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Data.get_runtime_sanity_check_op extend #610

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Sep 2, 2021
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Data.get_runtime_sanity_check_op extend #610

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35 changes: 24 additions & 11 deletions returnn/tf/util/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1478,28 +1478,41 @@ def get_runtime_sanity_check_op(self):
checks = []
with tf.name_scope("runtime_sanity_check"):
shape = tf.shape(self.placeholder)
batch_dim = shape[self.batch_dim_axis] if self.have_batch_axis() else 1
rank = tf.rank(self.placeholder)
data = [str(self), "shape", shape]
data = ["Data.get_runtime_sanity_check_op:", str(self), "shape", shape]
for i, tag in enumerate(self.dim_tags):
if tag.dyn_size is not None:
data += ["dyn_size[%i]" % i, tag.dyn_size, ".shape", tf.shape(tag.dyn_size)]
data += [
"dyn_size[%i] (%s)" % (i, tag), tag.dyn_size, ".shape", tf.shape(tag.dyn_size)]
checks += [tf.Assert(tf.equal(rank, self.batch_ndim), data + ["-> invalid rank"])]
if self.have_batch_axis():
batch_dim_via_info = self.get_batch_dim()
checks += [
tf.Assert(tf.equal(batch_dim, batch_dim_via_info), data + ["-> invalid batch dim info", batch_dim_via_info])]
for i in range(self.batch_ndim):
if self.batch_shape[i] is not None:
checks += [tf.Assert(tf.equal(shape[i], self.batch_shape[i]), data + ["-> invalid shape[%i]" % i])]
dyn_size = self.dim_tags[i].dyn_size
if dyn_size is not None:
dyn_size_ext = self.dim_tags[i].dyn_size_ext
if dyn_size_ext and dyn_size_ext.placeholder is not None:
dyn_size = dyn_size_ext.placeholder
if dyn_size_ext.have_batch_axis() and self.have_batch_axis():
checks += [tf.Assert(
tf.equal(tf.shape(dyn_size)[dyn_size_ext.batch_dim_axis], batch_dim),
data + ["-> invalid axis %i tag dyn size batch dim" % i])]
checks += [tf.Assert(
# Note: in almost all cases, we have equality here.
# However, not strictly in all cases, e.g. DecideLayer, maybe some others...
tf.less_equal(tf.reduce_max(dyn_size), shape[i]),
# But that should not be more than 1 less.
tf.logical_or(
tf.logical_and(
tf.less_equal(tf.reduce_max(dyn_size), shape[i]),
tf.greater_equal(tf.reduce_max(dyn_size), shape[i] - 1)),
# In other rare cases, this might be a broadcast dim
# (e.g. as initial values of att weights for a rec loop).
tf.equal(1, shape[i])),
data + ["-> invalid shape[%i] or max(dyn_size[%i])" % (i, i)])]
batch_dim = shape[self.batch_dim_axis] if self.have_batch_axis() else 1
for i, tag in enumerate(self.dim_tags):
if tag.dyn_size is not None:
checks += [tf.Assert(
tf.reduce_all(tf.equal(tf.shape(tag.dyn_size), [batch_dim])),
data + ["-> invalid shape(dyn_size[%i]) or invalid batch dim" % i, batch_dim])]
checks += [dyn_size_ext.get_runtime_sanity_check_op()]
return tf.group(*checks)

def get_placeholder_kwargs(self, with_batch=True):
Expand Down