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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
def create():
import tensorflow as tf
import tensorflow.experimental.numpy as tnp
ttf = tracer.import_("tensorflow", "tf")
ttnp = tracer.import_("tensorflow.experimental.numpy", "tnp")
def _broadcast_static_shape(shape1, shape2):
assert len(shape1) == len(shape2) and all(
s1 == s2 or s1 == 1 or s2 == 1 for s1, s2 in zip(shape1, shape2)
)
return tuple(max(s1, s2) for s1, s2 in zip(shape1, shape2))
class tensorflow(Backend):
name = "tensorflow"
tensor_types = [tf.Tensor]
@staticmethod
@einx.trace
def to_tensor(tensor, shape):
return einx.tracer.apply(
ttf.convert_to_tensor,
args=[tensor],
output=einx.tracer.Tensor(shape),
)
reshape = op.reshape(ttnp.reshape)
transpose = op.transpose(ttnp.transpose)
broadcast_to = op.broadcast_to(ttnp.broadcast_to)
@staticmethod
@einx.trace
def einsum(equation, *tensors):
return op.einsum(ttnp.einsum)(equation, *tensors, optimize="optimal")
arange = op.arange(ttnp.arange)
stack = op.stack(ttnp.stack)
concatenate = op.concatenate(ttnp.concatenate)
add = associative_binary_to_nary(op.elementwise(ttnp.add))
subtract = op.elementwise(ttnp.subtract)
multiply = associative_binary_to_nary(op.elementwise(ttnp.multiply))
true_divide = op.elementwise(ttnp.true_divide)
floor_divide = op.elementwise(ttnp.floor_divide)
divide = op.elementwise(ttnp.divide)
logical_and = associative_binary_to_nary(op.elementwise(ttnp.logical_and))
logical_or = associative_binary_to_nary(op.elementwise(ttnp.logical_or))
where = op.elementwise(ttnp.where)
less = op.elementwise(ttnp.less)
less_equal = op.elementwise(ttnp.less_equal)
greater = op.elementwise(ttnp.greater)
greater_equal = op.elementwise(ttnp.greater_equal)
equal = op.elementwise(ttnp.equal)
not_equal = op.elementwise(ttnp.not_equal)
maximum = associative_binary_to_nary(op.elementwise(ttnp.maximum))
minimum = associative_binary_to_nary(op.elementwise(ttnp.minimum))
sum = op.reduce(ttnp.sum)
mean = op.reduce(ttnp.mean)
var = op.reduce(ttnp.var)
std = op.reduce(ttnp.std)
prod = op.reduce(ttnp.prod)
count_nonzero = op.reduce(ttnp.count_nonzero)
any = op.reduce(ttnp.any)
all = op.reduce(ttnp.all)
min = op.reduce(ttnp.min)
max = op.reduce(ttnp.max)
logsumexp = op.reduce(ttf.math.reduce_logsumexp)
log = op.elementwise(ttnp.log)
exp = op.elementwise(ttnp.exp)
sqrt = op.elementwise(ttnp.sqrt)
rsqrt = op.elementwise(ttf.math.rsqrt)
square = op.elementwise(ttnp.square)
@classmethod
@einx.trace
def get_at(backend, tensor, coordinates):
coordinates, _ = backend._prepare_coordinates_and_update(coordinates, None)
if isinstance(coordinates, tuple):
out_shape = coordinates[0].shape
coordinates = ttf.stack(coordinates, axis=-1)
else:
out_shape = coordinates.shape[:-1]
return einx.tracer.apply(
ttf.gather_nd,
args=[tensor, coordinates],
output=einx.tracer.Tensor(out_shape),
)
@classmethod
@einx.trace
def _prepare_coordinates_and_update(backend, coordinates, updates):
assert updates is None or isinstance(updates, einx.tracer.Tensor)
if isinstance(coordinates, tuple):
assert all(isinstance(c, einx.tracer.Tensor) for c in coordinates)
shape = coordinates[0].shape
for c in coordinates[1:]:
shape = _broadcast_static_shape(shape, c.shape)
coordinates = [backend.broadcast_to(c, shape) for c in coordinates]
coordinates = backend.stack(coordinates, axis=-1)
else:
assert isinstance(coordinates, einx.tracer.Tensor)
coordinates = coordinates[(slice(None),) * (coordinates.ndim - 1) + (None,)]
coordinates = coordinates[..., None]
assert updates is None or updates.ndim + 1 == coordinates.ndim
# Broadcast to common shape
if updates is None:
shape = coordinates.shape[:-1]
else:
shape = _broadcast_static_shape(updates.shape, coordinates.shape[:-1])
coordinates = backend.broadcast_to(coordinates, shape + coordinates.shape[-1:])
