Remove unused variables from function's input

theano/compile/tests/test_pfunc.py

@@ -872,15 +872,12 @@ class Test_aliasing_rules(unittest.TestCase):
         orig_b = numpy.zeros((2,2))-.5
         A = self.shared(orig_a)
         B = self.shared(orig_b)
-        C = tensor.dmatrix()
-
-        z = numpy.zeros((2,2))
 
         data_of_a = data_of(A)
         data_of_b = data_of(B)
 
-        f = pfunc([C], [], updates=[(A,B),(B,A)])
-        f(z)
+        f = pfunc([], [], updates=[(A,B),(B,A)])
+        f()
         # correctness
         assert numpy.all(data_of(A) == -.5)
         assert numpy.all(data_of(B) == +.5)
@@ -902,16 +899,13 @@ class Test_aliasing_rules(unittest.TestCase):
         orig_b = numpy.zeros((2,2))-.5
         A = self.shared(orig_a)
         B = self.shared(orig_b)
-        C = tensor.dmatrix()
-
-        z = numpy.zeros((2,2))
 
         data_of_a = data_of(A)
         data_of_b = data_of(B)
 
-        f = pfunc([C], [], updates=[(A,B[:,::-1]),(B,A.T)])
+        f = pfunc([], [], updates=[(A,B[:,::-1]),(B,A.T)])
         theano.printing.debugprint(f)
-        f(z)
+        f()
         # correctness (doesn't actually test the view...)
         assert numpy.all(data_of(A) == -.5)
         assert numpy.all(data_of(B) == +.5)

theano/sandbox/cuda/tests/test_nnet.py

@@ -57,9 +57,9 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
     y_pred = T.argmax(p_y_given_x, axis=-1)
     loss = -T.mean(T.log(p_y_given_x)[T.arange(y.shape[0]), y])
     dW = T.grad(loss, dot_result)
-    classify = theano.function( inputs = [x,y,b,dot_result], outputs = [loss,y_pred,dW],
+    classify = theano.function( inputs = [y,b,dot_result], outputs = [loss,y_pred,dW],
                                 mode = mode_without_gpu)
-    classify_gpu = theano.function( inputs = [x,y,b,dot_result], outputs = [loss,y_pred,dW],
+    classify_gpu = theano.function( inputs = [y,b,dot_result], outputs = [loss,y_pred,dW],
                                     mode = mode_with_gpu)
     #theano.printing.debugprint(classify)
     #theano.printing.debugprint(classify_gpu)
@@ -67,8 +67,8 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
     assert any([isinstance(node.op,T.nnet.CrossentropySoftmaxArgmax1HotWithBias) for node in classify.maker.env.toposort()])
     assert any([isinstance(node.op,cuda.nnet.GpuCrossentropySoftmaxArgmax1HotWithBias) for node in classify_gpu.maker.env.toposort()])
 
-    out=classify(xx,yy,b_values,dot_value)
-    gout=classify_gpu(xx,yy,b_values,dot_value)
+    out=classify(yy,b_values,dot_value)
+    gout=classify_gpu(yy,b_values,dot_value)
 
     assert len(out)==len(gout)==3
     assert numpy.allclose(out[0],gout[0])

theano/sandbox/cuda/tests/test_opt.py

@@ -165,7 +165,7 @@ def test_huge_elemwise_fusion():
     """
     shape = (2,3,4,5,6)
     ttype = tensor.tensor(dtype='float32',broadcastable=(False,)*len(shape))
-    vars = [tensor.tanh(ttype) for x in range(10)]
+    vars = [tensor.tanh(ttype) for x in range(7)]
     f = pfunc(vars, [vars[0]-vars[1]-vars[2]-vars[3]-vars[4]-vars[5]-vars[6]], mode=mode_with_gpu)
     topo = f.maker.env.toposort()
     #theano.printing.debugprint(f)
@@ -177,14 +177,14 @@ def test_huge_elemwise_fusion():
     assert isinstance(topo[8].op.scalar_op,theano.scalar.basic.Composite)
     #let debugmode catch errors
     gen = lambda : theano._asarray(numpy.random.rand(*shape), dtype='float32')
-    f(gen(),gen(),gen(),gen(),gen(),gen(),gen(),gen(),gen(),gen())
+    f(gen(),gen(),gen(),gen(),gen(),gen(),gen())
 
