[Frontend] Return a scalar if all input args are scalar (#816)

python/test/unit/language/test_core.py

@@ -1546,7 +1546,7 @@ def test_num_warps_pow2():
                          [('int32', 'libdevice.ffs', ''),
                           ('float32', 'libdevice.pow', '/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'),
                           ('float64', 'libdevice.norm4d', '')])
-def test_libdevice(dtype_str, expr, lib_path):
+def test_libdevice_tensor(dtype_str, expr, lib_path):
 
     @triton.jit
     def kernel(X, Y, BLOCK: tl.constexpr):
@@ -1582,3 +1582,32 @@ def test_libdevice(dtype_str, expr, lib_path):
         np.testing.assert_equal(y_ref, to_numpy(y_tri))
     else:
         np.testing.assert_allclose(y_ref, to_numpy(y_tri), rtol=0.01)
+
+
+@pytest.mark.parametrize("dtype_str, expr, lib_path",
+                         [('float32', 'libdevice.pow', '')])
+def test_libdevice_scalar(dtype_str, expr, lib_path):
+
+    @triton.jit
+    def kernel(X, Y, BLOCK: tl.constexpr):
+        x = X
+        y = GENERATE_TEST_HERE
+        tl.store(Y + tl.arange(0, BLOCK), y)
+
+    shape = (128, )
+    rs = RandomState(17)
+    # limit the range of integers so that the sum does not overflow
+    x = numpy_random((1,), dtype_str=dtype_str, rs=rs)
+    y_ref = np.zeros(shape, dtype=x.dtype)
+
+    # numpy does not allow negative factors in power, so we use abs()
+    x = np.abs(x)
+    kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.libdevice.pow(x, x)'})
+    y_ref[:] = np.power(x, x)
+
+    # triton result
+    x_tri = to_triton(x)[0].item()
+    y_tri = to_triton(numpy_random((shape[0],), dtype_str=dtype_str, rs=rs), device='cuda')
+    kernel[(1,)](x_tri, y_tri, BLOCK=shape[0], extern_libs={'libdevice': lib_path})
+    # compare
+    np.testing.assert_allclose(y_ref, to_numpy(y_tri), rtol=0.01)

python/triton/language/extern.py

@@ -59,6 +59,12 @@ def elementwise(lib_name: str, lib_path: str, args: list, arg_type_symbol_dict:
         :return: the return value of the function
     '''
     dispatch_args = args.copy()
+    all_scalar = True
+    ret_shape = None
+    for dispatch_arg in dispatch_args:
+        if dispatch_arg.type.is_block():
+            all_scalar = False
+    if not all_scalar:
         if len(args) == 1:
             dispatch_args[0] = core._to_tensor(dispatch_args[0], _builder)
             ret_shape = dispatch_args[0].shape