Add a COp test in test_wrapper.

theano/gof/op.py

@@ -799,22 +799,15 @@ class Op(utils.object2, PureOp, CLinkerOp):
     # We add a default get_params() implementation which will try to detect params from the op
     # if params_type is set to a Wrapper. If not, we raise a MethodNotDefined exception.
     def get_params(self, node):
-        if hasattr(self, 'params_type'):
-            # If params_type is a Wrapper, we try to extract params from the op.
-            if isinstance(self.params_type, theano.gof.wrapper.Wrapper):
-                wrapper = self.params_type
-                op_has_wrap_attributes = True
-                for field in wrapper.fields:
-                    if not hasattr(self, field):
-                        op_has_wrap_attributes = False
-                        break
-                if op_has_wrap_attributes:
-                    wrap_dict = dict()
-                    for i in range(wrapper.length):
-                        field = wrapper.fields[i]
-                        _type = wrapper.types[i]
-                        wrap_dict[field] = _type.filter(getattr(self, field), strict=False, allow_downcast=True)
-                    return theano.gof.wrapper.Wrap(**wrap_dict)
+        if hasattr(self, 'params_type') and isinstance(self.params_type, theano.gof.wrapper.Wrapper):
+            wrapper = self.params_type
+            if hasattr(self, '__props__') and all(field in self.__props__ for field in wrapper.fields):
+                wrap_dict = dict()
+                for i in range(wrapper.length):
+                    field = wrapper.fields[i]
+                    _type = wrapper.types[i]
+                    wrap_dict[field] = _type.filter(getattr(self, field), strict=False, allow_downcast=True)
+                return theano.gof.wrapper.Wrap(wrapper, **wrap_dict)
         raise theano.gof.utils.MethodNotDefined('get_params')
 
     def prepare_node(self, node, storage_map, compute_map, impl):

theano/gof/tests/test_quadratic_function.c

+#section support_code_apply
+int APPLY_SPECIFIC(quadratic_function)(PyArrayObject* tensor, DTYPE_INPUT_0 a, DTYPE_INPUT_0 b, DTYPE_INPUT_0 c) {
+    NpyIter* iterator = NpyIter_New(tensor,
+        NPY_ITER_READWRITE | NPY_ITER_EXTERNAL_LOOP | NPY_ITER_REFS_OK,
+        NPY_KEEPORDER, NPY_NO_CASTING, NULL);
+    if(iterator == NULL) {
+        PyErr_SetString(PyExc_RuntimeError, "Unable to iterate over a tensor for an elemwise operation.");
+        return -1;
+    }
+    NpyIter_IterNextFunc* get_next = NpyIter_GetIterNext(iterator, NULL);
+    char** data_ptr = NpyIter_GetDataPtrArray(iterator);
+    npy_intp* stride_ptr = NpyIter_GetInnerStrideArray(iterator);
+    npy_intp* innersize_ptr = NpyIter_GetInnerLoopSizePtr(iterator);
+    do {
+        char* data = *data_ptr;
+        npy_intp stride = *stride_ptr;
+        npy_intp count = *innersize_ptr;
+        while(count) {
+            DTYPE_INPUT_0 x = *((DTYPE_INPUT_0*)data);
+            *((DTYPE_INPUT_0*)data) = a*x*x + b*x + c;
+            data += stride;
+            --count;
+        }
+    } while(get_next(iterator));
+    NpyIter_Deallocate(iterator);
+    return 0;
+}
+
+int APPLY_SPECIFIC(compute_quadratic)(PyArrayObject* X, PyArrayObject** Y, QUADRATIC_WRAPPER* coeff) {
+    DTYPE_INPUT_0 a = (DTYPE_INPUT_0) (*(COEFF_TYPE*) PyArray_GETPTR1(coeff->a, 0)); // 0-D TensorType.
+    DTYPE_INPUT_0 b =                                                 coeff->b;      // Scalar.
+    DTYPE_INPUT_0 c =                 (DTYPE_INPUT_0)PyFloat_AsDouble(coeff->c);     // Generic.
+    Py_XDECREF(*Y);
+    *Y = (PyArrayObject*)PyArray_EMPTY(PyArray_NDIM(X), PyArray_DIMS(X), TYPENUM_INPUT_0, PyArray_IS_F_CONTIGUOUS(X));
+    if (PyArray_CopyInto(*Y, X) != 0) {
+        PyErr_SetString(PyExc_RuntimeError, "Unable to copy input into output.");
+        return 1;
+    };
+    if (APPLY_SPECIFIC(quadratic_function)(*Y, a, b, c) != 0) {
+        PyErr_SetString(PyExc_RuntimeError, "Unable to compute quadratic function.");
+        return 1;
+    }
+    return 0;
+}

theano/gof/tests/test_wrapper.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 import theano
 import numpy
 from unittest import TestCase
-from theano.gof import Op, Apply
+from theano.gof import Op, COp, Apply
 from theano import Generic
 from theano.scalar import Scalar
 from theano.tensor import TensorType
@@ -18,7 +18,7 @@ generic_type = Generic()
 