if updates is not None:
updates = backend.broadcast_to(updates, shape)
return coordinates, updates
@classmethod
@einx.trace
def set_at(backend, tensor, coordinates, updates):
coordinates, updates = backend._prepare_coordinates_and_update(coordinates, updates)
return einx.tracer.apply(
ttf.tensor_scatter_nd_update,
args=[tensor, coordinates, updates],
output=einx.tracer.Tensor(tensor.shape),
)
@classmethod
@einx.trace
def add_at(backend, tensor, coordinates, updates):
coordinates, updates = backend._prepare_coordinates_and_update(coordinates, updates)
return einx.tracer.apply(
ttf.tensor_scatter_nd_add,
args=[tensor, coordinates, updates],
output=einx.tracer.Tensor(tensor.shape),
)
@classmethod
@einx.trace
def subtract_at(backend, tensor, coordinates, updates):
coordinates, updates = backend._prepare_coordinates_and_update(coordinates, updates)
return einx.tracer.apply(
ttf.tensor_scatter_nd_sub,
args=[tensor, coordinates, updates],
output=einx.tracer.Tensor(tensor.shape),
)
@staticmethod
@einx.trace
def flip(x, axis):
if isinstance(axis, int):
axis = [axis]
return op.keep_shape(ttf.reverse)(x, axis)
@staticmethod
@einx.trace
def roll(x, axis, shift):
if isinstance(axis, int):
axis = [axis]
if isinstance(shift, int):
shift = [shift]
return op.keep_shape(ttf.roll)(x, tuple(shift), axis=tuple(axis))
@staticmethod
@einx.trace
def softmax(x, axis):
if isinstance(axis, (list, tuple)):
if len(axis) != 1:
raise ValueError(
"Tensorflow only supports softmax along a single axis, "
f"got {len(axis)} axes"
)
axis = axis[0]
return op.keep_shape(ttf.nn.softmax)(x, axis=axis)
@staticmethod
@einx.trace
def log_softmax(x, axis):
if isinstance(axis, (list, tuple)):
if len(axis) != 1:
raise ValueError(
"Tensorflow only supports log_softmax along a single axis, "
f"got {len(axis)} axes"
)
axis = axis[0]
return op.keep_shape(ttf.nn.log_softmax)(x, axis=axis)
sqrt = op.keep_shape(ttf.math.sqrt)
rsqrt = op.keep_shape(ttf.math.rsqrt)
square = op.keep_shape(ttnp.square)
stop_gradient = op.keep_shape(ttf.stop_gradient)
@staticmethod
def vmap(op, in_axes, out_axes, input_shapes, output_shapes):
@einx.trace
def inner(*args):
# TODO: suboptimal (?) implementation of vmap in tensorflow that transposes the
# vmapped axis to the front and calls tf.vectorized_map. Possible optimization:
# Transpose only once for multiple vmaps?
if len(args) != len(in_axes):
raise ValueError(f"Expected {len(in_axes)} arguments, got {len(args)}")
value = {arg.shape[axis] for arg, axis in zip(args, in_axes) if axis is not None}
if len(value) != 1:
raise ValueError(
f"Expected all arguments to have same size along vmap axis, got {value}"
)
value = value.pop()
# Move vmapped axes to front
xs = []
for arg, axis in zip(args, in_axes):
if axis is not None:
if axis != 0:
perm = [axis] + [a for a in range(len(arg.shape)) if a != axis]
arg = einx.tracer.op.transpose(ttnp.transpose)(arg, perm)
else:
arg = arg[tf.newaxis]
xs.append(arg)
op2 = einx.trace(
lambda xs: op(*xs), args=[[einx.tracer.Tensor(x.shape[1:]) for x in xs]]
)
xs = einx.tracer.apply(
ttf.vectorized_map,
args=[op2, xs],
output=[einx.tracer.Tensor(shape) for shape in output_shapes],
)
if len(xs) != len(out_axes):
raise ValueError(
f"Expected {len(out_axes)} arguments from vmapped function, got {len(xs)}"
)
# Move vmapped axis to out_axis
xs = [
einx.tracer.op.transpose(ttnp.transpose)(
x,
[
(a + 1 if a < out_axis else (0 if a == out_axis else a))
for a in range(len(x.shape))
],
)
for x, out_axis in zip(xs, out_axes)
]
return tuple(xs)
return inner
class random:
@einx.trace
def bernoulli(rng, p, shape):
return (
einx.tracer.apply(
ttf.random.uniform,
args=[shape],
kwargs={"minval": 0.0, "maxval": 1.0, "dtype": "float32", "seed": rng},
output=einx.tracer.Tensor(shape),
)
<= p
)
return tensorflow()
|
