Seeding breaks random variate generation via copy

[chmp]

Edward relies on edward.copy to generate new variates of random variables (to quote klqp.py: Copy q(z) to obtain new set of posterior samples). Calling edward.set_seed does seem to prevent this mechanism from working properly. Minimal example:

import tensorflow as tf
import edward as ed

do_seed = True
if do_seed:
    ed.set_seed(42)

op = tf.random_normal([3])
copy_op = ed.copy(op)
print(ed.get_session().run([op, copy_op]))

# do_seed == False: [
#   array([-1.33050728, -0.36147749,  0.19219629], dtype=float32), 
#   array([ 0.32038462, -0.05204332, -1.20637977], dtype=float32)
# ]

# do_seed == True: [
#   array([ 0.80214548,  0.50567901,  1.81201267], dtype=float32), 
#   array([ 0.80214548,  0.50567901,  1.81201267], dtype=float32)
# ]

A similar behavior can be observed when replacing op with ed.models.Normal(0.0, 1.0).value(). With seed it generates both two equal numbers (for example: 0.1395757 and 0.1395757). Without seed two different variates are generated (for example: -0.64106858, 0.47237226).

Versions:

python: 3.5.1
tensorflow: 1.0.0
edward: 1.2.2

[dustinvtran]

Thanks for this catch.

This doesn't break when explicitly copying random variables (and not their tensors). This is whatKLqp and related algorithms do, so fortunately they work as intended.

import tensorflow as tf
import edward as ed
from edward.models import Normal

do_seed = True
if do_seed:
    ed.set_seed(42)

op = Normal(tf.zeros(3), tf.ones(3))
copy_op = ed.copy(op)
print(ed.get_session().run([op.value(), copy_op.value()]))

# do_seed == True: [
#  array([ 0.1395757 ,  1.83572245, -0.27154389], dtype=float32), 
#  array([-1.425722  , -1.01437879,  0.39387721], dtype=float32)
# ]

The consequence of random tf.Tensors + seed setting is mysterious. For all use cases I imagine for copy, I think it always makes sense for the copied tensor to output a different random value. So we should fix that.

[chmp]

Interesting. I didn't see this, since I was experimenting with bypassing edward and setting the value of the random variable directly (via value=...). In this case, the variates end up the same also - I guess unsurprising given the implementation :).

My theory of why this happens: I assume the seed for the value is copied as well, when performing the copy. Whereas, copying a random variable without a fixed value at construction only copies the parameters, since its value was never added to the _kwargs attribute.

For reference: I'm trying something akin to ed.models.Normal(0.0, 1.0, value=tf.random_normal([])).