 
 # A test op to compute `y = a*x^2 + bx + c` for any tensor x, with a, b, c as op params.
-class QuadraticFunction(Op):
+class QuadraticOpFunc(Op):
     __props__ = ('a', 'b', 'c')
     params_type = Wrapper(a=tensor_type_0d,
                           b=scalar_type,
@@ -39,7 +39,7 @@ class QuadraticFunction(Op):
         y[0] = coefficients.a * (x**2) + coefficients.b * x + coefficients.c
 
     def c_code_cache_version(self):
-        return (1, 2)
+        return (1, 3)
 
     def c_support_code_apply(self, node, name):
         float_type = node.inputs[0].type.dtype_specs()[1]
@@ -82,9 +82,9 @@ class QuadraticFunction(Op):
         float_typenum = numpy.dtype(node.inputs[0].type.dtype).num
         coeff_type = 'npy_' + numpy.dtype(dtype).name
         return """
-        %(float_type)s a = (%(float_type)s) (*(%(coeff_type)s*) PyArray_GETPTR1(%(coeff)s.a, 0)); // 0-D TensorType.
-        %(float_type)s b =                                                      %(coeff)s.b;      // Scalar.
-        %(float_type)s c =                     (%(float_type)s)PyFloat_AsDouble(%(coeff)s.c);     // Generic.
+        %(float_type)s a = (%(float_type)s) (*(%(coeff_type)s*) PyArray_GETPTR1(%(coeff)s->a, 0)); // 0-D TensorType.
+        %(float_type)s b =                                                      %(coeff)s->b;      // Scalar.
+        %(float_type)s c =                     (%(float_type)s)PyFloat_AsDouble(%(coeff)s->c);     // Generic.
         Py_XDECREF(%(Y)s);
         %(Y)s = (PyArrayObject*)PyArray_EMPTY(PyArray_NDIM(%(X)s), PyArray_DIMS(%(X)s), %(float_typenum)s, PyArray_IS_F_CONTIGUOUS(%(X)s));
         if (PyArray_CopyInto(%(Y)s, %(X)s) != 0) {
@@ -98,29 +98,54 @@ class QuadraticFunction(Op):
         """ % locals()
 
 
+# Same op as above, but implemented as a COp (with C code in an external file).
+class QuadraticCOpFunc(COp):
+    __props__ = ('a', 'b', 'c')
+    params_type = Wrapper(a=tensor_type_0d,
+                          b=scalar_type,
+                          c=generic_type)
+
+    def get_op_params(self):
+        return [('QUADRATIC_WRAPPER', self.params_type.name),
+                ('COEFF_TYPE', 'npy_' + numpy.dtype(dtype).name)]
+
+    def __init__(self, a, b, c):
+        super(QuadraticCOpFunc, self).__init__('test_quadratic_function.c',
+                                               'APPLY_SPECIFIC(compute_quadratic)')
+        self.a = a
+        self.b = b
+        self.c = c
+
+    def make_node(self, x):
+        x = tensor.as_tensor_variable(x)
+        return Apply(self, [x], [x.type()])
+
+
 class TestWrapper(TestCase):
 
