[Triton-MLIR] [Frontend] Return a scalar if all input args are scalar (#839)

python/tests/test_core.py

@@ -1544,7 +1544,7 @@ 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):
@@ -1580,3 +1580,32 @@ 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)