merged

sandbox/wraplinker.py

+from __future__ import absolute_import
+import numpy
+
+from ..gof.link import WrapLinker
+from ..gradient import numeric_grad
+
+class Todo(Exception): """todo"""
+
+#WrapLinker wrappers
+def cmp_outputs(i, node, *thunks):
+    """WrapLinker wrapper: raise an exception if outputs are different
+
+    numpy.ndarrays of floating point types are compared approximately, rather
+    than exactly.
+
+    """
+    class MisMatch(Exception): """Output mismatch"""
+
+    #define a comparison function, which works for all the results in a graph
+    #TODO: consider factoring this out (and maybe passing args explicitly
+    # instead of by closure)
+    def my_check_equal(x, y):
+        x, y = x[0], y[0]
+        if type(x) != type(y):
+            raise MisMatch("Output type mismatch", (x, y))
+
+        if hasattr(x, 'dtype'):
+            # was: isinstance(x,numpy.ndarray), which doesn't
+            # catch numpy.float64
+            if x.dtype != y.dtype or x.shape != y.shape:
+                raise MisMatch("ndarray type/shape.", (x,y))
+
+            if str(x.dtype).startswith('float'):
+                assert str(x.dtype) == 'float64' #otherwise we need to adjust
+                #our constant below... but to what?
+                abs_rel_err = numeric_grad.abs_rel_err(x, y)
+                max_abs_rel_err = numpy.max(abs_rel_err)
+                if max_abs_rel_err > 1.0e-7:
+                    raise MisMatch('max_abs_rel_err exceeds tolerence', (max_abs_rel_err,
+                        x, y))
+
+            elif str(x.dtype).startswith('complex'):
+                raise Todo()
+
+            else:
+                if not numpy.all(x==y):
+                    raise MisMatch
+
+        else:
+            print 'wtf??', type(x), type(y), node.op
+            if x != y:
+                print 'wow!! wtf??'
+                raise MisMatch("Output mismatch.", (x, y))
+
+    #loop over all the thunks
+    # ensure that the outputs from the first thunk match the outputs from
+    # all subsequent thunks
+    n_thunks = len(thunks)
+    if n_thunks > 1:
+        th0 = thunks[0]
+        for th in thunks[1:]:
+            for out0, outN in zip(th0.outputs, th.outputs):
+                my_check_equal(out0[0], outN[0])
+
+def numpy_wrapper(f):
+    def wrapper(i, node, *thunks):
+        """WrapLinker wrapper: raise an exception if a NaN is found in outputs
+        """
+        for thunk in thunks:
+            for output in thunk.outputs:
+                if hasattr(output, 'dtype'):
+                    if f(output)):
+                        raise Exception('uh oh', (thunk, output))
+    return wrapper
+
+numpy_any_isinf = numpy_wrapper(lambda a:numpy.any(numpy.isinf(a)))
+numpy_any_isnan = numpy_wrapper(lambda a:numpy.any(numpy.isnan(a)))
+numpy_all_isfinite = numpy_wrapper(lambda a:numpy.all(numpy.isfinite(a)))
+
+def run_all(i, node, *thunks):
+    """WrapLinker wrapper: run the thunks
+    """
+    for th in thunks:
+        th()
+
+
+
+def WrapLinkerMany(linkers, wrappers):
+    """ Variant on WrapLinker that runs a series of wrapper functions instead of
+    just one.
+    """
+    def wrapper(*args):
+        for f in wrappers:
+            f(*args)
+    return WrapLinker(linkers, wrapper)
+
+
+def DualLinker(linkers):
+    return WrapLinkerMany(linkers, [run_all, cmp_outputs])
+