Consistency Model: Fix JIT compilation (#224)

* Consistency Models: fixes for TF jit

* Consistency Model: Fix JIT compilation

Adjusting the schedule requires special care to work with JIT
compilation.
With this commit, we pre-calculate the schedule, so that setting
`current_step` works in all backends.

bayesflow/networks/consistency_models/consistency_model.py

@@ -1,11 +1,11 @@
-import math
-
 import keras
 from keras import ops
 from keras.saving import (
     register_keras_serializable,
 )
 
+import numpy as np
+
 from bayesflow.types import Tensor
 from bayesflow.utils import find_network, keras_kwargs
 
@@ -82,32 +82,34 @@ def __init__(
 
         self.s0 = float(s0)
         self.s1 = float(s1)
-        self.current_step = 0.0
+        # 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.assign(0)
 
         self.seed_generator = keras.random.SeedGenerator()
 
-    def _schedule_discretization(self) -> int:
+    def _schedule_discretization(self, step) -> float:
         """Schedule function for adjusting the discretization level `N` during
         the course of training.
 
         Implements the function N(k) from [2], Section 3.4.
         """
 
-        k_ = math.floor(self.total_steps / (math.log(self.s1 / self.s0) / math.log(2.0) + 1.0))
-        out = min(self.s0 * math.pow(2.0, math.floor(self.current_step / k_)), self.s1) + 1.0
-        return int(out)
+        k_ = ops.floor(self.total_steps / (ops.log(self.s1 / self.s0) / ops.log(2.0) + 1.0))
+        out = ops.minimum(self.s0 * ops.power(2.0, ops.floor(step / k_)), self.s1) + 1.0
+        return out
 
     def _discretize_time(self, num_steps, rho=7.0):
         """Function for obtaining the discretized time according to [2],
         Section 2, bottom of page 2.
         """
 
-        N = num_steps + 1.0
+        N = num_steps + 1
         indices = ops.arange(1, N + 1, dtype="float32")
         one_over_rho = 1.0 / rho
         discretized_time = (
             self.eps**one_over_rho
-            + (indices - 1.0) / (N - 1.0) * (self.max_time**one_over_rho - self.eps**one_over_rho)
+            + (indices - 1.0) / (ops.cast(N, "float32") - 1.0) * (self.max_time**one_over_rho - self.eps**one_over_rho)
         ) ** rho
         return discretized_time
 
@@ -131,9 +133,36 @@ def build(self, xz_shape, conditions_shape=None):
         self.student_projector.build(input_shape)
 
         # Choose coefficient according to [2] Section 3.3
-        self.c_huber = 0.00054 * math.sqrt(xz_shape[-1])
+        self.c_huber = 0.00054 * ops.sqrt(xz_shape[-1])
         self.c_huber2 = self.c_huber**2
 
+        ## Calculate discretization schedule in advance
+        # The Jax compiler requires fixed-size arrays, so we have
+        # to store all the discretized_times in one matrix in advance
+        # and later only access the relevant entries.
+
+        # First, we calculate all unique numbers of discretization steps n
+        # in a loop, as self.total_steps might be large
+        self.max_n = int(self._schedule_discretization(self.total_steps))
+        assert self.max_n == self.s1 + 1
+        unique_n = set()
+        for step in range(int(self.total_steps)):
+            unique_n.add(int(self._schedule_discretization(step)))
+        unique_n = sorted(list(unique_n))
+
+        # Next, we calculate the discretized times for each n
+        # and establish a mapping between n and the position i of the
+        # discretizated times in the vector
+        discretized_times = np.zeros((len(unique_n), self.max_n + 1))
+        discretization_map = np.zeros((self.max_n + 1,), dtype=np.int32)
+        for i, n in enumerate(unique_n):
+            disc = self._discretize_time(n)
+            discretized_times[i, : len(disc)] = disc
+            discretization_map[n] = i
+        # Finally, we convert the vectors to tensors
+        self.discretized_times = ops.convert_to_tensor(discretized_times, dtype="float32")
+        self.discretization_map = ops.convert_to_tensor(discretization_map)
+
     def call(
         self,
         xz: Tensor,
@@ -224,19 +253,27 @@ def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "tr
 
         # The discretization schedule requires the number of passed training steps.
         # To be independent of external information, we track it here.
-        self.current_step += 1
+        if stage == "training":
+            self.current_step.assign_add(1)
+            self.current_step.assign(ops.minimum(self.current_step, self.total_steps - 1))
 
-        current_num_steps = self._schedule_discretization()
-        discretized_time = self._discretize_time(current_num_steps)
+        discretization_index = ops.take(
+            self.discretization_map, ops.cast(self._schedule_discretization(self.current_step), "int32")
+        )
+        discretized_time = ops.take(self.discretized_times, discretization_index, axis=0)
 
         # Randomly sample t_n and t_[n+1] and reshape to (batch_size, 1)
         # adapted noise schedule from [2], Section 3.5
         p_mean = -1.1
         p_std = 2.0
-        log_p = ops.log(
+        p = ops.where(
+            discretized_time[1:] > 0.0,
             ops.erf((ops.log(discretized_time[1:]) - p_mean) / (ops.sqrt(2.0) * p_std))
-            - ops.erf((ops.log(discretized_time[:-1]) - p_mean) / (ops.sqrt(2.0) * p_std))
+            - ops.erf((ops.log(discretized_time[:-1]) - p_mean) / (ops.sqrt(2.0) * p_std)),
+            0.0,
         )
+
+        log_p = ops.log(p)
         times = keras.random.categorical(ops.expand_dims(log_p, 0), ops.shape(x)[0], seed=self.seed_generator)[0]
         t1 = ops.take(discretized_time, times)[..., None]
         t2 = ops.take(discretized_time, times + 1)[..., None]