fix: init_ask detection and state.first_step

src/evox/workflows/distributed.py

@@ -6,8 +6,16 @@
 import jax.numpy as jnp
 import ray
 
-from evox import Algorithm, Problem, State, Workflow, use_state
-from evox.utils import algorithm_has_init_ask, parse_opt_direction
+from evox import (
+    Algorithm,
+    Problem,
+    State,
+    Workflow,
+    use_state,
+    has_init_ask,
+    has_init_tell,
+)
+from evox.utils import parse_opt_direction
 
 
 class WorkerWorkflow(Workflow):
@@ -36,7 +44,7 @@ def __init__(
         self.fit_transforms = fit_transforms
 
     def setup(self, key):
-        return State(generation=0)
+        return State(generation=0, first_step=True)
 
     def _get_slice(self, pop_size):
         slice_per_worker = pop_size // self.num_workers
@@ -45,20 +53,23 @@ def _get_slice(self, pop_size):
         end = start + slice_per_worker + (self.worker_index < remainder)
         return start, end
 
+    def _ask(self, state):
+        if has_init_ask(self.algorithm) and state.first_step:
+            ask = self.algorithm.init_ask
+        else:
+            ask = self.algorithm.ask
+
+        # candidate: individuals that need to be evaluated (may differ from population)
+        # Note: num_cands can be different from init_ask() and ask()
+        cands, state = use_state(ask)(state)
+
+        return cands, state
+
     def step1(self, state: State):
         if "pre_ask" in self.non_empty_hooks:
             ray.get(self.monitor_actor.push.remote("pre_ask", state))
 
-        if state.generation == 0:
-            is_init = algorithm_has_init_ask(self.algorithm, state)
-        else:
-            is_init = False
-
-        if is_init:
-            cand_sol, state = use_state(self.algorithm.init_ask)(state)
-        else:
-            cand_sol, state = use_state(self.algorithm.ask)(state)
-
+        cand_sol, state = self._ask(state)
         if "post_ask" in self.non_empty_hooks:
             ray.get(self.monitor_actor.push.remote("post_ask", None, cand_sol))
 
@@ -82,12 +93,17 @@ def step1(self, state: State):
 
         return partial_fitness, state
 
-    def step2(self, state: State, fitness: List[jax.Array]):
-        if state.generation == 0:
-            is_init = algorithm_has_init_ask(self.algorithm, state)
+    def _tell(self, state, transformed_fitness):
+        if has_init_tell(self.algorithm) and state.first_step:
+            tell = self.algorithm.init_tell
         else:
-            is_init = False
+            tell = self.algorithm.tell
 
+        state = use_state(tell)(state, transformed_fitness)
+
+        return state
+
+    def step2(self, state: State, fitness: List[jax.Array]):
         fitness = jnp.concatenate(fitness, axis=0)
         fitness = fitness * self.opt_direction
 
@@ -112,15 +128,19 @@ def step2(self, state: State, fitness: List[jax.Array]):
                 )
             )
 
-        if is_init:
-            state = use_state(self.algorithm.init_tell)(state, fitness)
-        else:
-            state = use_state(self.algorithm.tell)(state, fitness)
+        state = self._tell(state, fitness)
 
         if "post_tell" in self.non_empty_hooks:
             ray.get(self.monitor_actor.push.remote("post_tell", state))
-
-        return state.update(generation=state.generation + 1)
+
+
+        if has_init_ask(self.algorithm) and state.first_step:
+            # this ensures that _step() will be re-jitted
+            state = state.replace(generation=state.generation + 1, first_step=False)
+        else:
+            state = state.replace(generation=state.generation + 1)
+
+        return state
 
     def valid(self, state: State, metric: str):
         new_state = use_state(self.problem.valid)(state, metric=metric)