[Triton-MLIR]Add ptx vprintf support (#825)

Not know how to write unit test for this feature.

Co-authored-by: Yan Chunwei <yanchunwei@outlook.com>

python/tests/printf_helper.py

@@ -0,0 +1,56 @@
+import torch
+from torch.testing import assert_close
+
+import triton
+import triton.language as tl
+
+torch_type = {
+    "bool": torch.bool,
+    'int8': torch.int8,
+    'uint8': torch.uint8,
+    'int16': torch.int16,
+    "int32": torch.int32,
+    'int64': torch.long,
+    'float16': torch.float16,
+    'bfloat16': torch.bfloat16,
+    "float32": torch.float32,
+    "float64": torch.float64
+}
+
+
+def get_tensor(shape, data_type, b_positive=False):
+    x = None
+    if data_type.startswith('int'):
+        x = torch.arange(0, shape[0], dtype=torch_type[data_type], device='cuda')
+    else:
+        x = torch.arange(0, shape[0], dtype=torch_type[data_type], device='cuda')
+
+    return x
+
+# @pytest.mark.parametrize('data_type',
+#                          [("int8"),
+#                           ('int16'),
+#                           ('int32'),
+#                           ("int64"),
+#                           ('float16'),
+#                           ("float32"),
+#                           ("float64")])
+
+
+def printf(data_type):
+    @triton.jit
+    def kernel(X, Y, BLOCK: tl.constexpr):
+        x = tl.load(X + tl.arange(0, BLOCK))
+        tl.printf("", x)
+        tl.store(Y + tl.arange(0, BLOCK), x)
+
+    shape = (128, )
+    # limit the range of integers so that the sum does not overflow
+    x = get_tensor(shape, data_type)
+    y = torch.zeros(shape, dtype=x.dtype, device="cuda")
+    kernel[(1,)](x, y, BLOCK=shape[0])
+    assert_close(y, x)
+
+
+printf("float16")
+printf("int8")