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Fix: coupling flows and CMs not working on GPU due to int type #273

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Merged
merged 1 commit into from
Dec 9, 2024
Merged

Fix: coupling flows and CMs not working on GPU due to int type #273

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4 changes: 2 additions & 2 deletions bayesflow/networks/consistency_models/consistency_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,7 +83,7 @@ def __init__(
self.s0 = float(s0)
self.s1 = float(s1)
# create variable that works with JIT compilation
self.current_step = self.add_weight(name="current_step", initializer="zeros", trainable=False, dtype="int32")
self.current_step = self.add_weight(name="current_step", initializer="zeros", trainable=False, dtype="int")
self.current_step.assign(0)

self.seed_generator = keras.random.SeedGenerator()
Expand Down Expand Up @@ -258,7 +258,7 @@ def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "tr
self.current_step.assign(ops.minimum(self.current_step, self.total_steps - 1))

discretization_index = ops.take(
self.discretization_map, ops.cast(self._schedule_discretization(self.current_step), "int32")
self.discretization_map, ops.cast(self._schedule_discretization(self.current_step), "int")
)
discretized_time = ops.take(self.discretized_times, discretization_index, axis=0)

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4 changes: 2 additions & 2 deletions bayesflow/networks/coupling_flow/permutations/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,12 @@ def build(self, xz_shape: Shape, **kwargs) -> None:
shape=(xz_shape[-1],),
initializer=keras.initializers.Constant(forward_indices),
trainable=False,
dtype="int32",
dtype="int",
)

self.inverse_indices = self.add_weight(
shape=(xz_shape[-1],),
initializer=keras.initializers.Constant(inverse_indices),
trainable=False,
dtype="int32",
dtype="int",
)
4 changes: 2 additions & 2 deletions bayesflow/networks/coupling_flow/permutations/swap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,12 @@ def build(self, xz_shape: Shape, **kwargs) -> None:
shape=(xz_shape[-1],),
initializer=keras.initializers.Constant(forward_indices),
trainable=False,
dtype="int32",
dtype="int",
)

self.inverse_indices = self.add_variable(
shape=(xz_shape[-1],),
initializer=keras.initializers.Constant(inverse_indices),
trainable=False,
dtype="int32",
dtype="int",
)
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