add nan safe log&divide

python/sdist/amici/jax.template.py

@@ -1,7 +1,8 @@
+# ruff: noqa: F401, F821, F841
 import jax.numpy as jnp
 from interpax import interp1d
 
-from amici.jax.model import JAXModel
+from amici.jax.model import JAXModel, safe_log, safe_div
 
 
 class JAXModel_TPL_MODEL_NAME(JAXModel):
@@ -11,7 +12,6 @@ def __init__(self):
         super().__init__()
 
     def _xdot(self, t, x, args):
-
         pk, tcl = args
 
         TPL_X_SYMS = x
@@ -24,7 +24,6 @@ def _xdot(self, t, x, args):
         return TPL_XDOT_RET
 
     def _w(self, t, x, pk, tcl):
-
         TPL_X_SYMS = x
         TPL_PK_SYMS = pk
         TPL_TCL_SYMS = tcl
@@ -34,23 +33,20 @@ def _w(self, t, x, pk, tcl):
         return TPL_W_RET
 
     def _x0(self, pk):
-
         TPL_PK_SYMS = pk
 
         TPL_X0_EQ
 
         return TPL_X0_RET
 
     def _x_solver(self, x):
-
         TPL_X_RDATA_SYMS = x
 
         TPL_X_SOLVER_EQ
 
         return TPL_X_SOLVER_RET
 
     def _x_rdata(self, x, tcl):
-
         TPL_X_SYMS = x
         TPL_TCL_SYMS = tcl
 
@@ -59,7 +55,6 @@ def _x_rdata(self, x, tcl):
         return TPL_X_RDATA_RET
 
     def _tcl(self, x, pk):
-
         TPL_X_RDATA_SYMS = x
         TPL_PK_SYMS = pk
 
@@ -68,7 +63,6 @@ def _tcl(self, x, pk):
         return TPL_TOTAL_CL_RET
 
     def _y(self, t, x, pk, tcl):
-
         TPL_X_SYMS = x
         TPL_PK_SYMS = pk
         TPL_W_SYMS = self._w(t, x, pk, tcl)
@@ -86,7 +80,6 @@ def _sigmay(self, y, pk):
 
         return TPL_SIGMAY_RET
 
-
     def _nllh(self, t, x, pk, tcl, my, iy):
         y = self._y(t, x, pk, tcl)
         TPL_Y_SYMS = y

python/sdist/amici/jax/model.py

@@ -557,3 +557,39 @@ def simulate_condition(
             stats_dyn=stats_dyn,
             stats_posteq=stats_posteq,
         )
+
+
+def safe_log(x: jnp.float_) -> jnp.float_:
+    """
+    Safe logarithm that returns `jnp.log(jnp.finfo(jnp.float_).eps)` for x <= 0.
+
+    :param x:
+        input
+    :return:
+        logarithm of x
+    """
+    # see https://docs.kidger.site/equinox/api/debug/, need double jnp.where to guard
+    # against nans in forward & backward passes
+    safe_x = jnp.where(
+        x > jnp.finfo(jnp.float_).eps, x, jnp.finfo(jnp.float_).eps
+    )
+    return jnp.where(
+        x > 0, jnp.log(safe_x), jnp.log(jnp.finfo(jnp.float_).eps)
+    )
+
+
+def safe_div(x: jnp.float_, y: jnp.float_) -> jnp.float_:
+    """
+    Safe division that returns `x/jnp.finfo(jnp.float_).eps` for `y == 0`.
+
+    :param x:
+        numerator
+    :param y:
+        denominator
+    :return:
+        x / y
+    """
+    # see https://docs.kidger.site/equinox/api/debug/, need double jnp.where to guard
+    # against nans in forward & backward passes
+    safe_y = jnp.where(y != 0, y, jnp.finfo(jnp.float_).eps)
+    return jnp.where(y != 0, x / safe_y, x / jnp.finfo(jnp.float_).eps)

python/sdist/amici/jaxcodeprinter.py

@@ -27,6 +27,15 @@ def _print_AmiciSpline(self, expr: sp.Expr) -> str:
         # FIXME: untested, where are spline nodes coming from anyways?
         return f'interp1d(time, {self.doprint(expr.args[2:])}, kind="cubic")'
 
+    def _print_log(self, expr: sp.Expr) -> str:
+        return f"safe_log({self.doprint(expr.args[0])})"
+
+    def _print_Mul(self, expr: sp.Expr) -> str:
+        numer, denom = expr.as_numer_denom()
+        if denom == 1:
+            return super()._print_Mul(expr)
+        return f"safe_div({self.doprint(numer)}, {self.doprint(denom)})"
+
     def _get_sym_lines(
         self,
         symbols: sp.Matrix | Iterable[str],

tests/benchmark-models/test_petab_benchmark.py

@@ -299,14 +299,8 @@ def test_jax_llh(benchmark_problem):
 
     np.random.seed(cur_settings.rng_seed)
 
-    problems_for_gradient_check_jax = list(
-        set(problems_for_gradient_check) - {"Laske_PLOSComputBiol2019"}
-        # Laske has nan values in gradient due to nan values in observables that are not used in the likelihood
-        # but are problematic during backpropagation
-    )
-
     problem_parameters = None
-    if problem_id in problems_for_gradient_check_jax:
+    if problem_id in problems_for_gradient_check:
         point = petab_problem.x_nominal_free_scaled
         for _ in range(20):
             amici_solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)
@@ -352,12 +346,12 @@ def test_jax_llh(benchmark_problem):
                 [problem_parameters[pid] for pid in jax_problem.parameter_ids]
             ),
         )
-    if problem_id in problems_for_gradient_check_jax:
-        (llh_jax, _), sllh_jax = eqx.filter_jit(
-            eqx.filter_value_and_grad(run_simulations, has_aux=True)
+    if problem_id in problems_for_gradient_check:
+        (llh_jax, _), sllh_jax = eqx.filter_value_and_grad(
+            run_simulations, has_aux=True
         )(jax_problem, simulation_conditions)
     else:
-        llh_jax, _ = beartype(eqx.filter_jit(run_simulations))(
+        llh_jax, _ = beartype(run_simulations)(
             jax_problem, simulation_conditions
         )
 
@@ -369,14 +363,14 @@ def test_jax_llh(benchmark_problem):
         err_msg=f"LLH mismatch for {problem_id}",
     )
 
-    if problem_id in problems_for_gradient_check_jax:
+    if problem_id in problems_for_gradient_check:
         sllh_amici = r_amici[SLLH]
         np.testing.assert_allclose(
             sllh_jax.parameters,
             np.array([sllh_amici[pid] for pid in jax_problem.parameter_ids]),
             rtol=1e-2,
             atol=1e-2,
-            err_msg=f"SLLH mismatch for {problem_id}",
+            err_msg=f"SLLH mismatch for {problem_id}, {dict(zip(jax_problem.parameter_ids, sllh_jax.parameters))}",
         )