def testErrors(self):
"""Tests that proper errors are raised.
"""
shape = [2, 3]
gen = random.Generator.from_seed(1234)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
r"must have shape \[\], not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, [0, 0], shape)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
r"must have shape \[\], not"):
gen_stateful_random_ops.rng_skip(
gen.state.handle, gen.algorithm, [0, 0])
with self.assertRaisesWithPredicateMatch(
TypeError, "EagerTensor of dtype int64"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, 1.1, shape)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"Unsupported algorithm id"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, 123, shape)
var = variables.Variable([0, 0], dtype=dtypes.int32)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"dtype of RNG state variable must be int64, not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
var = variables.Variable([[0]], dtype=dtypes.int64)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"RNG state must have one and only one dimension, not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
var = variables.Variable([0], dtype=dtypes.int64)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"For the Philox algorithm, the size of state must be at least"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
with self.assertRaisesWithPredicateMatch(
ValueError,
"minval must be a scalar; got a tensor of shape "):
@def_function.function
def f():
gen.uniform(shape=shape, minval=array_ops.zeros(shape, "int32"),
maxval=100, dtype="int32")
f()
with self.assertRaisesWithPredicateMatch(
ValueError,
"maxval must be a scalar; got a tensor of shape "):
@def_function.function
def f2():
gen.uniform(
shape=shape, minval=0, maxval=array_ops.ones(shape, "int32") * 100,
dtype="int32")
f2()
def skip(self, delta):
"""Advance the counter of a counter-based RNG.
Args:
delta: the amount of advancement. The state of the RNG after
`skip(n)` will be the same as that after `normal([n])`
(or any other distribution). The actual increment added to the
counter is an unspecified implementation detail.
"""
gen_stateful_random_ops.rng_skip(self.state.handle, self.algorithm, delta)
def skip(self, delta):
"""Advance the counter of a counter-based RNG.
Args:
delta: the amount of advancement. The state of the RNG after
`skip(n)` will be the same as that after `normal([n])`
(or any other distribution). The actual increment added to the
counter is an unspecified implementation detail.
"""
if compat.forward_compatible(2020, 10, 25):
return self._skip(delta)
gen_stateful_random_ops.rng_skip(
self.state.handle, math_ops.cast(self.algorithm, dtypes.int64),
math_ops.cast(delta, dtypes.int64))
def testErrors(self):
"""Tests that proper errors are raised.
"""
shape = [2, 3]
gen = random.Generator.from_seed(1234)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
r"must have shape \[\], not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, [0, 0], shape)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
r"must have shape \[\], not"):
gen_stateful_random_ops.rng_skip(
gen.state.handle, gen.algorithm, [0, 0])
with self.assertRaisesWithPredicateMatch(
TypeError, "EagerTensor of dtype int64"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, 1.1, shape)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"Unsupported algorithm id"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, 123, shape)
var = variables.Variable([0, 0], dtype=dtypes.int32)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"dtype of RNG state variable must be int64, not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
var = variables.Variable([[0]], dtype=dtypes.int64)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"RNG state must have one and only one dimension, not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
var = variables.Variable([0], dtype=dtypes.int64)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"For the Philox algorithm, the size of state must be at least"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
def testErrors(self):
"""Tests that proper errors are raised.
"""
shape = [2, 3]
gen = random.Generator.from_seed(1234)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
r"must have shape \[\], not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, [0, 0], shape)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
r"must have shape \[\], not"):
gen_stateful_random_ops.rng_skip(
gen.state.handle, gen.algorithm, [0, 0])
with self.assertRaisesWithPredicateMatch(
TypeError, "Requested dtype: int64"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, 1.1, shape)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"Unsupported algorithm id"):
gen_stateful_random_ops.stateful_standard_normal_v2(
gen.state.handle, 123, shape)
var = variables.Variable([0, 0], dtype=dtypes.int32)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"dtype of RNG state variable must be int64, not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
var = variables.Variable([[0]], dtype=dtypes.int64)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"RNG state must have one and only one dimension, not"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
var = variables.Variable([0], dtype=dtypes.int64)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"For the Philox algorithm, the size of state must be at least"):
gen_stateful_random_ops.stateful_standard_normal_v2(
var.handle, random.RNG_ALG_PHILOX, shape)
