enh: jit gradient and value

src/zfit_physics/tfpwa/loss.py

@@ -8,6 +8,7 @@
 
 import zfit
 import zfit.z.numpy as znp
+from zfit import z
 from zfit.core.interfaces import ZfitParameter
 from zfit.util.container import convert_to_container
 
@@ -32,14 +33,12 @@ def nll_from_fcn(fcn: tf_pwa.model.FCN, *, params: ParamType = None):
 
     # something is off here: for the value, we need to pass the parameters as a dict
     # but for the gradient/hesse, we need to pass them as a list
-    # TODO: activate if https://github.com/jiangyi15/tf-pwa/pull/153 is merged
-    # @z.function(wraps="loss")
+    @z.function(wraps="loss")
     def eval_func(params):
         paramdict = make_paramdict(params)
         return fcn(paramdict)
 
-    # TODO: activate if https://github.com/jiangyi15/tf-pwa/pull/153 is merged
-    # @z.function(wraps="loss")
+    @z.function(wraps="loss")
     def eval_grad(params):
         return fcn.nll_grad(params)[1]
 

tests/tfpwa/test_basic_example_tfpwa.py

@@ -67,6 +67,8 @@ def test_example1_tfpwa():
         fit_result = config.fit(method="BFGS")
 
         kwargs = dict(gradient='zfit', tol=0.01)
+        # kwargs = dict(gradient=False, tol=0.01)
+        # kwargs = dict(tol=0.01)
         assert pytest.approx(nll.value(), 0.001) == initial_val
         v, g, h = fcn.nll_grad_hessian()
         vz, gz, hz = nll.value_gradient_hessian()
@@ -79,7 +81,7 @@ def test_example1_tfpwa():
         np.testing.assert_allclose(g, gz1, atol=0.001)
 
         minimizer = zfit.minimize.Minuit(verbosity=7, **kwargs)
-        # minimizer = zfit.minimize.ScipyBFGS(verbosity=7, **kwargs)  # performs bestamba
+        # minimizer = zfit.minimize.ScipyBFGS(verbosity=7, **kwargs)  # performs best
         # minimizer = zfit.minimize.NLoptMMAV1(verbosity=7, **kwargs)
         # minimizer = zfit.minimize.ScipyLBFGSBV1(verbosity=7, **kwargs)
         # minimizer = zfit.minimize.NLoptLBFGSV1(verbosity=7, **kwargs)