Fix: validation caching

src/cellflow/solvers/_genot.py

@@ -293,26 +293,31 @@ def _get_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable:
 
         kwargs = dict(kwargs_frozen)
 
-        def vf(t: float, x: jnp.ndarray, args: tuple[dict[str, jnp.ndarray], jnp.ndarray]) -> jnp.ndarray:
-            params = self.vf_state.params
-            x_0, condition, encoder_noise = args
+        def vf(
+            t: float, x: jnp.ndarray, args: tuple[Any, jnp.ndarray, dict[str, jnp.ndarray], jnp.ndarray]
+        ) -> jnp.ndarray:
+            params, x_0, condition, encoder_noise = args
             return self.vf_state.apply_fn({"params": params}, t, x, x_0, condition, encoder_noise, train=False)[0]
 
         def solve_ode(
-            latent: jnp.ndarray, x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray
+            params: Any,
+            latent: jnp.ndarray,
+            x: jnp.ndarray,
+            condition: dict[str, jnp.ndarray],
+            encoder_noise: jnp.ndarray,
         ) -> jnp.ndarray:
             term = diffrax.ODETerm(vf)
             sol = diffrax.diffeqsolve(
                 term,
                 t0=0.0,
                 t1=1.0,
                 y0=latent,
-                args=(x, condition, encoder_noise),
+                args=(params, x, condition, encoder_noise),
                 **kwargs,
             )
             return sol.ys[0]
 
-        fn = jax.jit(jax.vmap(solve_ode, in_axes=[0, 0, None, None]))
+        fn = jax.jit(jax.vmap(solve_ode, in_axes=[None, 0, 0, None, None]))
         self._predict_fn_cache[kwargs_frozen] = fn
         return fn
 
@@ -337,7 +342,7 @@ def _predict_jit(
         latent = self.latent_noise_fn(rng_genot, (x.shape[0],))
 
         predict_fn = self._get_predict_fn(kwargs_frozen)
-        return predict_fn(latent, x, condition, encoder_noise)
+        return predict_fn(self.vf_state.params, latent, x, condition, encoder_noise)
 
     @property
     def is_trained(self) -> bool:

src/cellflow/solvers/_otfm.py

@@ -198,24 +198,25 @@ def _get_predict_fn(self, kwargs_frozen: frozen_dict.FrozenDict) -> Callable:
 
         kwargs = dict(kwargs_frozen)
 
-        def vf(t: jnp.ndarray, x: jnp.ndarray, args: tuple[dict[str, jnp.ndarray], jnp.ndarray]) -> jnp.ndarray:
-            params = self.vf_state_inference.params
-            condition, encoder_noise = args
+        def vf(t: jnp.ndarray, x: jnp.ndarray, args: tuple[Any, dict[str, jnp.ndarray], jnp.ndarray]) -> jnp.ndarray:
+            params, condition, encoder_noise = args
             return self.vf_state_inference.apply_fn({"params": params}, t, x, condition, encoder_noise, train=False)[0]
 
-        def solve_ode(x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray) -> jnp.ndarray:
+        def solve_ode(
+            params: Any, x: jnp.ndarray, condition: dict[str, jnp.ndarray], encoder_noise: jnp.ndarray
+        ) -> jnp.ndarray:
             ode_term = diffrax.ODETerm(vf)
             result = diffrax.diffeqsolve(
                 ode_term,
                 t0=0.0,
                 t1=1.0,
                 y0=x,
-                args=(condition, encoder_noise),
+                args=(params, condition, encoder_noise),
                 **kwargs,
             )
             return result.ys[0]
 
-        fn = jax.jit(jax.vmap(solve_ode, in_axes=[0, None, None]))
+        fn = jax.jit(jax.vmap(solve_ode, in_axes=[None, 0, None, None]))
         self._predict_fn_cache[kwargs_frozen] = fn
         return fn
 
@@ -238,7 +239,7 @@ def _predict_jit(
         encoder_noise = jnp.zeros(noise_dim) if use_mean else jax.random.normal(rng, noise_dim)
 
         predict_fn = self._get_predict_fn(kwargs_frozen)
-        return predict_fn(x, condition, encoder_noise)
+        return predict_fn(self.vf_state_inference.params, x, condition, encoder_noise)
 
     def predict(
         self,

tests/trainer/test_trainer.py

@@ -8,7 +8,7 @@
 
 import cellflow
 from cellflow._compat import ConstantNoiseFlow
-from cellflow.solvers import _otfm
+from cellflow.solvers import _genot, _otfm
 from cellflow.training import CellFlowTrainer, ComputationCallback, Metrics
 from cellflow.utils import match_linear
 
@@ -227,3 +227,68 @@ def test_predict_kwargs_iter(self, dataloader, valid_loader):
         diff_2 = end_2 - start_2
 
         assert diff_2 - diff_1 > 0.4
+
+    @pytest.mark.parametrize("solver_class", ["otfm", "genot"])
+    def test_validation_metrics_change_when_predict_cache_is_reused(
+        self,
+        dataloader,
+        valid_loader,
+        solver_class,
+    ):
+        opt = optax.adam(1e-3)
+        if solver_class == "otfm":
+            vf = cellflow.networks.ConditionalVelocityField(
+                output_dim=5,
+                max_combination_length=2,
+                condition_embedding_dim=12,
+                hidden_dims=(32, 32),
+                decoder_dims=(32, 32),
+            )
+            solver = _otfm.OTFlowMatching(
+                vf=vf,
+                match_fn=match_linear,
+                probability_path=ConstantNoiseFlow(0.0),
+                optimizer=opt,
+                conditions=cond,
+                rng=vf_rng,
+            )
+        else:
+            vf = cellflow.networks.GENOTConditionalVelocityField(
+                output_dim=5,
+                max_combination_length=2,
+                condition_embedding_dim=12,
+                hidden_dims=(32, 32),
+                decoder_dims=(32, 32),
+            )
+            solver = _genot.GENOT(
+                vf=vf,
+                data_match_fn=match_linear,
+                probability_path=ConstantNoiseFlow(0.0),
+                optimizer=opt,
+                source_dim=5,
+                target_dim=5,
+                conditions=cond,
+                rng=vf_rng,
+            )
+
+        trainer = CellFlowTrainer(solver=solver, predict_kwargs={"max_steps": 3, "throw": False})
+        metrics_callback = Metrics(metrics=["e_distance"])
+
+        valid_source_data, valid_true_data, valid_pred_data = trainer._validation_step(valid_loader)
+        metric_before = metrics_callback.on_log_iteration(valid_source_data, valid_true_data, valid_pred_data, solver)[
+            "val_e_distance_mean"
+        ]
+
+        batch = dataloader.sample(None)
+        metric_diffs = []
+        for i in range(3):
+            solver.step_fn(jax.random.PRNGKey(i), batch)
+            valid_source_data, valid_true_data, valid_pred_data = trainer._validation_step(valid_loader)
+            metric_after = metrics_callback.on_log_iteration(
+                valid_source_data, valid_true_data, valid_pred_data, solver
+            )["val_e_distance_mean"]
+            metric_diffs.append(abs(metric_after - metric_before))
+            metric_before = metric_after
+
+        assert len(solver._predict_fn_cache) == 1
+        assert any(diff > 0 for diff in metric_diffs)