     # Test the case where we can't put the computation on the gpu! their is too many
     # dimensions to the input to have 2 inputs to the op!
 
     shape = (1,2,3,4,5,6,7,2,2,3,2,1,2,2,2,)
     ttype = tensor.tensor(dtype='float32',broadcastable=(False,)*len(shape))
-    vars = [tensor.tanh(ttype) for x in range(10)]
+    vars = [tensor.tanh(ttype) for x in range(7)]
     f = pfunc(vars, [vars[0]-vars[1]-vars[2]-vars[3]-vars[4]-vars[5]-vars[6]], mode=mode_with_gpu)
     topo = f.maker.env.toposort()
     #theano.printing.debugprint(f)
@@ -193,7 +193,7 @@ def test_huge_elemwise_fusion():
     assert sum([isinstance(node.op, tensor.Elemwise) for node in topo]) == 1
     #let debugmode catch errors
     gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
-    f(gen(), gen(), gen(), gen(), gen(), gen(), gen(), gen(), gen(), gen())
+    f(gen(), gen(), gen(), gen(), gen(), gen(), gen())
 
     def gen(shape):
         return theano._asarray(numpy.random.rand(*shape), dtype='float32')

theano/sandbox/cuda/tests/test_var.py

@@ -50,7 +50,7 @@ def test_givens():
     data = numpy.float32([1,2,3,4])
     x = f32sc(data)
     y = x**2
-    f = theano.function([x], y, givens={x:x+1})
+    f = theano.function([], y, givens={x:x+1})
 
 class T_updates(unittest.TestCase):
     # Test that you can use a TensorType expression to update a

theano/scan_module/tests/test_scan.py

@@ -2222,7 +2222,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x])
         sy, upy = theano.scan(sum, sequences=[x])
 
-        f = theano.function([x, y], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x], [sx, sy], mode=mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n:
                        isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2231,7 +2231,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x])
         sy, upy = theano.scan(sum, sequences=[x], mode='FAST_COMPILE')
 
-        f = theano.function([x, y], [sx, sy],
+        f = theano.function([x], [sx, sy],
                             mode=mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n:
@@ -2241,7 +2241,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x])
         sy, upy = theano.scan(sum, sequences=[x], truncate_gradient=1)
 
-        f = theano.function([x, y], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x], [sx, sy], mode=mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n:
                        isinstance(n.op, theano.scan_module.scan_op.Scan), topo)

theano/tensor/tests/test_basic.py

@@ -3553,22 +3553,16 @@ class T_divimpl(unittest.TestCase):
         f = fscalar()
         c = cscalar()
 
-        assert numpy.allclose(function([i, ii, d, f, c], i/d)(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (5.0/7.0))
-        assert numpy.allclose(function([i, ii, d, f, c], d/i)(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (7.0/5.0))
-        assert numpy.allclose(function([i, ii, d, f, c], i/f)(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (5.0/11.0))
-        assert numpy.allclose(function([i, ii, d, f, c], f/i)(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (11.0/5.0))
-        assert numpy.allclose(function([i, ii, d, f, c], i//ii)(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (5/3))
-        assert numpy.allclose(function([i, ii, d, f, c], ii//i)(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (3/5))
-        assert numpy.allclose(function([i, ii, d, f, c], true_div(i,ii))(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (5./3.))
-        assert numpy.allclose(function([i, ii, d, f, c], true_div(ii,i))(5, 3, 7.0, 11.0, numpy.complex(5,3)),
-                (3./5.))
+        assert numpy.allclose(function([i, d], i / d)(5, 7.0), (5.0 / 7.0))
+        assert numpy.allclose(function([i, d], d / i)(5, 7.0), (7.0 / 5.0))
+        assert numpy.allclose(function([i, f], i / f)(5, 11.0), (5.0 / 11.0))
+        assert numpy.allclose(function([i, f], f / i)(5, 11.0), (11.0 / 5.0))
+        assert numpy.allclose(function([i, ii], i // ii)(5, 3), (5 / 3))
+        assert numpy.allclose(function([i, ii], ii // i)(5, 3), (3 / 5))
+        assert numpy.allclose(function([i, ii], true_div(i, ii))(5, 3),
+                (5. / 3.))
+        assert numpy.allclose(function([i, ii], true_div(ii, i))(5, 3),
+                (3. / 5.))
 
