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Propagate kwargs through keras.ops.isclose #19782

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Merged
merged 10 commits into from
Jun 6, 2024
Merged

Propagate kwargs through keras.ops.isclose #19782

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1c74553
propagate kwargs through isclose
LarsKue May 31, 2024
445bd8a
switch isclose **kwargs to explicit kwargs
LarsKue Jun 3, 2024
28c7730
reduce line lengths
LarsKue Jun 3, 2024
a1e94ce
fix ops.isclose signature
LarsKue Jun 4, 2024
8e46db5
fix ops.IsClose compute_output_spec signature
LarsKue Jun 4, 2024
6015e66
implement isclose rtol atol equal_nan args for all backends
LarsKue Jun 4, 2024
c15dcdb
shorten line lengths again
LarsKue Jun 4, 2024
d982d16
revert using tf.experimental.numpy.isclose
LarsKue Jun 5, 2024
f64a5f0
fix lint
LarsKue Jun 5, 2024
fc45e87
add docs for new parameters
LarsKue Jun 5, 2024
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4 changes: 2 additions & 2 deletions keras/src/backend/jax/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -598,10 +598,10 @@ def imag(x):
return jnp.imag(x)


def isclose(x1, x2):
def isclose(x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
return jnp.isclose(x1, x2)
return jnp.isclose(x1, x2, rtol, atol, equal_nan)


@sparse.densifying_unary
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4 changes: 2 additions & 2 deletions keras/src/backend/numpy/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -505,8 +505,8 @@ def imag(x):
return np.imag(x)


def isclose(x1, x2):
return np.isclose(x1, x2)
def isclose(x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False):
return np.isclose(x1, x2, rtol, atol, equal_nan)


def isfinite(x):
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10 changes: 6 additions & 4 deletions keras/src/backend/tensorflow/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops
from tensorflow.python.ops.math_ops import is_nan

from keras.src import tree
from keras.src.backend import config
Expand Down Expand Up @@ -1250,16 +1251,17 @@ def imag(x):
return tf.math.imag(x)


def isclose(x1, x2):
def isclose(x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
dtype = dtypes.result_type(x1.dtype, x2.dtype)
x1 = tf.cast(x1, dtype)
x2 = tf.cast(x2, dtype)
if "float" in dtype:
# atol defaults to 1e-08
# rtol defaults to 1e-05
return tf.abs(x1 - x2) <= (1e-08 + 1e-05 * tf.abs(x2))
result = tf.abs(x1 - x2) <= (atol + rtol * tf.abs(x2))
if equal_nan:
result = result | (is_nan(x1) & is_nan(x2))
return result
else:
return tf.equal(x1, x2)

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4 changes: 2 additions & 2 deletions keras/src/backend/torch/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -697,13 +697,13 @@ def imag(x):
return torch.imag(x)


def isclose(x1, x2):
def isclose(x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False):
x1 = convert_to_tensor(x1)
x2 = convert_to_tensor(x2)
result_dtype = dtypes.result_type(x1.dtype, x2.dtype)
x1 = cast(x1, result_dtype)
x2 = cast(x2, result_dtype)
return torch.isclose(x1, x2)
return torch.isclose(x1, x2, rtol, atol, equal_nan)


def isfinite(x):
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17 changes: 11 additions & 6 deletions keras/src/ops/numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2801,30 +2801,35 @@ def imag(x):


class Isclose(Operation):
def call(self, x1, x2):
return backend.numpy.isclose(x1, x2)
def call(self, x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False):
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If you change the signature in keras.ops, you need to change the backend function for all 4 backends, not just numpy.

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Of course, silly mistake. I am still finding my way into the library. Fixed now!

return backend.numpy.isclose(x1, x2, rtol, atol, equal_nan)

def compute_output_spec(self, x1, x2):
def compute_output_spec(
self, x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False
):
x1_shape = getattr(x1, "shape", [])
x2_shape = getattr(x2, "shape", [])
output_shape = broadcast_shapes(x1_shape, x2_shape)
return KerasTensor(output_shape, dtype="bool")


@keras_export(["keras.ops.isclose", "keras.ops.numpy.isclose"])
def isclose(x1, x2):
def isclose(x1, x2, rtol=1e-5, atol=1e-8, equal_nan=False):
"""Return whether two tensors are element-wise almost equal.

Args:
x1: First input tensor.
x2: Second input tensor.
rtol: Relative tolerance.
atol: Absolute tolerance.
equal_nan: If `True`, element-wise NaNs are considered equal.

Returns:
Output boolean tensor.
"""
if any_symbolic_tensors((x1, x2)):
return Isclose().symbolic_call(x1, x2)
return backend.numpy.isclose(x1, x2)
return Isclose().symbolic_call(x1, x2, rtol, atol, equal_nan)
return backend.numpy.isclose(x1, x2, rtol, atol, equal_nan)


class Isfinite(Operation):
Expand Down