Check that array size doesn't overflow at construction time

Now, requesting to allocate an array that is too big gives the exception
'array is too big', like numpy.

This does depend on a gcc extension, `__builtin_mul_overflow`, present
since at least version 5. This extension is also supported in clang.
msvc is probably the only compiler of note that does not support it.

Closes: #576

Author: jepler

code/ndarray.c

@@ -593,6 +593,13 @@ bool ndarray_is_dense(ndarray_obj_t *ndarray) {
     return stride == ndarray->strides[ULAB_MAX_DIMS-ndarray->ndim] ? true : false;
 }
 
+static size_t multiply_size(size_t a, size_t b) {
+    size_t result;
+    if (__builtin_mul_overflow(a, b, &result)) {
+            mp_raise_ValueError(translate("array is too big"));
+    }
+    return result;
+}
 
 ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides, uint8_t dtype) {
     // Creates the base ndarray with shape, and initialises the values to straight 0s
@@ -612,11 +619,11 @@ ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides
     for(uint8_t i=ULAB_MAX_DIMS; i > ULAB_MAX_DIMS-ndim; i--) {
         ndarray->shape[i-1] = shape[i-1];
         ndarray->strides[i-1] = _strides[i-1];
-        ndarray->len *= shape[i-1];
+        ndarray->len = multiply_size(ndarray->len, shape[i-1]);
     }
 
     // if the length is 0, still allocate a single item, so that contractions can be handled
-    size_t len = ndarray->itemsize * MAX(1, ndarray->len);
+    size_t len = multiply_size(ndarray->itemsize, MAX(1, ndarray->len));
     uint8_t *array = m_new0(byte, len);
     // this should set all elements to 0, irrespective of the of the dtype (all bits are zero)
     // we could, perhaps, leave this step out, and initialise the array only, when needed

tests/2d/numpy/zeros.py

@@ -11,3 +11,18 @@
 for dtype in dtypes:
     print(np.zeros((3,3), dtype=dtype))
     print(np.zeros((4,2), dtype=dtype))
+
+try:
+    np.zeros((1<<31, 1<<31))
+except ValueError:
+    print("ValueError")
+
+try:
+    np.zeros((2147483653, 2147483649))
+except ValueError:
+    print("ValueError")
+
+try:
+    np.zeros((194899806, 189294637612))
+except ValueError:
+    print("ValueError")

tests/2d/numpy/zeros.py.exp

@@ -37,3 +37,6 @@ array([[0.0, 0.0],
        [0.0, 0.0],
        [0.0, 0.0],
        [0.0, 0.0]], dtype=float64)
+ValueError
+ValueError
+ValueError