 
 class T_mean(unittest.TestCase):

theano/tensor/tests/test_blas.py

@@ -660,7 +660,7 @@ def test_inplace0():
     X,Y,Z,a,b = T.dmatrix('X'), T.dmatrix('Y'), T.dmatrix('Z'), T.dscalar('a'), T.dscalar('b')
     R, S, c = T.dmatrix('R'), T.dmatrix('S'), T.dscalar('c')
 
-    f = inplace_func([X,Y,Z,a,b, R, S, c],
+    f = inplace_func([Z, b, R, S],
             [Z * (Z + b * T.dot(R,S).T)], mode='FAST_RUN')
     if (gemm_inplace in [n.op for n in f.maker.env.nodes]):
         print pp(f.maker.env.outputs[0])
@@ -678,7 +678,7 @@ def test_inplace0():
 def test_inplace1():
     X,Y,Z,a,b = XYZab()
     # with > 2 terms in the overall addition
-    f = inplace_func([X,Y,Z,a,b],
+    f = inplace_func([X, Y, Z],
             [Z + Z + T.dot(X,Y)], mode='FAST_RUN')
     theano.printing.debugprint(f)
     # it doesn't work inplace because we didn't mark Z as mutable input

theano/tensor/tests/test_opt.py

@@ -713,14 +713,14 @@ def test_local_merge_abs():
     mode = theano.compile.mode.get_mode(mode).excluding(
                                                     "local_elemwise_fusion")
 
-    f = theano.function([x, y, z], (abs(y * z * -2)), mode=mode)
-    f(x_val, y_val, z_val)
+    f = theano.function([y, z], (abs(y * z * -2)), mode=mode)
+    f(y_val, z_val)
     theano.printing.debugprint(f)
     assert isinstance(f.maker.env.toposort()[1].op.scalar_op, scal.Abs)
     assert len(f.maker.env.toposort()) == 2
 
-    f = theano.function([x, y, z],abs(x / y), mode=mode)
-    f(x_val, y_val, z_val)
+    f = theano.function([x, y],abs(x / y), mode=mode)
+    f(x_val, y_val)
     theano.printing.debugprint(f)
     assert isinstance(f.maker.env.toposort()[1].op.scalar_op, scal.Abs)
     assert len(f.maker.env.toposort())==2
@@ -2214,8 +2214,7 @@ class test_shapeoptimizer(unittest.TestCase):
             mode = 'FAST_RUN'
 
         v = T.vector()
-        m = T.matrix()
-        f = function([v,m], v.dimshuffle('x','x',0).shape[1], mode=mode)
+        f = function([v], v.dimshuffle('x','x',0).shape[1], mode=mode)
         topo = f.maker.env.toposort()
         assert len(topo) == 1
         assert topo[0].op == theano.compile.function_module.deep_copy_op
@@ -2371,34 +2370,34 @@ def test_local_mul_specialize():
     v = T.vector()
     m = T.vector()
 
-    f = function([v,m], v*1, mode=mode)
+    f = function([v], v*1, mode=mode)
     nodes = [node.op for node in f.maker.env.toposort()]
     print nodes
     nodes == [theano.compile.function_module.deep_copy_op]
 
-    f = function([v,m], v*0, mode=mode)
+    f = function([v], v*0, mode=mode)
     nodes = [node.op for node in f.maker.env.toposort()]
     print nodes
     assert nodes == [Shape_i(0), T.alloc]
 
-    f = function([v,m], v*(-1), mode=mode)
+    f = function([v], v*(-1), mode=mode)
     nodes = [node.op for node in f.maker.env.toposort()]
     print nodes
     assert nodes == [T.neg]
 
-    f = function([v,m], v*1*(-m), mode=mode)
+    f = function([v, m], v*1*(-m), mode=mode)
     nodes = [node.op for node in f.maker.env.toposort()]
     print nodes
     theano.printing.debugprint(f)
     assert nodes == [T.mul, inplace.neg_inplace]
 
-    f = function([v,m], v*0*(-m), mode=mode)
+    f = function([v, m], v*0*(-m), mode=mode)
     nodes = [node.op for node in f.maker.env.toposort()]
     print nodes
     theano.printing.debugprint(f)
     assert nodes == [Shape_i(0), T.alloc]
 
-    f = function([v,m], v*(-1)*(-m), mode=mode)
+    f = function([v, m], v*(-1)*(-m), mode=mode)
     nodes = [node.op for node in f.maker.env.toposort()]
     print nodes
     theano.printing.debugprint(f)