fixed Split.__hash__, use default_mode for verify_grad in tensor/_test_basic

tensor/_test_basic.py

@@ -28,6 +28,13 @@ def eval_outputs(outputs, mode = default_mode):
         return results[0]
     return results
 
+_public_verify_grad = verify_grad
+def verify_grad(*args, **kwargs):
+    assert 'mode' not in kwargs
+    kwargs['mode'] = default_mode
+    r =  _public_verify_grad(*args, **kwargs)
+    return r
+
 
 def _numpy_checker(x, y):
     """
@@ -1774,14 +1781,14 @@ class T_op_cache(unittest.TestCase):
         self.failUnless(numpy.all(fn_py(a) == fn_c_or_py(a)))
 
 if __name__ == '__main__':
-    if 1:
     if len(sys.argv) >= 2 and sys.argv[1] == 'OPT':
         default_mode = compile.Mode(linker = 'c&py',
                                     optimizer = 'math')
         sys.argv[1:] = sys.argv[2:]
+    if 1:
         unittest.main()
     else:
-        testcase =  DotTester
+        testcase =  T_Join_and_Split
 
         suite = unittest.TestLoader()
         suite = suite.loadTestsFromTestCase(testcase)

tensor/basic.py

@@ -1394,7 +1394,7 @@ class Split(Op):
                 self.len_splits == other.len_splits)
 
     def __hash__(self):
-        return 76612131345 ^ len(self.len_splits)
+        return hash(Split) ^ self.len_splits
  
     def make_node(self, x, axis, splits):
         """WRITEME"""
@@ -2161,7 +2161,7 @@ class numeric_grad:
         return numpy.max(errs)
 
 def verify_grad(testcase, op, pt, n_tests=1, rng=numpy.random, eps=1.0e-7, tol=0.0001,
-        linker='c&py'):
+        mode=compile.Mode(optimizer=None, linker='c&py')):
     """ WRITEME
     
     testcase.failUnless(analytic gradient matches finite-diff gradient)
@@ -2172,9 +2172,8 @@ def verify_grad(testcase, op, pt, n_tests=1, rng=numpy.random, eps=1.0e-7, tol=0
     #print "PT", pt
 
     def function(inputs, output):
-        return compile.function(inputs, output, 
-                mode=compile.Mode(optimizer = None, linker = linker), 
-                accept_inplace=True)
+        f = compile.function(inputs, output, mode=mode, accept_inplace=True)
+        return f
 
     for test_num in xrange(n_tests):
         tensor_pt = [value(p.copy(), name='input %i'%i) for i,p in enumerate(pt)]