
for a in axes:
shape[a] = 1
else:
for a in sorted(axes, reverse=True):
del shape[a]
kwargs = {**kwargs, **{"axis": axis}}
if "keepdims" in kwargs:
kwargs["keepdim"] = kwargs.pop("keepdims")
return tracer.apply(func, args=[tensor], kwargs=kwargs, output=tracer.Tensor(shape))
return reduce
def to_dtype(x):
if isinstance(x, str):
return getattr(dtypes, x)
else:
return x
to_dtype2 = to_dtype
class tinygrad(Backend):
name = "tinygrad"
tensor_types = [Tensor]
to_dtype = staticmethod(to_dtype2)
@staticmethod
@einx.trace
def to_tensor(tensor, shape):
return einx.tracer.apply(
tTensor,
args=[tensor],
output=einx.tracer.Tensor(shape),
)
reshape = op.reshape(tTensor.reshape)
transpose = op.transpose(tTensor.permute)
broadcast_to = op.broadcast_to(tTensor.expand)
@classmethod
@einx.trace
def einsum(backend, equation, *tensors):
x = equation.split("->")
if len(x) != 2:
raise ValueError("Invalid equation")
inputs, output = x
inputs = inputs.split(",")
if len(inputs) != len(tensors):
raise ValueError("Invalid equation")
inputs = [x.strip().replace(" ", "") for x in inputs]
tensors = [t for t in tensors]
scalars = []
for i in list(range(len(inputs)))[::-1]:
if (len(inputs[i]) > 0) != (len(tensors[i].shape) > 0):
raise ValueError("Invalid equation")
if len(inputs[i]) == 0:
scalars.append(tensors[i])
inputs.pop(i)
tensors.pop(i)
if len(tensors) > 1:
equation = ",".join(inputs) + "->" + output
x = op.einsum(tTensor.einsum)(equation, *tensors)
elif len(tensors) == 1:
x = tensors[0]
else:
x = scalars[0]
scalars = scalars[1:]
for scalar in scalars:
x = backend.multiply(x, scalar)
return x
@staticmethod
@einx.trace
def arange(n, dtype="int32"):
if isinstance(dtype, str):
dtype = getattr(tdtypes, dtype)
return op.arange(tTensor.arange)(n, dtype=dtype)
@staticmethod
@einx.trace
def concatenate(tensors, axis=0):
shape = list(tensors[0].shape)
shape[axis] = sum(tensor.shape[axis] for tensor in tensors)
return tracer.apply(
tTensor.cat, args=[*tensors], kwargs={"dim": axis}, output=tracer.Tensor(shape)
)
add = associative_binary_to_nary(elementwise(tTensor.add))
subtract = elementwise(tTensor.sub)
multiply = associative_binary_to_nary(elementwise(tTensor.mul))
true_divide = elementwise(tTensor.div)
floor_divide = elementwise(partial(tTensor.div, upcast=False))
divide = elementwise(tTensor.div)
logical_and = associative_binary_to_nary(elementwise(tTensor.mul))
logical_or = associative_binary_to_nary(elementwise(tTensor.add))
where = elementwise(tTensor.where)
less = elementwise(tracer.Operator("<"))
less_equal = elementwise(tracer.Operator("<="))
greater = elementwise(tracer.Operator(">"))
greater_equal = elementwise(tracer.Operator(">="))
equal = elementwise(tracer.Operator("=="))
not_equal = elementwise(tracer.Operator("!="))
maximum = associative_binary_to_nary(elementwise(tTensor.maximum))
minimum = associative_binary_to_nary(elementwise(tTensor.minimum))
sum = reduce(tTensor.sum)
mean = reduce(tTensor.mean)
var = reduce(tTensor.var)
std = reduce(tTensor.std)
count_nonzero = reduce(tTensor.sum)
min = reduce(tTensor.min)
max = reduce(tTensor.max)
# tinygrad's logsumexp currently does not support multiple axes, so
# we use our custom implementation instead:
# logsumexp = reduce(tTensor.logsumexp)
log = op.elementwise(tTensor.log)
exp = op.elementwise(tTensor.exp)
sqrt = op.elementwise(tTensor.sqrt)
rsqrt = op.elementwise(tTensor.rsqrt)
square = op.elementwise(tTensor.square)
@staticmethod
@einx.trace
def get_at(tensor, coordinates):
raise NotImplementedError()
@staticmethod
@einx.trace
def set_at(tensor, coordinates, updates):
raise NotImplementedError()
@staticmethod
@einx.trace
def add_at(tensor, coordinates, updates):
raise NotImplementedError()
@staticmethod
@einx.trace
def subtract_at(tensor, coordinates, updates):
raise NotImplementedError()
flip = op.keep_shape(tTensor.flip)
softmax = op.keep_shape(tTensor.softmax)
log_softmax = op.keep_shape(tTensor.log_softmax)
@staticmethod
@einx.trace
def stop_gradient(tensor):
return tensor # TODO: set requires_grad to False?
@staticmethod
@einx.trace
def vmap(op, in_axes, out_axes, input_shapes, output_shapes):
raise NotImplementedError(
"Functions relying on vmap are not supported for the tinygrad backend"
)
class random:
@einx.trace
def bernoulli(rng, p, shape):
return (
einx.tracer.apply(
tTensor.rand,
args=[*shape],
output=einx.tracer.Tensor(shape),
)
<= p
)
return tinygrad()
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