remove multiple exit from prange (#1901)

* remove multiple exit from prange
* closes #1900 
* skips computation of KL divergence if inf was already computed add unit test
Signed-off-by: Edoardo Pasca <[email protected]>

CHANGELOG.md

@@ -1,6 +1,8 @@
 * 24.x.x
 - Bug fixes:
   - Fix bug with 'median' and 'mean' methods in Masker averaging over the wrong axes.
+- Enhancements:
+  - Removed multiple exits from numba implementation of KullbackLeibler divergence (#1901)
 
 * 24.2.0
   - New Features:
@@ -21,7 +23,7 @@
     - Internal refactor: Separate framework into multiple files (#1692)
     - Allow the SIRT algorithm to take `initial=None` (#1906)
     - Add checks on equality method of `AcquisitionData` and `ImageData` for equality of data type and geometry (#1919)
-    - Add check on equality method of `AcquisitionGeometry` for equality of dimension labels (#1919)    
+    - Add check on equality method of `AcquisitionGeometry` for equality of dimension labels (#1919)
   - Testing:
     - New unit tests for operators and functions to check for in place errors and the behaviour of `out` (#1805)
     - Updates in SPDHG vs PDHG unit test to reduce test time and adjustments to parameters (#1898)

Wrappers/Python/cil/optimisation/functions/KullbackLeibler.py

@@ -343,25 +343,9 @@ def kl_gradient_mask(x, b, out, eta, mask):
     @njit(parallel=True)
     def kl_div(x, y, eta):
         accumulator = numpy.zeros(get_num_threads(), dtype=numpy.float64)
+        has_inf = 0
         for i in prange(x.size):
-            X = x.flat[i]
-            Y = y.flat[i] + eta.flat[i]
-            if X > 0 and Y > 0:
-                # out.flat[i] = X * numpy.log(X/Y) - X + Y
-                accumulator[get_thread_id()] += X * numpy.log(X/Y) - X + Y
-            elif X == 0 and Y >= 0:
-                # out.flat[i] = Y
-                accumulator[get_thread_id()] += Y
-            else:
-                # out.flat[i] = numpy.inf
-                return numpy.inf
-        return sum(accumulator)
-
-    @njit(parallel=True)
-    def kl_div_mask(x, y, eta, mask):
-        accumulator = numpy.zeros(get_num_threads(), dtype=numpy.float64)
-        for i in prange(x.size):
-            if mask.flat[i] > 0:
+            if has_inf == 0:
                 X = x.flat[i]
                 Y = y.flat[i] + eta.flat[i]
                 if X > 0 and Y > 0:
@@ -372,7 +356,29 @@ def kl_div_mask(x, y, eta, mask):
                     accumulator[get_thread_id()] += Y
                 else:
                     # out.flat[i] = numpy.inf
-                    return numpy.inf
+                    accumulator[get_thread_id()] = numpy.inf
+                    has_inf = 1
+        return sum(accumulator)
+
+    @njit(parallel=True)
+    def kl_div_mask(x, y, eta, mask):
+        accumulator = numpy.zeros(get_num_threads(), dtype=numpy.float64)
+        has_inf = 0
+        for i in prange(x.size):
+            if has_inf == 0:
+                if mask.flat[i] > 0:
+                    X = x.flat[i]
+                    Y = y.flat[i] + eta.flat[i]
+                    if X > 0 and Y > 0:
+                        # out.flat[i] = X * numpy.log(X/Y) - X + Y
+                        accumulator[get_thread_id()] += X * numpy.log(X/Y) - X + Y
+                    elif X == 0 and Y >= 0:
+                        # out.flat[i] = Y
+                        accumulator[get_thread_id()] += Y
+                    else:
+                        # out.flat[i] = numpy.inf
+                        accumulator[get_thread_id()] = numpy.inf
+                        has_inf = 1
         return sum(accumulator)
 
     # convex conjugate

Wrappers/Python/test/test_function_KullbackLeibler.py

@@ -173,6 +173,22 @@ def test_KullbackLeibler_numba_call(self):
 
         numpy.testing.assert_allclose(f(u1), f_np(u1),  rtol=1e-5)
 
+    def test_KullbackLeibler_numba_kl_div_has_inf(self):
+        x = self.u1 * self.mask
+        x *= -1
+
+        from cil.optimisation.functions.KullbackLeibler import kl_div
+
+        numpy.testing.assert_equal(kl_div(x.array, self.b1.array, self.eta.array), numpy.inf)
+
+    def test_KullbackLeibler_numba_kl_div_mask_has_inf(self):
+        x = self.u1 * self.mask
+        x *= -1
+
+        from cil.optimisation.functions.KullbackLeibler import kl_div_mask
+
+        numpy.testing.assert_equal(kl_div_mask(x.array, self.b1.array, self.eta.array, self.mask.array), numpy.inf)
+
     def test_KullbackLeibler_numba_call_mask(self):
         f = self.f
         f_np = self.f_np