-    def test_wrap_hash_and_eq(self):
-        w1 = Wrap(a=1, b='test string', array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
-        w2 = Wrap(a=1, b='test string', array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+    def test_hash_and_eq_wrap(self):
+        wp1 = Wrapper(a=Generic(), array=TensorType('int32', (False,)), floatting=Scalar('float32'),
+                      npy_scalar=TensorType('float64', tuple()))
+        wp2 = Wrapper(a=Generic(), array=TensorType('int32', (False,)), floatting=Scalar('float32'),
+                      npy_scalar=TensorType('float64', tuple()))
+        w1 = Wrap(wp1, a=1, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        w2 = Wrap(wp2, a=1, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
         assert w1 == w2
         assert not (w1 != w2)
         assert hash(w1) == hash(w2)
-        assert all(hasattr(w1, key) for key in ('a', 'b', 'array', 'floatting', 'npy_scalar'))
-        # Changing attributes names only.
-        w2 = Wrap(other_name=1, b='test string', array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
-        assert w1 != w2
-        # Changing attributes types only.
-        w2 = Wrap(a=1, b='test string', array=[1, 2, 4, 5, 7], floatting=-4.5, npy_scalar=numpy.asarray(12))
+        # Changing attributes names only (a -> other_name).
+        wp2_other = Wrapper(other_name=Generic(), array=TensorType('int32', (False,)), floatting=Scalar('float32'),
+                            npy_scalar=TensorType('float64', tuple()))
+        w2 = Wrap(wp2_other, other_name=1, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
         assert w1 != w2
-        # Changing attributes values only.
-        w2 = Wrap(a=1, b='string', array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        # Changing attributes values only (now a=2).
+        w2 = Wrap(wp2, a=2, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
         assert w1 != w2
-        # Changing NumPy array values.
-        w2 = Wrap(a=1, b='test string', array=numpy.asarray([1, 2, 4, -5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        # Changing NumPy array values (5 -> -5).
+        w2 = Wrap(wp2, a=1, array=numpy.asarray([1, 2, 4, -5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
         assert w1 != w2
 
-    def test_wrapper_hash_and_eq(self):
+    def test_hash_and_eq_wrapper(self):
         w1 = Wrapper(a1=TensorType('int64', (False, False)),
                      a2=TensorType('int64', (False, True, False, False, True)),
                      a3=Generic())
@@ -133,7 +158,7 @@ class TestWrapper(TestCase):
         assert w1.name == w2.name
         # Changing attributes names only.
         w2 = Wrapper(a1=TensorType('int64', (False, False)),
-                     other_name=TensorType('int64', (False, True, False, False, True)),
+                     other_name=TensorType('int64', (False, True, False, False, True)),  # a2 -> other_name
                      a3=Generic())
         assert w1 != w2
         # Changing attributes types only.
@@ -157,7 +182,8 @@ class TestWrapper(TestCase):
                     a3=Generic())
 
         # With a value that does not match the wrapper.
-        o = Wrap(a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int64'),
+        o = Wrap(w,
+                 a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int64'),
                  a2=random_tensor.astype('float32'),
                  a3=2000)
         # should fail (o.a1 is not int32, o.a2 is not float64)
@@ -168,7 +194,8 @@ class TestWrapper(TestCase):
         w.filter(o, strict=False, allow_downcast=True)
 
         # With a value that matches the wrapper.
-        o1 = Wrap(a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
+        o1 = Wrap(w,
+                  a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                   a2=random_tensor.astype('float64'),
                   a3=2000)
         # All should pass.
@@ -176,15 +203,17 @@ class TestWrapper(TestCase):
         w.filter(o1, strict=False, allow_downcast=False)
         w.filter(o1, strict=False, allow_downcast=True)
 
-        # Check value_eq and value_eq_approx.
-        o2 = Wrap(a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
+        # Check values_eq and values_eq_approx.
+        o2 = Wrap(w,
+                  a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                   a2=random_tensor.astype('float64'),
                   a3=2000)
         assert w.values_eq(o1, o2)
         assert w.values_eq_approx(o1, o2)
 
         # Check value_eq_approx.
-        o3 = Wrap(a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('float32'),
+        o3 = Wrap(w,
+                  a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('float32'),
                   a2=random_tensor.astype('float64'),
                   a3=2000.0)
         assert w.values_eq_approx(o1, o3)
@@ -192,14 +221,18 @@ class TestWrapper(TestCase):
     def test_op_params(self):
         a, b, c = 2, 3, -7
         x = tensor.matrix()
-        y = QuadraticFunction(a, b, c)(x)
-        f = theano.function([x], y)
+        y1 = QuadraticOpFunc(a, b, c)(x)
+        y2 = QuadraticCOpFunc(a, b, c)(x)
+        f1 = theano.function([x], y1)
+        f2 = theano.function([x], y2)
         shape = (100, 100)
         # The for-loop is here just to force profiling print something interesting.
         # When running this test without this loop, profiling does not print neither list of classes nor list of ops
         # (maybe because the function is extremely fast ?).
         for i in range(50):
             vx = numpy.random.normal(size=shape[0] * shape[1]).astype(dtype).reshape(*shape)
-            vy = f(vx)
+            vy1 = f1(vx)
+            vy2 = f2(vx)
             ref = a * (vx**2) + b * vx + c
-            utt.assert_allclose(ref, vy)
+            utt.assert_allclose(vy1, vy2)
+            utt.assert_allclose(ref, vy1)