CPU backend now tests if the data type of array parameters is correct

cpu/cpujit.py

@@ -248,7 +248,7 @@ template_extract_array = """
 PyObject * obj_{name} = PyDict_GetItemString(kwargs, "{name}");
 if( obj_{name} == NULL) {{  PyErr_SetString(PyExc_TypeError, "Keyword argument '{name}' missing"); return NULL; }};
 Py_buffer buffer_{name};
-int buffer_{name}_res = PyObject_GetBuffer(obj_{name}, &buffer_{name}, PyBUF_STRIDES | PyBUF_WRITABLE);
+int buffer_{name}_res = PyObject_GetBuffer(obj_{name}, &buffer_{name}, PyBUF_STRIDES | PyBUF_WRITABLE | PyBUF_FORMAT);
 if (buffer_{name}_res == -1) {{ return NULL; }}
 """
 
@@ -333,26 +333,38 @@ def create_function_boilerplate_code(parameter_info, name, insert_checks=True):
             post_call_code += template_release_buffer.format(name=field.name)
             parameters.append("({dtype} *)buffer_{name}.buf".format(dtype=str(field.dtype), name=field.name))
 
-            if insert_checks and field.has_fixed_shape:
-                shape_cond = ["buffer_{name}.shape[{i}] == {s}".format(s=s, name=field.name, i=i)
-                              for i, s in enumerate(field.spatial_shape)]
-                shape_cond = " && ".join(shape_cond)
-                pre_call_code += template_check_array.format(cond=shape_cond, what="shape", name=field.name,
-                                                             expected=str(field.shape))
-
-                item_size = field.dtype.numpy_dtype.itemsize
-                expected_strides = [e * item_size for e in field.spatial_strides]
-                stride_check_code = "(buffer_{name}.strides[{i}] == {s} || buffer_{name}.shape[{i}]<=1)"
-                strides_cond = " && ".join([stride_check_code.format(s=s, i=i, name=field.name)
-                                            for i, s in enumerate(expected_strides)])
-                pre_call_code += template_check_array.format(cond=strides_cond, what="strides", name=field.name,
-                                                             expected=str(expected_strides))
-
-            if insert_checks and not field.has_fixed_shape:
-                if FieldType.is_generic(field):
-                    variable_sized_normal_fields.add(field)
-                elif FieldType.is_indexed(field):
-                    variable_sized_index_fields.add(field)
+            if insert_checks:
+                np_dtype = field.dtype.numpy_dtype
+                item_size = np_dtype.itemsize
+
+                if np_dtype.isbuiltin and FieldType.is_generic(field):
+                    dtype_cond = "buffer_{name}.format[0] == '{format}'".format(name=field.name,
+                                                                                format=field.dtype.numpy_dtype.char)
+                    pre_call_code += template_check_array.format(cond=dtype_cond, what="data type", name=field.name,
+                                                                 expected=str(field.dtype.numpy_dtype))
+
+                item_size_cond = "buffer_{name}.itemsize == {size}".format(name=field.name, size=item_size)
+                pre_call_code += template_check_array.format(cond=item_size_cond, what="itemsize", name=field.name,
+                                                             expected=item_size)
+
+                if field.has_fixed_shape:
+                    shape_cond = ["buffer_{name}.shape[{i}] == {s}".format(s=s, name=field.name, i=i)
+                                  for i, s in enumerate(field.spatial_shape)]
+                    shape_cond = " && ".join(shape_cond)
+                    pre_call_code += template_check_array.format(cond=shape_cond, what="shape", name=field.name,
+                                                                 expected=str(field.shape))
+
+                    expected_strides = [e * item_size for e in field.spatial_strides]
+                    stride_check_code = "(buffer_{name}.strides[{i}] == {s} || buffer_{name}.shape[{i}]<=1)"
+                    strides_cond = " && ".join([stride_check_code.format(s=s, i=i, name=field.name)
+                                                for i, s in enumerate(expected_strides)])
+                    pre_call_code += template_check_array.format(cond=strides_cond, what="strides", name=field.name,
+                                                                 expected=str(expected_strides))
+                else:
+                    if FieldType.is_generic(field):
+                        variable_sized_normal_fields.add(field)
+                    elif FieldType.is_indexed(field):
+                        variable_sized_index_fields.add(field)
         elif param.is_field_stride:
             field = param.fields[0]
             item_size = field.dtype.numpy_dtype.itemsize